diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml deleted file mode 100644 index a0848ae1fe52..000000000000 --- a/.github/workflows/docker.yml +++ /dev/null @@ -1,201 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -name: Build & Push Docker Images - -on: - workflow_dispatch: {} - pull_request: - paths: - - scripts/docker/*.dockerfile - - scripts/setup-*.sh - - .github/workflows/docker.yml - - docker-compose.yml - push: - branches: [main] - paths: - - scripts/docker/*.dockerfile - - scripts/setup-*.sh - - .github/workflows/docker.yml - -concurrency: - group: ${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.sha }} - cancel-in-progress: true - -permissions: - contents: read - -env: - BASE_NAME: ghcr.io/facebookincubator - -jobs: - multi-arch-base: - name: Build ${{ matrix.target }} Image on ${{ matrix.os.platform }} - runs-on: ${{ matrix.os.runner }} - permissions: - packages: write - strategy: - fail-fast: false - matrix: - os: - - {runner: ubuntu-24.04-arm, platform: arm64} - - {runner: ubuntu-24.04, platform: amd64} - target: [ci, pyvelox, ubuntu, fedora] - - steps: - - name: Free Disk Space - run: | - # 15G - sudo rm -rf /usr/local/lib/android || : - # 5.3GB - sudo rm -rf /opt/hostedtoolcache/CodeQL || : - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # v3.11.1 - - - name: Login to GitHub Container Registry - uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 - with: - registry: ghcr.io - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Bake Images - id: bake - uses: docker/bake-action@37816e747588cb137173af99ab33873600c46ea8 # v6.8.0 - env: - DOCKER_UPLOAD_CACHE: ${{ github.repository == 'facebookincubator/velox' && github.event_name != 'pull_request'}} - with: - targets: ${{ matrix.target }}-${{ matrix.os.platform }} - push: ${{ github.repository == 'facebookincubator/velox' && github.event_name != 'pull_request'}} - # loads the image into the local image store, this allows us to export it in the next step - load: ${{ github.event_name == 'pull_request' && matrix.target == 'ci' && matrix.os.platform == 'amd64' }} - - - name: Export Base Image - # The dependent images are just build for amd64 and can pull from the - # registry when not running in a PR - if: ${{ github.event_name == 'pull_request' && matrix.target == 'ci' && matrix.os.platform == 'amd64' }} - run: | - # Update tag to what the dependent images expect - docker tag "$BASE_NAME/velox-dev:centos9-amd64" "$BASE_NAME/velox-dev:centos9" - docker save -o centos9.tar "$BASE_NAME/velox-dev:centos9" - - - name: Upload Base Image - if: ${{ github.event_name == 'pull_request' && matrix.target == 'ci' && matrix.os.platform == 'amd64' }} - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 - with: - path: centos9.tar - name: centos9-amd64 - retention-days: 1 - - - name: Export digests - env: - METADATA: ${{ steps.bake.outputs.metadata }} - DIGEST_DIR: ${{ runner.temp }}/digests - run: | - mkdir -p "$DIGEST_DIR" - cd "$DIGEST_DIR" || exit 1 - # Add the target for any images that shouldn't be build as multi-platform - # into the skip list - echo "$METADATA" | jq -r 'def skip: ["ubuntu", "fedora"]; - . | to_entries[] | .key | split("-")[0] | - if . as $name | skip | index($name) != null then empty else . end' | \ - while read -r image_name; do - touch "$image_name" - done - ls -la - - - name: Upload digest - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 - with: - name: digests-${{ matrix.target }}-${{ matrix.os.platform }} - path: ${{ runner.temp }}/digests/* - retention-days: 1 - - merge: - if: ${{ github.repository == 'facebookincubator/velox' && github.event_name != 'pull_request'}} - needs: multi-arch-base - runs-on: ubuntu-latest - permissions: - packages: write - steps: - - name: Login to GitHub Container Registry - uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 - with: - registry: ghcr.io - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Download digests - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0 - with: - path: ${{ runner.temp }}/digests - pattern: digests-* - merge-multiple: true - - - name: Create manifest list and push - working-directory: ${{ runner.temp }}/digests - run: | - ls -laR - - for file in *; do - image_name="$BASE_NAME/velox-dev:$file" - - docker buildx imagetools create -t "$image_name" "${image_name}-amd64" "${image_name}-arm64" - done - - dependent-images: - # In PRs the merge job will be skipped but we want to run this job anyway - if: ${{ success() || (needs.multi-arch-base.result == 'success' && needs.merge.result == 'skipped') }} - needs: [multi-arch-base, merge] - name: Build and Push ${{ matrix.target }} - runs-on: ubuntu-latest - permissions: - packages: write - strategy: - fail-fast: false - matrix: - target: [java] - steps: - - name: Login to GitHub Container Registry - uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 # v3.4.0 - with: - registry: ghcr.io - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Set up QEMU - uses: docker/setup-qemu-action@29109295f81e9208d7d86ff1c6c12d2833863392 # v3.6.0 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # v3.11.1 - - - name: Download Base Image - if: ${{ github.event_name == 'pull_request' }} - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0 - with: - pattern: centos9-amd64 - path: /tmp - - - name: Load Base Image - if: ${{ github.event_name == 'pull_request' }} - run: | - docker load -i /tmp/centos9-amd64/centos9.tar - docker images - rm /tmp/centos9-amd64/centos9.tar - - - name: Build and Push - uses: docker/bake-action@37816e747588cb137173af99ab33873600c46ea8 # v6.8.0 - with: - targets: ${{ matrix.target }} - push: ${{ github.repository == 'facebookincubator/velox' && github.event_name != 'pull_request'}} diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml deleted file mode 100644 index 80ebde553ad5..000000000000 --- a/.github/workflows/docs.yml +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name: Update Documentation - -on: - push: - paths: - - velox/docs/** - - .github/workflows/docs.yml - - pull_request: - paths: - - velox/docs/** - - .github/workflows/docs.yml - -permissions: - contents: write - -concurrency: - group: ${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.sha }} - cancel-in-progress: true - -jobs: - build_docs: - name: Build and Push - runs-on: 8-core-ubuntu - container: ghcr.io/facebookincubator/velox-dev:centos9 - env: - CCACHE_DIR: /tmp/ccache - steps: - - - name: Restore ccache - if: false - uses: apache/infrastructure-actions/stash/restore@3354c1565d4b0e335b78a76aedd82153a9e144d4 - id: restore-cache - with: - path: ${{ env.CCACHE_DIR }} - key: ccache-wheels-8-core-ubuntu - - - name: Checkout - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1 - with: - fetch-depth: 0 - persist-credentials: true - - - name: Install System Dependencies - run: | - dnf install -y --setopt=install_weak_deps=False pandoc - pip3 install pipx - pipx install --force uv cmake==3.31 - echo "PATH=$PATH:/github/home/.local/bin" >> $GITHUB_ENV - - - name: Install Python Dependencies - run: | - which uv - make python-venv - which uv - uv pip install -r scripts/docs-requirements.txt - - # Install pyvelox to generate it's docs - make python-build - - - name: Save ccache - uses: apache/infrastructure-actions/stash/save@3354c1565d4b0e335b78a76aedd82153a9e144d4 - with: - path: ${{ env.CCACHE_DIR }} - key: ccache-wheels-8-core-ubuntu - - - name: Build Documentation - run: | - source .venv/bin/activate - cd velox/docs - make clean - # pyvelox - mkdir -p bindings/python - pandoc ../../python/README.md --from markdown --to rst -s -o bindings/python/README_generated_pyvelox.rst - # velox - make html - - - name: Setup Git User - run: | - git config --global user.email "velox@users.noreply.github.com" - git config --global user.name "velox" - git config --global --add safe.directory ${GITHUB_WORKSPACE} - - - name: Push Documentation - if: ${{ github.event_name == 'push' && github.repository == 'facebookincubator/velox'}} - run: | - git checkout gh-pages - cp -R velox/docs/_build/html/* docs - git add docs - - if [ -n "$(git status --porcelain --untracked-files=no)" ] - then - git commit -m "Update documentation" - git push - fi - - - name: Upload Documentation - if: github.event_name == 'pull_request' - uses: actions/upload-artifact@5d5d22a31266ced268874388b861e4b58bb5c2f3 # v4.3.1 - with: - path: velox/docs/_build/html - retention-days: 3 diff --git a/.github/workflows/gluten.yml b/.github/workflows/gluten.yml new file mode 100644 index 000000000000..16e476104e3a --- /dev/null +++ b/.github/workflows/gluten.yml @@ -0,0 +1,211 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: Gluten Build + +on: + pull_request: + paths: + - .github/workflows/gluten.yml +env: + MVN_CMD: mvn -ntp + +jobs: + + gluten-cpp-build: + name: gluten cpp build + # prevent errors when forks ff their main branch + if: ${{ github.repository == 'IBM/velox' }} + runs-on: ubuntu-22.04 + env: + CCACHE_DIR: "${{ github.workspace }}/.ccache" + steps: + - uses: actions/checkout@v4 + - name: Get Ccache + uses: actions/cache/restore@v4 + with: + path: '${{ env.CCACHE_DIR }}' + key: ccache-centos7-release-default-${{github.sha}} + restore-keys: | + ccache-centos7-release-default + - name: Setup Gluten + run: | + git clone --depth 1 https://github.com/apache/incubator-gluten gluten && cd gluten + BRANCH=$(echo ${GITHUB_REF#refs/heads/}) + sed -i 's/oap-project/IBM/g' ep/build-velox/src/get_velox.sh + # sed -i 's/VELOX_BRANCH=2025.*/VELOX_BRANCH=${BRANCH}/g' ep/build-velox/src/get_velox.sh + - name: Build Gluten native libraries + run: | + docker pull apache/gluten:vcpkg-centos-7 + docker run -v $GITHUB_WORKSPACE:/work -w /work apache/gluten:vcpkg-centos-7 bash -c " + git config --global --add safe.directory /work + set -e + df -a + cd /work/gluten + export CCACHE_DIR=/work/.ccache + export CCACHE_SLOPPINESS=file_macro,locale,time_macros + mkdir -p /work/.ccache + ccache -sz + source /opt/rh/devtoolset-11/enable + export NUM_THREADS=4 + ./dev/builddeps-veloxbe.sh --enable_vcpkg=ON --build_arrow=OFF --build_tests=OFF --build_benchmarks=OFF \ + --build_examples=OFF --enable_s3=ON --enable_gcs=ON --enable_hdfs=ON --enable_abfs=ON --velox_home=/work + ccache -s + mkdir -p /work/.m2/repository/org/apache/arrow/ + cp -r /root/.m2/repository/org/apache/arrow/* /work/.m2/repository/org/apache/arrow/ + " + - uses: actions/upload-artifact@v4 + with: + name: velox-native-lib-centos-7-${{github.sha}} + path: ./gluten/cpp/build/releases/ + if-no-files-found: error + - uses: actions/upload-artifact@v4 + with: + name: arrow-jars-centos-7-${{github.sha}} + path: .m2/repository/org/apache/arrow/ + if-no-files-found: error + + spark-test-spark32: + needs: gluten-cpp-build + runs-on: ubuntu-22.04 + container: apache/gluten:centos-8-jdk8 + steps: + - name: Setup Gluten + run: | + git clone --depth 1 https://github.com/apache/incubator-gluten gluten && cd gluten + - name: Download All Artifacts + uses: actions/download-artifact@v4 + with: + name: velox-native-lib-centos-7-${{github.sha}} + path: ./gluten/cpp/build/releases + - name: Download Arrow Jars + uses: actions/download-artifact@v4 + with: + name: arrow-jars-centos-7-${{github.sha}} + path: /root/.m2/repository/org/apache/arrow/ + - name: Build package for Spark 3.2 + run: | + cd $GITHUB_WORKSPACE/gluten + export SPARK_SCALA_VERSION=2.12 + yum install -y java-17-openjdk-devel + export JAVA_HOME=/usr/lib/jvm/java-17-openjdk + export PATH=$JAVA_HOME/bin:$PATH + java -version + $MVN_CMD clean package -Pspark-3.2 -Pbackends-velox -Piceberg -Pdelta -Phudi -DskipTests + + spark-test-spark34: + needs: gluten-cpp-build + runs-on: ubuntu-22.04 + container: apache/gluten:centos-8-jdk8 + steps: + - name: Setup Gluten + run: | + git clone --depth 1 https://github.com/apache/incubator-gluten gluten && cd gluten + - name: Download All Artifacts + uses: actions/download-artifact@v4 + with: + name: velox-native-lib-centos-7-${{github.sha}} + path: ./gluten/cpp/build/releases + - name: Download Arrow Jars + uses: actions/download-artifact@v4 + with: + name: arrow-jars-centos-7-${{github.sha}} + path: /root/.m2/repository/org/apache/arrow/ + - name: Prepare spark.test.home for Spark 3.4.4 (other tests) + run: | + dnf module -y install python39 && \ + alternatives --set python3 /usr/bin/python3.9 && \ + pip3 install setuptools==77.0.3 && \ + pip3 install pyspark==3.4.4 cython && \ + pip3 install pandas==2.2.3 pyarrow==20.0.0 + - name: Build and Run unit test for Spark 3.4.4 (other tests) + run: | + cd $GITHUB_WORKSPACE/gluten + export SPARK_SCALA_VERSION=2.12 + yum install -y java-17-openjdk-devel + export JAVA_HOME=/usr/lib/jvm/java-17-openjdk + export PATH=$JAVA_HOME/bin:$PATH + java -version + export SPARK_HOME=/opt/shims/spark34/spark_home/ + ls -l $SPARK_HOME + $MVN_CMD clean test -Pspark-3.4 -Pjava-17 -Pbackends-velox -Piceberg -Pdelta -Phudi -Pspark-ut \ + -DtagsToExclude=org.apache.spark.tags.ExtendedSQLTest,org.apache.gluten.tags.UDFTest,org.apache.gluten.tags.EnhancedFeaturesTest,org.apache.gluten.tags.SkipTest \ + -DargLine="-Dspark.test.home=$SPARK_HOME ${EXTRA_FLAGS}" + - name: Upload test report + if: always() + uses: actions/upload-artifact@v4 + with: + name: ${{ github.job }}-report + path: '**/surefire-reports/TEST-*.xml' + - name: Upload unit tests log files + if: ${{ !success() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ github.job }}-test-log + path: | + **/target/*.log + **/gluten-ut/**/hs_err_*.log + **/gluten-ut/**/core.* + - name: Upload golden files + if: failure() + uses: actions/upload-artifact@v4 + with: + name: ${{ github.job }}-golden-files + path: /tmp/tpch-approved-plan/** + + spark-test-spark34-slow: + needs: gluten-cpp-build + runs-on: ubuntu-22.04 + container: apache/gluten:centos-8-jdk8 + steps: + - name: Setup Gluten + run: | + git clone --depth 1 https://github.com/apache/incubator-gluten gluten && cd gluten + - name: Download All Artifacts + uses: actions/download-artifact@v4 + with: + name: velox-native-lib-centos-7-${{github.sha}} + path: ./gluten/cpp/build/releases + - name: Download Arrow Jars + uses: actions/download-artifact@v4 + with: + name: arrow-jars-centos-7-${{github.sha}} + path: /root/.m2/repository/org/apache/arrow/ + - name: Build and Run unit test for Spark 3.4.4 (slow tests) + run: | + cd $GITHUB_WORKSPACE/gluten + yum install -y java-17-openjdk-devel + export JAVA_HOME=/usr/lib/jvm/java-17-openjdk + export PATH=$JAVA_HOME/bin:$PATH + java -version + export SPARK_HOME=/opt/shims/spark34/spark_home/ + ls -l $SPARK_HOME + $MVN_CMD clean test -Pspark-3.4 -Pjava-17 -Pbackends-velox -Piceberg -Pdelta -Pspark-ut -Phudi \ + -DtagsToInclude=org.apache.spark.tags.ExtendedSQLTest \ + -DargLine="-Dspark.test.home=$SPARK_HOME ${EXTRA_FLAGS}" + - name: Upload test report + if: always() + uses: actions/upload-artifact@v4 + with: + name: ${{ github.job }}-report + path: '**/surefire-reports/TEST-*.xml' + - name: Upload unit tests log files + if: ${{ !success() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ github.job }}-test-log + path: | + **/target/*.log + **/gluten-ut/**/hs_err_*.log + **/gluten-ut/**/core.* diff --git a/.github/workflows/gluten_daily.yml b/.github/workflows/gluten_daily.yml new file mode 100644 index 000000000000..66c703ab5b6c --- /dev/null +++ b/.github/workflows/gluten_daily.yml @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: Gluten Daily Build + +on: + push: + branches: + - 'main' + +concurrency: + group: ${{ github.repository }}-${{ github.head_ref || github.sha }}-${{ github.workflow }} + cancel-in-progress: true + +jobs: + + gluten-cpp-build: + name: gluten cpp build + # prevent errors when forks ff their main branch + if: ${{ github.repository == 'IBM/velox' }} + runs-on: ubuntu-22.04 + env: + CCACHE_DIR: "${{ github.workspace }}/.ccache" + steps: + - uses: actions/checkout@v4 + - name: Get Ccache + uses: actions/cache/restore@v4 + with: + path: '${{ env.CCACHE_DIR }}' + key: ccache-centos7-release-default-${{github.sha}} + restore-keys: | + ccache-centos7-release-default + - name: Setup Gluten + run: | + git clone --depth 1 https://github.com/apache/incubator-gluten gluten && cd gluten + BRANCH=$(echo ${GITHUB_REF#refs/heads/}) + sed -i 's/oap-project/IBM/g' ep/build-velox/src/get_velox.sh + #sed -i 's/VELOX_BRANCH=2025.*/VELOX_BRANCH=main/g' ep/build-velox/src/get_velox.sh + - name: Build Gluten native libraries + run: | + docker pull apache/gluten:vcpkg-centos-7 + docker run -v $GITHUB_WORKSPACE:/work -w /work apache/gluten:vcpkg-centos-7 bash -c " + git config --global --add safe.directory /work + set -e + df -a + cd /work/gluten + export CCACHE_DIR=/work/.ccache + mkdir -p /work/.ccache + source /opt/rh/devtoolset-11/enable + export NUM_THREADS=4 + ccache -sz + ./dev/builddeps-veloxbe.sh --enable_vcpkg=ON --build_arrow=OFF --build_tests=OFF --build_benchmarks=OFF \ + --build_examples=OFF --enable_s3=ON --enable_gcs=ON --enable_hdfs=ON --enable_abfs=ON --velox_home=/work + ccache -s + mkdir -p /work/.m2/repository/org/apache/arrow/ + cp -r /root/.m2/repository/org/apache/arrow/* /work/.m2/repository/org/apache/arrow/ + " + - name: Save ccache + uses: actions/cache/save@v4 + id: ccache + with: + path: '${{ env.CCACHE_DIR }}' + key: ccache-centos7-release-default-${{github.sha}} diff --git a/.github/workflows/linux-build.yml b/.github/workflows/linux-build.yml index f91a404668d5..79fcc33be1a5 100644 --- a/.github/workflows/linux-build.yml +++ b/.github/workflows/linux-build.yml @@ -15,21 +15,6 @@ name: Linux Build using GCC on: - push: - branches: - - main - paths: - - velox/** - - '!velox/docs/**' - - CMakeLists.txt - - CMake/** - - scripts/setup-ubuntu.sh - - scripts/setup-common.sh - - scripts/setup-versions.sh - - scripts/setup-helper-functions.sh - - .github/workflows/linux-build.yml - - .github/workflows/linux-build-base.yml - pull_request: paths: - velox/** diff --git a/.github/workflows/macos.yml b/.github/workflows/macos.yml deleted file mode 100644 index f2db45237cbd..000000000000 --- a/.github/workflows/macos.yml +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -name: macOS Build - -on: - push: - paths: - - velox/** - - '!velox/docs/**' - - CMakeLists.txt - - CMake/** - - scripts/setup-macos.sh - - .github/workflows/macos.yml - - pull_request: - paths: - - velox/** - - '!velox/docs/**' - - CMakeLists.txt - - CMake/** - - scripts/setup-macos.sh - - .github/workflows/macos.yml - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.sha }} - cancel-in-progress: true - -jobs: - macos-build: - if: ${{ github.repository == 'facebookincubator/velox' }} - name: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - # macos-13 = x86_64 Mac - # macos-15 = arm64 Mac and cmake 4.0 - os: [macos-13, macos-15] - runs-on: ${{ matrix.os }} - env: - CCACHE_DIR: ${{ github.workspace }}/ccache - # The arm runners have only 7GB RAM - BUILD_TYPE: ${{ matrix.os == 'macos-15' && 'Release' || 'Debug' }} - INSTALL_PREFIX: /tmp/deps-install - steps: - - name: Checkout - uses: actions/checkout@v4 - with: - persist-credentials: false - - - name: Install Dependencies - env: - HOMEBREW_NO_INSTALLED_DEPENDENTS_CHECK: 'TRUE' - CMAKE_POLICY_VERSION_MINIMUM: '3.5' - run: | - source scripts/setup-macos.sh - install_build_prerequisites - install_velox_deps_from_brew - # Needed for faiss to find BLAS - install_faiss_deps - install_double_conversion - - echo "NJOBS=`sysctl -n hw.ncpu`" >> $GITHUB_ENV - majorVersion=$(sw_vers -productVersion | awk -F '.' '{print $1}') - if (( majorVersion == 13 )); then - brew uninstall cmake - pipx install --force cmake==3.31 - fi - echo "$INSTALL_PREFIX/bin" >> $GITHUB_PATH - - - name: Cache ccache - uses: apache/infrastructure-actions/stash/restore@3354c1565d4b0e335b78a76aedd82153a9e144d4 - with: - path: ${{ env.CCACHE_DIR }} - key: ccache-macos-1-${{ matrix.os }} - - - name: Configure Build - env: - fmt_SOURCE: BUNDLED #brew fmt11 is not supported - faiss_SOURCE: BUNDLED #brew faiss is not supported - CMAKE_POLICY_VERSION_MINIMUM: '3.5' - run: | - ccache -sz -M 5Gi - cmake \ - -B _build/$BUILD_TYPE \ - -GNinja \ - -DTREAT_WARNINGS_AS_ERRORS=1 \ - -DENABLE_ALL_WARNINGS=1 \ - -DVELOX_ENABLE_PARQUET=ON \ - -DVELOX_MONO_LIBRARY=ON \ - -DVELOX_BUILD_SHARED=ON \ - -DCMAKE_BUILD_TYPE=$BUILD_TYPE \ - -DVELOX_ENABLE_FAISS=ON - - - name: Build - run: | - cmake --build _build/$BUILD_TYPE -j $NJOBS - ccache -s - - - uses: apache/infrastructure-actions/stash/save@3354c1565d4b0e335b78a76aedd82153a9e144d4 - with: - path: ${{ env.CCACHE_DIR }} - key: ccache-macos-1-${{ matrix.os }} - - - name: Run Tests - if: false - run: ctest -j $NJOBS --test-dir _build/debug --output-on-failure diff --git a/.github/workflows/preliminary_checks.yml b/.github/workflows/preliminary_checks.yml deleted file mode 100644 index e18f9864da86..000000000000 --- a/.github/workflows/preliminary_checks.yml +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -name: Run Checks - -on: - pull_request: - types: - - opened - - reopened - - edited - - synchronize - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.sha }} - cancel-in-progress: true - -jobs: - pre-commit: - runs-on: ubuntu-latest - steps: - - run: python -m pip install pre-commit - - - uses: actions/checkout@v4 - with: - fetch-depth: 2 - persist-credentials: false - - - uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4.2.3 - with: - path: ~/.cache/pre-commit - key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} - restore-keys: | - pre-commit- - - - name: Run pre-commit - env: - GH_TOKEN: ${{ github.token }} - run: | - files=$(git diff --name-only HEAD^1 HEAD) - echo "::group::Changed files" - echo $files | tr ' ' '\n' - echo "::endgroup::" - pre-commit run --show-diff-on-failure --color=always --files $files - title-check: - name: PR Title Format - runs-on: ubuntu-latest - steps: - - shell: python - env: - title: '${{ github.event.pull_request.title }}' - run: | - import re - import os - title = os.environ["title"] - title_re = r"^(feat|fix|build|test|docs|refactor|misc)(\(.+\))?!?: ([A-Z].+)[^.]$" - match = re.search(title_re, title) - - if match is None: - print("::error::Please follow conventional commit guidelines in commit titles as described in CONTRIBUTING.md: https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md#commit-messages") - exit(1) - else: - exit(0) diff --git a/website/static/.nojekyll b/dummy.txt similarity index 100% rename from website/static/.nojekyll rename to dummy.txt diff --git a/velox/connectors/hive/HiveConfig.cpp b/velox/connectors/hive/HiveConfig.cpp index eed0c0bdda3f..ae71e45e7019 100644 --- a/velox/connectors/hive/HiveConfig.cpp +++ b/velox/connectors/hive/HiveConfig.cpp @@ -230,6 +230,13 @@ bool HiveConfig::readStatsBasedFilterReorderDisabled( config_->get(kReadStatsBasedFilterReorderDisabled, false)); } +bool HiveConfig::isRequestedTypeCheckEnabled( + const config::ConfigBase* session) const { + return session->get( + kEnableRequestedTypeCheckSession, + config_->get(kEnableRequestedTypeCheck, true)); +} + std::string HiveConfig::hiveLocalDataPath() const { return config_->get(kLocalDataPath, ""); } diff --git a/velox/connectors/hive/HiveConfig.h b/velox/connectors/hive/HiveConfig.h index 9d23e60c2a71..bcc7019e65a1 100644 --- a/velox/connectors/hive/HiveConfig.h +++ b/velox/connectors/hive/HiveConfig.h @@ -194,6 +194,11 @@ class HiveConfig { static constexpr const char* kPreserveFlatMapsInMemorySession = "hive.preserve_flat_maps_in_memory"; + static constexpr const char* kEnableRequestedTypeCheck = + "enable-requested-type-check"; + static constexpr const char* kEnableRequestedTypeCheckSession = + "enable_requested_type_check"; + InsertExistingPartitionsBehavior insertExistingPartitionsBehavior( const config::ConfigBase* session) const; @@ -265,6 +270,10 @@ class HiveConfig { bool readStatsBasedFilterReorderDisabled( const config::ConfigBase* session) const; + /// Whether to enable requested type check in the ReaderBase::convertType. + /// Returns true by default. + bool isRequestedTypeCheckEnabled(const config::ConfigBase* session) const; + /// Returns the file system path containing local data. If non-empty, /// initializes LocalHiveConnectorMetadata to provide metadata for the tables /// in the directory. diff --git a/velox/connectors/hive/HiveConnector.cpp b/velox/connectors/hive/HiveConnector.cpp index 5159f1fc495b..3f28309a3d7e 100644 --- a/velox/connectors/hive/HiveConnector.cpp +++ b/velox/connectors/hive/HiveConnector.cpp @@ -20,6 +20,7 @@ #include "velox/connectors/hive/HiveDataSink.h" #include "velox/connectors/hive/HiveDataSource.h" #include "velox/connectors/hive/HivePartitionFunction.h" +#include "velox/connectors/hive/iceberg/IcebergDataSink.h" #include #include @@ -87,17 +88,29 @@ std::unique_ptr HiveConnector::createDataSink( ConnectorInsertTableHandlePtr connectorInsertTableHandle, ConnectorQueryCtx* connectorQueryCtx, CommitStrategy commitStrategy) { - auto hiveInsertHandle = - std::dynamic_pointer_cast( - connectorInsertTableHandle); - VELOX_CHECK_NOT_NULL( - hiveInsertHandle, "Hive connector expecting hive write handle!"); - return std::make_unique( - inputType, - hiveInsertHandle, - connectorQueryCtx, - commitStrategy, - hiveConfig_); + if (auto icebergInsertHandle = + std::dynamic_pointer_cast( + connectorInsertTableHandle)) { + return std::make_unique( + inputType, + icebergInsertHandle, + connectorQueryCtx, + commitStrategy, + hiveConfig_); + } else { + auto hiveInsertHandle = + std::dynamic_pointer_cast( + connectorInsertTableHandle); + + VELOX_CHECK_NOT_NULL( + hiveInsertHandle, "Hive connector expecting hive write handle!"); + return std::make_unique( + inputType, + hiveInsertHandle, + connectorQueryCtx, + commitStrategy, + hiveConfig_); + } } std::unique_ptr HivePartitionFunctionSpec::create( diff --git a/velox/connectors/hive/HiveConnectorUtil.cpp b/velox/connectors/hive/HiveConnectorUtil.cpp index 99961ec3df56..757ce3a776d4 100644 --- a/velox/connectors/hive/HiveConnectorUtil.cpp +++ b/velox/connectors/hive/HiveConnectorUtil.cpp @@ -23,6 +23,10 @@ #include "velox/expression/Expr.h" #include "velox/expression/ExprToSubfieldFilter.h" +#include +#include +#include + namespace facebook::velox::connector::hive { namespace { @@ -604,6 +608,8 @@ void configureReaderOptions( } readerOptions.setFileFormat(hiveSplit->fileFormat); + readerOptions.setEnableRequestedTypeCheck( + hiveConfig->isRequestedTypeCheckEnabled(sessionProperties)); } } @@ -925,4 +931,9 @@ core::TypedExprPtr extractFiltersFromRemainingFilter( } return expr; } + +std::string makeUuid() { + return boost::lexical_cast(boost::uuids::random_generator()()); +} + } // namespace facebook::velox::connector::hive diff --git a/velox/connectors/hive/HiveConnectorUtil.h b/velox/connectors/hive/HiveConnectorUtil.h index d649b12d0930..75906343bd07 100644 --- a/velox/connectors/hive/HiveConnectorUtil.h +++ b/velox/connectors/hive/HiveConnectorUtil.h @@ -114,4 +114,6 @@ core::TypedExprPtr extractFiltersFromRemainingFilter( common::SubfieldFilters& filters, double& sampleRate); +std::string makeUuid(); + } // namespace facebook::velox::connector::hive diff --git a/velox/connectors/hive/HiveDataSink.cpp b/velox/connectors/hive/HiveDataSink.cpp index 70f5ce4e4623..782c5596c4e0 100644 --- a/velox/connectors/hive/HiveDataSink.cpp +++ b/velox/connectors/hive/HiveDataSink.cpp @@ -30,10 +30,6 @@ #include "velox/exec/OperatorUtils.h" #include "velox/exec/SortBuffer.h" -#include -#include -#include - using facebook::velox::common::testutil::TestValue; namespace facebook::velox::connector::hive { @@ -95,14 +91,12 @@ std::vector getPartitionChannels( // Returns the column indices of non-partition data columns. std::vector getNonPartitionChannels( - const std::vector& partitionChannels, - const column_index_t childrenSize) { + const std::shared_ptr& insertTableHandle) { std::vector dataChannels; - dataChannels.reserve(childrenSize - partitionChannels.size()); - for (column_index_t i = 0; i < childrenSize; i++) { - if (std::find(partitionChannels.cbegin(), partitionChannels.cend(), i) == - partitionChannels.cend()) { + for (column_index_t i = 0; i < insertTableHandle->inputColumns().size(); + i++) { + if (!insertTableHandle->inputColumns()[i]->isPartitionKey()) { dataChannels.push_back(i); } } @@ -119,10 +113,6 @@ std::string makePartitionDirectory( return tableDirectory; } -std::string makeUuid() { - return boost::lexical_cast(boost::uuids::random_generator()()); -} - std::unordered_map tableTypeNames() { return { {LocationHandle::TableType::kNew, "kNew"}, @@ -383,6 +373,18 @@ HiveDataSink::HiveDataSink( ? createBucketFunction( *insertTableHandle->bucketProperty(), inputType) + : nullptr, + getPartitionChannels(insertTableHandle), + getNonPartitionChannels(insertTableHandle), + !getPartitionChannels(insertTableHandle).empty() + ? std::make_unique( + inputType, + getPartitionChannels(insertTableHandle), + hiveConfig->maxPartitionsPerWriters( + connectorQueryCtx->sessionProperties()), + connectorQueryCtx->memoryPool(), + hiveConfig->isPartitionPathAsLowerCase( + connectorQueryCtx->sessionProperties())) : nullptr) {} HiveDataSink::HiveDataSink( @@ -392,7 +394,10 @@ HiveDataSink::HiveDataSink( CommitStrategy commitStrategy, const std::shared_ptr& hiveConfig, uint32_t bucketCount, - std::unique_ptr bucketFunction) + std::unique_ptr bucketFunction, + const std::vector& partitionChannels, + const std::vector& dataChannels, + std::unique_ptr partitionIdGenerator) : inputType_(std::move(inputType)), insertTableHandle_(std::move(insertTableHandle)), connectorQueryCtx_(connectorQueryCtx), @@ -401,19 +406,9 @@ HiveDataSink::HiveDataSink( updateMode_(getUpdateMode()), maxOpenWriters_(hiveConfig_->maxPartitionsPerWriters( connectorQueryCtx->sessionProperties())), - partitionChannels_(getPartitionChannels(insertTableHandle_)), - partitionIdGenerator_( - !partitionChannels_.empty() - ? std::make_unique( - inputType_, - partitionChannels_, - maxOpenWriters_, - connectorQueryCtx_->memoryPool(), - hiveConfig_->isPartitionPathAsLowerCase( - connectorQueryCtx->sessionProperties())) - : nullptr), - dataChannels_( - getNonPartitionChannels(partitionChannels_, inputType_->size())), + partitionChannels_(partitionChannels), + partitionIdGenerator_(std::move(partitionIdGenerator)), + dataChannels_(dataChannels), bucketCount_(static_cast(bucketCount)), bucketFunction_(std::move(bucketFunction)), writerFactory_( @@ -489,6 +484,8 @@ void HiveDataSink::appendData(RowVectorPtr input) { // Compute partition and bucket numbers. computePartitionAndBucketIds(input); + splitInputRowsAndEnsureWriters(input); + // All inputs belong to a single non-bucketed partition. The partition id // must be zero. if (!isBucketed() && partitionIdGenerator_->numPartitions() == 1) { @@ -497,8 +494,6 @@ void HiveDataSink::appendData(RowVectorPtr input) { return; } - splitInputRowsAndEnsureWriters(); - for (auto index = 0; index < writers_.size(); ++index) { const vector_size_t partitionSize = partitionSizes_[index]; if (partitionSize == 0) { @@ -683,30 +678,33 @@ bool HiveDataSink::finish() { std::vector HiveDataSink::close() { setState(State::kClosed); closeInternal(); + return commitMessage(); +} +std::vector HiveDataSink::commitMessage() const { std::vector partitionUpdates; partitionUpdates.reserve(writerInfo_.size()); for (int i = 0; i < writerInfo_.size(); ++i) { const auto& info = writerInfo_.at(i); VELOX_CHECK_NOT_NULL(info); // clang-format off - auto partitionUpdateJson = folly::toJson( - folly::dynamic::object - ("name", info->writerParameters.partitionName().value_or("")) - ("updateMode", - HiveWriterParameters::updateModeToString( - info->writerParameters.updateMode())) - ("writePath", info->writerParameters.writeDirectory()) - ("targetPath", info->writerParameters.targetDirectory()) - ("fileWriteInfos", folly::dynamic::array( - folly::dynamic::object - ("writeFileName", info->writerParameters.writeFileName()) - ("targetFileName", info->writerParameters.targetFileName()) - ("fileSize", ioStats_.at(i)->rawBytesWritten()))) - ("rowCount", info->numWrittenRows) - ("inMemoryDataSizeInBytes", info->inputSizeInBytes) - ("onDiskDataSizeInBytes", ioStats_.at(i)->rawBytesWritten()) - ("containsNumberedFileNames", true)); + auto partitionUpdateJson = folly::toJson( + folly::dynamic::object + ("name", info->writerParameters.partitionName().value_or("")) + ("updateMode", + HiveWriterParameters::updateModeToString( + info->writerParameters.updateMode())) + ("writePath", info->writerParameters.writeDirectory()) + ("targetPath", info->writerParameters.targetDirectory()) + ("fileWriteInfos", folly::dynamic::array( + folly::dynamic::object + ("writeFileName", info->writerParameters.writeFileName()) + ("targetFileName", info->writerParameters.targetFileName()) + ("fileSize", ioStats_.at(i)->rawBytesWritten()))) + ("rowCount", info->numWrittenRows) + ("inMemoryDataSizeInBytes", info->inputSizeInBytes) + ("onDiskDataSizeInBytes", ioStats_.at(i)->rawBytesWritten()) + ("containsNumberedFileNames", true)); // clang-format on partitionUpdates.push_back(partitionUpdateJson); } @@ -753,11 +751,7 @@ uint32_t HiveDataSink::appendWriter(const HiveWriterId& id) { VELOX_CHECK_EQ(writers_.size(), writerInfo_.size()); VELOX_CHECK_EQ(writerIndexMap_.size(), writerInfo_.size()); - std::optional partitionName; - if (isPartitioned()) { - partitionName = - partitionIdGenerator_->partitionName(id.partitionId.value()); - } + std::optional partitionName = getPartitionName(id); // Without explicitly setting flush policy, the default memory based flush // policy is used. @@ -779,6 +773,32 @@ uint32_t HiveDataSink::appendWriter(const HiveWriterId& id) { setMemoryReclaimers(writerInfo_.back().get(), ioStats_.back().get()); + auto options = createWriterOptions(); + // Prevents the memory allocation during the writer creation. + WRITER_NON_RECLAIMABLE_SECTION_GUARD(writerInfo_.size() - 1); + auto writer = writerFactory_->createWriter( + dwio::common::FileSink::create( + writePath, + { + .bufferWrite = false, + .connectorProperties = hiveConfig_->config(), + .fileCreateConfig = hiveConfig_->writeFileCreateConfig(), + .pool = writerInfo_.back()->sinkPool.get(), + .metricLogger = dwio::common::MetricsLog::voidLog(), + .stats = ioStats_.back().get(), + }), + options); + writer = maybeCreateBucketSortWriter(std::move(writer)); + writers_.emplace_back(std::move(writer)); + + extendBuffersForPartitionedTables(); + + writerIndexMap_.emplace(id, writers_.size() - 1); + return writerIndexMap_[id]; +} + +std::shared_ptr HiveDataSink::createWriterOptions() + const { // Take the writer options provided by the user as a starting point, or // allocate a new one. auto options = insertTableHandle_->writerOptions(); @@ -828,31 +848,17 @@ uint32_t HiveDataSink::appendWriter(const HiveWriterId& id) { options->adjustTimestampToTimezone = connectorQueryCtx_->adjustTimestampToTimezone(); options->processConfigs(*hiveConfig_->config(), *connectorSessionProperties); + return options; +} - // Prevents the memory allocation during the writer creation. - WRITER_NON_RECLAIMABLE_SECTION_GUARD(writerInfo_.size() - 1); - auto writer = writerFactory_->createWriter( - dwio::common::FileSink::create( - writePath, - { - .bufferWrite = false, - .connectorProperties = hiveConfig_->config(), - .fileCreateConfig = hiveConfig_->writeFileCreateConfig(), - .pool = writerInfo_.back()->sinkPool.get(), - .metricLogger = dwio::common::MetricsLog::voidLog(), - .stats = ioStats_.back().get(), - .fileSystemStats = fileSystemStats_.get(), - }), - options); - writer = maybeCreateBucketSortWriter(std::move(writer)); - writers_.emplace_back(std::move(writer)); - // Extends the buffer used for partition rows calculations. - partitionSizes_.emplace_back(0); - partitionRows_.emplace_back(nullptr); - rawPartitionRows_.emplace_back(nullptr); - - writerIndexMap_.emplace(id, writers_.size() - 1); - return writerIndexMap_[id]; +std::optional HiveDataSink::getPartitionName( + const HiveWriterId& id) const { + std::optional partitionName; + if (isPartitioned()) { + partitionName = + partitionIdGenerator_->partitionName(id.partitionId.value()); + } + return partitionName; } std::unique_ptr @@ -882,6 +888,13 @@ HiveDataSink::maybeCreateBucketSortWriter( sortWriterFinishTimeSliceLimitMs_); } +void HiveDataSink::extendBuffersForPartitionedTables() { + // Extends the buffer used for partition rows calculations. + partitionSizes_.emplace_back(0); + partitionRows_.emplace_back(nullptr); + rawPartitionRows_.emplace_back(nullptr); +} + HiveWriterId HiveDataSink::getWriterId(size_t row) const { std::optional partitionId; if (isPartitioned()) { @@ -896,7 +909,25 @@ HiveWriterId HiveDataSink::getWriterId(size_t row) const { return HiveWriterId{partitionId, bucketId}; } -void HiveDataSink::splitInputRowsAndEnsureWriters() { +void HiveDataSink::updatePartitionRows( + uint32_t index, + vector_size_t numRows, + vector_size_t row) { + VELOX_DCHECK_LT(index, partitionSizes_.size()); + VELOX_DCHECK_EQ(partitionSizes_.size(), partitionRows_.size()); + VELOX_DCHECK_EQ(partitionRows_.size(), rawPartitionRows_.size()); + if (FOLLY_UNLIKELY(partitionRows_[index] == nullptr) || + (partitionRows_[index]->capacity() < numRows * sizeof(vector_size_t))) { + partitionRows_[index] = + allocateIndices(numRows, connectorQueryCtx_->memoryPool()); + rawPartitionRows_[index] = + partitionRows_[index]->asMutable(); + } + rawPartitionRows_[index][partitionSizes_[index]] = row; + ++partitionSizes_[index]; +} + +void HiveDataSink::splitInputRowsAndEnsureWriters(RowVectorPtr /* input */) { VELOX_CHECK(isPartitioned() || isBucketed()); if (isBucketed() && isPartitioned()) { VELOX_CHECK_EQ(bucketIds_.size(), partitionIds_.size()); @@ -910,18 +941,7 @@ void HiveDataSink::splitInputRowsAndEnsureWriters() { const auto id = getWriterId(row); const uint32_t index = ensureWriter(id); - VELOX_DCHECK_LT(index, partitionSizes_.size()); - VELOX_DCHECK_EQ(partitionSizes_.size(), partitionRows_.size()); - VELOX_DCHECK_EQ(partitionRows_.size(), rawPartitionRows_.size()); - if (FOLLY_UNLIKELY(partitionRows_[index] == nullptr) || - (partitionRows_[index]->capacity() < numRows * sizeof(vector_size_t))) { - partitionRows_[index] = - allocateIndices(numRows, connectorQueryCtx_->memoryPool()); - rawPartitionRows_[index] = - partitionRows_[index]->asMutable(); - } - rawPartitionRows_[index][partitionSizes_[index]] = row; - ++partitionSizes_[index]; + updatePartitionRows(index, numRows, row); } for (uint32_t i = 0; i < partitionSizes_.size(); ++i) { @@ -932,6 +952,15 @@ void HiveDataSink::splitInputRowsAndEnsureWriters() { } } +std::string HiveDataSink::makePartitionDirectory( + const std::string& tableDirectory, + const std::optional& partitionSubdirectory) const { + if (partitionSubdirectory.has_value()) { + return fs::path(tableDirectory) / partitionSubdirectory.value(); + } + return tableDirectory; +} + HiveWriterParameters HiveDataSink::getWriterParameters( const std::optional& partition, std::optional bucketId) const { diff --git a/velox/connectors/hive/HiveDataSink.h b/velox/connectors/hive/HiveDataSink.h index 9f702c7c0794..9dcd10457ad9 100644 --- a/velox/connectors/hive/HiveDataSink.h +++ b/velox/connectors/hive/HiveDataSink.h @@ -528,7 +528,10 @@ class HiveDataSink : public DataSink { CommitStrategy commitStrategy, const std::shared_ptr& hiveConfig, uint32_t bucketCount, - std::unique_ptr bucketFunction); + std::unique_ptr bucketFunction, + const std::vector& partitionChannels, + const std::vector& dataChannels, + std::unique_ptr partitionIdGenerator); void appendData(RowVectorPtr input) override; @@ -544,11 +547,14 @@ class HiveDataSink : public DataSink { bool canReclaim() const; - private: + protected: // Validates the state transition from 'oldState' to 'newState'. void checkStateTransition(State oldState, State newState); + void setState(State newState); + virtual std::vector commitMessage() const; + class WriterReclaimer : public exec::MemoryReclaimer { public: static std::unique_ptr create( @@ -613,7 +619,7 @@ class HiveDataSink : public DataSink { // Compute the partition id and bucket id for each row in 'input'. void computePartitionAndBucketIds(const RowVectorPtr& input); - // Get the HiveWriter corresponding to the row + // Get the hive writer id corresponding to the row // from partitionIds and bucketIds. FOLLY_ALWAYS_INLINE HiveWriterId getWriterId(size_t row) const; @@ -621,9 +627,9 @@ class HiveDataSink : public DataSink { // to each corresponding (bucketed) partition based on the partition and // bucket ids calculated by 'computePartitionAndBucketIds'. The function also // ensures that there is a writer created for each (bucketed) partition. - void splitInputRowsAndEnsureWriters(); + virtual void splitInputRowsAndEnsureWriters(RowVectorPtr input); - // Makes sure to create one writer for the given writer id. The function + // Makes sure the writer is created for the given writer id. The function // returns the corresponding index in 'writers_'. uint32_t ensureWriter(const HiveWriterId& id); @@ -631,10 +637,25 @@ class HiveDataSink : public DataSink { // the newly created writer in 'writers_'. uint32_t appendWriter(const HiveWriterId& id); - std::unique_ptr + virtual std::shared_ptr createWriterOptions() + const; + + virtual std::optional getPartitionName( + const HiveWriterId& id) const; + + virtual std::unique_ptr maybeCreateBucketSortWriter( std::unique_ptr writer); + std::string makePartitionDirectory( + const std::string& tableDirectory, + const std::optional& partitionSubdirectory) const; + + void + updatePartitionRows(uint32_t index, vector_size_t numRows, vector_size_t row); + + void extendBuffersForPartitionedTables(); + HiveWriterParameters getWriterParameters( const std::optional& partition, std::optional bucketId) const; @@ -658,7 +679,7 @@ class HiveDataSink : public DataSink { // Invoked to write 'input' to the specified file writer. void write(size_t index, RowVectorPtr input); - void closeInternal(); + virtual void closeInternal(); const RowTypePtr inputType_; const std::shared_ptr insertTableHandle_; diff --git a/velox/connectors/hive/HiveDataSource.cpp b/velox/connectors/hive/HiveDataSource.cpp index 7e5626396496..3cf742c64ac4 100644 --- a/velox/connectors/hive/HiveDataSource.cpp +++ b/velox/connectors/hive/HiveDataSource.cpp @@ -218,7 +218,9 @@ std::unique_ptr HiveDataSource::createSplitReader() { fsStats_, fileHandleFactory_, executor_, - scanSpec_); + scanSpec_, + expressionEvaluator_, + totalRemainingFilterTime_); } std::vector HiveDataSource::setupBucketConversion() { @@ -318,6 +320,7 @@ void HiveDataSource::addSplit(std::shared_ptr split) { } splitReader_ = createSplitReader(); + if (!bucketChannels.empty()) { splitReader_->setBucketConversion(std::move(bucketChannels)); } diff --git a/velox/connectors/hive/HiveDataSource.h b/velox/connectors/hive/HiveDataSource.h index 041783f2964c..e10228727871 100644 --- a/velox/connectors/hive/HiveDataSource.h +++ b/velox/connectors/hive/HiveDataSource.h @@ -159,7 +159,7 @@ class HiveDataSource : public DataSource { subfields_; common::SubfieldFilters filters_; std::shared_ptr metadataFilter_; - std::unique_ptr remainingFilterExprSet_; + std::shared_ptr remainingFilterExprSet_; RowVectorPtr emptyOutput_; dwio::common::RuntimeStatistics runtimeStats_; std::atomic totalRemainingFilterTime_{0}; diff --git a/velox/connectors/hive/PartitionIdGenerator.cpp b/velox/connectors/hive/PartitionIdGenerator.cpp index bb20301b678e..d2cd3788037f 100644 --- a/velox/connectors/hive/PartitionIdGenerator.cpp +++ b/velox/connectors/hive/PartitionIdGenerator.cpp @@ -29,10 +29,10 @@ PartitionIdGenerator::PartitionIdGenerator( uint32_t maxPartitions, memory::MemoryPool* pool, bool partitionPathAsLowerCase) - : pool_(pool), - partitionChannels_(std::move(partitionChannels)), + : partitionChannels_(std::move(partitionChannels)), maxPartitions_(maxPartitions), - partitionPathAsLowerCase_(partitionPathAsLowerCase) { + partitionPathAsLowerCase_(partitionPathAsLowerCase), + pool_(pool) { VELOX_USER_CHECK( !partitionChannels_.empty(), "There must be at least one partition key."); for (auto channel : partitionChannels_) { @@ -61,6 +61,19 @@ PartitionIdGenerator::PartitionIdGenerator( } } +PartitionIdGenerator::PartitionIdGenerator( + std::vector partitionChannels, + uint32_t maxPartitions, + memory::MemoryPool* pool, + bool partitionPathAsLowerCase) + : partitionChannels_(std::move(partitionChannels)), + maxPartitions_(maxPartitions), + partitionPathAsLowerCase_(partitionPathAsLowerCase), + pool_(pool) { + VELOX_USER_CHECK( + !partitionChannels_.empty(), "There must be at least one partition key."); +} + void PartitionIdGenerator::run( const RowVectorPtr& input, raw_vector& result) { @@ -170,7 +183,7 @@ void PartitionIdGenerator::updateValueToPartitionIdMapping() { } void PartitionIdGenerator::savePartitionValues( - uint64_t partitionId, + uint32_t partitionId, const RowVectorPtr& input, vector_size_t row) { for (auto i = 0; i < partitionChannels_.size(); ++i) { diff --git a/velox/connectors/hive/PartitionIdGenerator.h b/velox/connectors/hive/PartitionIdGenerator.h index c4e0320b46c1..43a77740b90d 100644 --- a/velox/connectors/hive/PartitionIdGenerator.h +++ b/velox/connectors/hive/PartitionIdGenerator.h @@ -38,10 +38,12 @@ class PartitionIdGenerator { memory::MemoryPool* pool, bool partitionPathAsLowerCase); + virtual ~PartitionIdGenerator() = default; + /// Generate sequential partition IDs for input vector. /// @param input Input RowVector. /// @param result Generated integer IDs indexed by input row number. - void run(const RowVectorPtr& input, raw_vector& result); + virtual void run(const RowVectorPtr& input, raw_vector& result); /// Return the total number of distinct partitions processed so far. uint64_t numPartitions() const { @@ -52,16 +54,38 @@ class PartitionIdGenerator { /// style. It is derived from the partitionValues_ at index partitionId. /// Partition keys appear in the order of partition columns in the table /// schema. - std::string partitionName(uint64_t partitionId) const; + virtual std::string partitionName(uint64_t partitionId) const; - private: - static constexpr const int32_t kHasherReservePct = 20; + protected: + PartitionIdGenerator( + std::vector partitionChannels, + uint32_t maxPartitions, + memory::MemoryPool* pool, + bool partitionPathAsLowerCase); // Computes value IDs using VectorHashers for all rows in 'input'. void computeValueIds( const RowVectorPtr& input, raw_vector& valueIds); + const std::vector partitionChannels_; + + std::vector> hashers_; + + // A vector holding unique partition key values. One row per partition. Row + // numbers match partition IDs. + RowVectorPtr partitionValues_; + + const uint32_t maxPartitions_; + + // A mapping from value ID produced by VectorHashers to a partition ID. + std::unordered_map partitionIds_; + + const bool partitionPathAsLowerCase_; + + private: + static constexpr const int32_t kHasherReservePct = 20; + // In case of rehash (when value IDs produced by VectorHashers change), we // update value id for pre-existing partitions while keeping partition ids. // This method rebuilds 'partitionIds_' by re-calculating the value ids using @@ -70,29 +94,15 @@ class PartitionIdGenerator { // Copies partition values of 'row' from 'input' into 'partitionId' row in // 'partitionValues_'. - void savePartitionValues( - uint64_t partitionId, + virtual void savePartitionValues( + uint32_t partitionId, const RowVectorPtr& input, vector_size_t row); memory::MemoryPool* const pool_; - const std::vector partitionChannels_; - - const uint32_t maxPartitions_; - - const bool partitionPathAsLowerCase_; - - std::vector> hashers_; bool hasMultiplierSet_ = false; - // A mapping from value ID produced by VectorHashers to a partition ID. - std::unordered_map partitionIds_; - - // A vector holding unique partition key values. One row per partition. Row - // numbers match partition IDs. - RowVectorPtr partitionValues_; - // All rows are set valid to compute partition IDs for all input rows. SelectivityVector allRows_; }; diff --git a/velox/connectors/hive/SplitReader.cpp b/velox/connectors/hive/SplitReader.cpp index 7f4e3ad42899..32fdd4738fb8 100644 --- a/velox/connectors/hive/SplitReader.cpp +++ b/velox/connectors/hive/SplitReader.cpp @@ -54,6 +54,18 @@ VectorPtr newConstantFromString( pool, size, false, type, std::move(days)); } + if constexpr (std::is_same_v || std::is_same_v) { + if (type->isDecimal()) { + auto [precision, scale] = getDecimalPrecisionScale(*type); + T result; + const auto status = DecimalUtil::castFromString( + StringView(value.value()), precision, scale, result); + VELOX_USER_CHECK(status.ok(), status.message()); + return std::make_shared>( + pool, size, false, type, std::move(result)); + } + } + if constexpr (std::is_same_v) { return std::make_shared>( pool, size, false, type, StringView(value.value())); @@ -84,7 +96,9 @@ std::unique_ptr SplitReader::create( const std::shared_ptr& fsStats, FileHandleFactory* fileHandleFactory, folly::Executor* executor, - const std::shared_ptr& scanSpec) { + const std::shared_ptr& scanSpec, + core::ExpressionEvaluator* expressionEvaluator, + std::atomic& totalRemainingFilterTime) { // Create the SplitReader based on hiveSplit->customSplitInfo["table_format"] if (hiveSplit->customSplitInfo.count("table_format") > 0 && hiveSplit->customSplitInfo["table_format"] == "hive-iceberg") { @@ -99,7 +113,9 @@ std::unique_ptr SplitReader::create( fsStats, fileHandleFactory, executor, - scanSpec); + scanSpec, + expressionEvaluator, + totalRemainingFilterTime); } else { return std::unique_ptr(new SplitReader( hiveSplit, @@ -416,11 +432,17 @@ std::vector SplitReader::adaptColumns( auto fileTypeIdx = fileType->getChildIdxIfExists(fieldName); if (!fileTypeIdx.has_value()) { // Column is missing. Most likely due to schema evolution. - VELOX_CHECK(tableSchema, "Unable to resolve column '{}'", fieldName); + auto outputTypeIdx = readerOutputType_->getChildIdxIfExists(fieldName); + TypePtr fieldType; + if (outputTypeIdx.has_value()) { + // Field name exists in the user-specified output type. + fieldType = readerOutputType_->childAt(outputTypeIdx.value()); + } else { + VELOX_CHECK(tableSchema, "Unable to resolve column '{}'", fieldName); + fieldType = tableSchema->findChild(fieldName); + } childSpec->setConstantValue(BaseVector::createNullConstant( - tableSchema->findChild(fieldName), - 1, - connectorQueryCtx_->memoryPool())); + fieldType, 1, connectorQueryCtx_->memoryPool())); } else { // Column no longer missing, reset constant value set on the spec. childSpec->setConstantValue(nullptr); diff --git a/velox/connectors/hive/SplitReader.h b/velox/connectors/hive/SplitReader.h index 1860b67d2b21..08836ca81b8a 100644 --- a/velox/connectors/hive/SplitReader.h +++ b/velox/connectors/hive/SplitReader.h @@ -67,7 +67,9 @@ class SplitReader { const std::shared_ptr& fsStats, FileHandleFactory* fileHandleFactory, folly::Executor* executor, - const std::shared_ptr& scanSpec); + const std::shared_ptr& scanSpec, + core::ExpressionEvaluator* expressionEvaluator, + std::atomic& totalRemainingFilterTime); virtual ~SplitReader() = default; @@ -160,18 +162,18 @@ class SplitReader { VectorPtr& output, const std::vector& ranges); + void setPartitionValue( + common::ScanSpec* spec, + const std::string& partitionKey, + const std::optional& value) const; + private: /// Different table formats may have different meatadata columns. /// This function will be used to update the scanSpec for these columns. - std::vector adaptColumns( + virtual std::vector adaptColumns( const RowTypePtr& fileType, const std::shared_ptr& tableSchema) const; - void setPartitionValue( - common::ScanSpec* spec, - const std::string& partitionKey, - const std::optional& value) const; - protected: std::shared_ptr hiveSplit_; const std::shared_ptr hiveTableHandle_; diff --git a/velox/connectors/hive/iceberg/CMakeLists.txt b/velox/connectors/hive/iceberg/CMakeLists.txt index bc78005c91bb..dc91ae4778d0 100644 --- a/velox/connectors/hive/iceberg/CMakeLists.txt +++ b/velox/connectors/hive/iceberg/CMakeLists.txt @@ -12,11 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. -velox_add_library(velox_hive_iceberg_splitreader IcebergSplitReader.cpp - IcebergSplit.cpp PositionalDeleteFileReader.cpp) +velox_add_library( + velox_hive_iceberg_splitreader + EqualityDeleteFileReader.cpp + FilterUtil.cpp + IcebergColumnHandle.cpp + IcebergDeleteFile.cpp + DataFileStatsCollector.cpp + IcebergDataSink.cpp + IcebergPartitionIdGenerator.cpp + IcebergSplit.cpp + IcebergSplitReader.cpp + Murmur3.cpp + PartitionSpec.cpp + PositionalDeleteFileReader.cpp + TransformFactory.cpp + Transforms.cpp) velox_link_libraries(velox_hive_iceberg_splitreader velox_connector - Folly::folly) + velox_dwio_common Folly::folly) + +if(VELOX_ENABLE_PARQUET) + velox_link_libraries(velox_hive_iceberg_splitreader + velox_dwio_arrow_parquet_writer) +endif() if(${VELOX_BUILD_TESTING}) add_subdirectory(tests) diff --git a/velox/connectors/hive/iceberg/DataFileStatsCollector.cpp b/velox/connectors/hive/iceberg/DataFileStatsCollector.cpp new file mode 100644 index 000000000000..98b05f81f0e9 --- /dev/null +++ b/velox/connectors/hive/iceberg/DataFileStatsCollector.cpp @@ -0,0 +1,121 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "velox/connectors/hive/iceberg/DataFileStatsCollector.h" +#include "velox/common/base/Exceptions.h" +#include "velox/common/encode/Base64.h" +#include "velox/dwio/parquet/writer/arrow/Metadata.h" +#include "velox/dwio/parquet/writer/arrow/Statistics.h" + +namespace facebook::velox::connector::hive::iceberg { + +using namespace facebook::velox::parquet; + +DataFileStatsCollector::DataFileStatsCollector( + std::shared_ptr< + std::vector>> + settings) + : FileStatsCollector(std::move(settings)) {} + +void DataFileStatsCollector::collectStats( + const void* metadata, + const std::shared_ptr& dataFileStats) { + const auto& fileMetadata = + *static_cast*>( + metadata); + VELOX_CHECK_NOT_NULL(fileMetadata); + + std::unordered_set skipBoundsFields; + std::function processFields = + [&skipBoundsFields, + &processFields](IcebergDataFileStatsSettings* field) -> int32_t { + if (field->skipBounds) { + skipBoundsFields.insert(field->fieldId); + } + if (field->children.empty()) { + return 1; + } + int32_t count = 0; + for (const auto& child : field->children) { + count += processFields(child.get()); + } + return count; + }; + + // numFields is not the number of columns in Iceberg table's schema, + // e.g., schema_->size(). It also contains the sub-fields when there are + // nested types in table's schema. + int32_t numFields = 0; + for (const auto& field : *statsSetting_) { + auto* icebergField = + static_cast(field.get()); + numFields += processFields(icebergField); + } + + std::unordered_map> + globalMinStats; + std::unordered_map> + globalMaxStats; + + dataFileStats->numRecords = fileMetadata->num_rows(); + const auto numRowGroups = fileMetadata->num_row_groups(); + for (auto i = 0; i < numRowGroups; ++i) { + const auto rgm = fileMetadata->RowGroup(i); + VELOX_CHECK_EQ(numFields, rgm->num_columns()); + dataFileStats->splitOffsets.emplace_back(rgm->file_offset()); + + for (auto j = 0; j < numFields; ++j) { + const auto columnChunkMetadata = rgm->ColumnChunk(j); + const auto fieldId = columnChunkMetadata->field_id(); + const auto numValues = columnChunkMetadata->num_values(); + + dataFileStats->valueCounts[fieldId] += numValues; + dataFileStats->columnsSizes[fieldId] += + columnChunkMetadata->total_compressed_size(); + + const auto columnChunkStats = columnChunkMetadata->statistics(); + if (columnChunkStats->nan_count() > 0) { + dataFileStats->nanValueCounts[fieldId] += columnChunkStats->nan_count(); + } + dataFileStats->nullValueCounts[fieldId] += columnChunkStats->null_count(); + + if (columnChunkStats->HasMinMax() && + !skipBoundsFields.contains(fieldId)) { + if (globalMaxStats.find(fieldId) == globalMaxStats.end()) { + globalMinStats[fieldId] = columnChunkStats; + globalMaxStats[fieldId] = columnChunkStats; + } else { + globalMaxStats[fieldId] = arrow::Statistics::CompareAndGetMax( + globalMaxStats[fieldId], columnChunkStats); + globalMinStats[fieldId] = arrow::Statistics::CompareAndGetMin( + globalMinStats[fieldId], columnChunkStats); + } + } + } + } + + for (const auto& [fieldId, minStats] : globalMinStats) { + const auto lowerBound = minStats->MinValue(); + dataFileStats->lowerBounds[fieldId] = + encoding::Base64::encode(lowerBound.data(), lowerBound.size()); + } + for (const auto& [fieldId, maxStats] : globalMaxStats) { + const auto upperBound = maxStats->MaxValue(); + dataFileStats->upperBounds[fieldId] = + encoding::Base64::encode(upperBound.data(), upperBound.size()); + } +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/DataFileStatsCollector.h b/velox/connectors/hive/iceberg/DataFileStatsCollector.h new file mode 100644 index 000000000000..e379dc391799 --- /dev/null +++ b/velox/connectors/hive/iceberg/DataFileStatsCollector.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include "velox/dwio/common/DataFileStatsCollector.h" + +namespace facebook::velox::connector::hive::iceberg { + +/// Settings for collecting Iceberg parquet data file statistics. +/// Holds the Iceberg source field id and whether to skip bounds +/// collection for this field. For nested field, it contains child fields. +struct IcebergDataFileStatsSettings + : public dwio::common::DataFileStatsSettings { + int32_t fieldId; + bool skipBounds; + std::vector> children; + + IcebergDataFileStatsSettings(int32_t id, bool skip) + : fieldId(id), skipBounds(skip), children() {} +}; + +class DataFileStatsCollector : public dwio::common::FileStatsCollector { + public: + explicit DataFileStatsCollector( + std::shared_ptr< + std::vector>> + settings); + + void collectStats( + const void* metadata, + const std::shared_ptr& fileStats) + override; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/EqualityDeleteFileReader.cpp b/velox/connectors/hive/iceberg/EqualityDeleteFileReader.cpp new file mode 100644 index 000000000000..47b6bdf1b2e6 --- /dev/null +++ b/velox/connectors/hive/iceberg/EqualityDeleteFileReader.cpp @@ -0,0 +1,224 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/EqualityDeleteFileReader.h" + +#include "velox/connectors/hive/HiveConnectorUtil.h" +#include "velox/connectors/hive/iceberg/FilterUtil.h" +#include "velox/connectors/hive/iceberg/IcebergDeleteFile.h" +#include "velox/core/Expressions.h" +#include "velox/dwio/common/ReaderFactory.h" + +using namespace facebook::velox::common; +using namespace facebook::velox::core; +using namespace facebook::velox::exec; + +namespace facebook::velox::connector::hive::iceberg { + +static constexpr const int kMaxBatchRows = 10'000; + +EqualityDeleteFileReader::EqualityDeleteFileReader( + const IcebergDeleteFile& deleteFile, + std::shared_ptr baseFileSchema, + FileHandleFactory* fileHandleFactory, + folly::Executor* executor, + const ConnectorQueryCtx* connectorQueryCtx, + const std::shared_ptr& hiveConfig, + const std::shared_ptr ioStats, + const std::shared_ptr& fsStats, + const std::string& connectorId) + : deleteFile_(deleteFile), + baseFileSchema_(baseFileSchema), + fileHandleFactory_(fileHandleFactory), + pool_(connectorQueryCtx->memoryPool()), + deleteSplit_(nullptr), + deleteRowReader_(nullptr) { + VELOX_CHECK_EQ(deleteFile_.content, FileContent::kEqualityDeletes); + + if (deleteFile_.recordCount == 0) { + return; + } + + // TODO: push down filter if previous delete file contains this one. E.g. + // previous equality delete file has a=1, and this file also contains + // columns a, then a!=1 can be pushed as a filter when reading this delete + // file. + + deleteSplit_ = std::make_shared( + connectorId, + deleteFile_.filePath, + deleteFile_.fileFormat, + 0, + deleteFile_.fileSizeInBytes); + + // Create the Reader and RowReader for the equality delete file + + dwio::common::ReaderOptions deleteReaderOpts(pool_); + configureReaderOptions( + hiveConfig, + connectorQueryCtx, + nullptr, + deleteSplit_, + {}, + deleteReaderOpts); + + const FileHandleKey fileHandleKey{ + .filename = deleteFile_.filePath, + .tokenProvider = connectorQueryCtx->fsTokenProvider()}; + auto deleteFileHandleCachePtr = fileHandleFactory_->generate(fileHandleKey); + auto deleteFileInput = createBufferedInput( + *deleteFileHandleCachePtr, + deleteReaderOpts, + connectorQueryCtx, + ioStats, + fsStats, + executor); + + auto deleteReader = + dwio::common::getReaderFactory(deleteReaderOpts.fileFormat()) + ->createReader(std::move(deleteFileInput), deleteReaderOpts); + + // For now, we assume only the delete columns are written in the delete file + deleteFileRowType_ = deleteReader->rowType(); + auto scanSpec = std::make_shared(""); + scanSpec->addAllChildFields(deleteFileRowType_->asRow()); + + dwio::common::RowReaderOptions deleteRowReaderOpts; + configureRowReaderOptions( + {}, + scanSpec, + nullptr, + deleteFileRowType_, + deleteSplit_, + hiveConfig, + connectorQueryCtx->sessionProperties(), + deleteRowReaderOpts); + + deleteRowReader_.reset(); + deleteRowReader_ = deleteReader->createRowReader(deleteRowReaderOpts); +} + +void EqualityDeleteFileReader::readDeleteValues( + SubfieldFilters& subfieldFilters, + std::vector& expressionInputs) { + VELOX_CHECK(deleteRowReader_); + VELOX_CHECK(deleteSplit_); + + if (!deleteValuesOutput_) { + deleteValuesOutput_ = BaseVector::create(deleteFileRowType_, 0, pool_); + } + + // TODO: verfiy if the field is an Iceberg RowId. Velox currently doesn't + // support pushing down filters to non-RowId types, i.e. sub-fields of Array + // or Map + if (deleteFileRowType_->size() == 1) { + // Construct the IN list filter that can be pushed down to the base file + // readers, then update the baseFileScanSpec. + buildDomainFilter(subfieldFilters); + } else { + // Build the filter functions that will be evaluated after all base file + // read is done + buildFilterFunctions(expressionInputs); + } + + deleteSplit_.reset(); +} + +void EqualityDeleteFileReader::buildDomainFilter( + SubfieldFilters& subfieldFilters) { + std::unique_ptr filter = std::make_unique(); + auto name = baseFileSchema_->childByFieldId(deleteFile_.equalityFieldIds[0]) + ->fullName(); + while (deleteRowReader_->next(kMaxBatchRows, deleteValuesOutput_)) { + if (deleteValuesOutput_->size() == 0) { + continue; + } + + deleteValuesOutput_->loadedVector(); + auto vector = + std::dynamic_pointer_cast(deleteValuesOutput_)->childAt(0); + + auto typeKind = vector->type()->kind(); + VELOX_CHECK( + typeKind != TypeKind::DOUBLE && typeKind != TypeKind::REAL, + "Iceberg does not allow DOUBLE or REAL columns as the equality delete columns: {} : {}", + name, + typeKind); + + auto notExistsFilter = + createNotInFilter(vector, 0, deleteValuesOutput_->size(), typeKind); + filter = filter->mergeWith(notExistsFilter.get()); + } + + if (filter->kind() != FilterKind::kAlwaysTrue) { + if (subfieldFilters.find(common::Subfield(name)) != subfieldFilters.end()) { + subfieldFilters[common::Subfield(name)] = + subfieldFilters[common::Subfield(name)]->mergeWith(filter.get()); + } else { + subfieldFilters[common::Subfield(name)] = std::move(filter); + } + } +} + +void EqualityDeleteFileReader::buildFilterFunctions( + std::vector& expressionInputs) { + auto numDeleteFields = deleteFileRowType_->size(); + VELOX_CHECK_GT( + numDeleteFields, + 0, + "Iceberg equality delete file should have at least one field."); + + // TODO: logical expression simplifications + while (deleteRowReader_->next(kMaxBatchRows, deleteValuesOutput_)) { + if (deleteValuesOutput_->size() == 0) { + continue; + } + + deleteValuesOutput_->loadedVector(); + auto rowVector = std::dynamic_pointer_cast(deleteValuesOutput_); + auto numDeletedValues = rowVector->childAt(0)->size(); + + for (int i = 0; i < numDeletedValues; i++) { + std::vector disconjunctInputs; + + for (int j = 0; j < numDeleteFields; j++) { + auto type = deleteFileRowType_->childAt(j); + auto name = + baseFileSchema_->childByFieldId(deleteFile_.equalityFieldIds[j]) + ->fullName(); + auto value = BaseVector::wrapInConstant(1, i, rowVector->childAt(j)); + + std::vector isNotEqualInputs; + isNotEqualInputs.push_back( + std::make_shared(type, name)); + isNotEqualInputs.push_back(std::make_shared(value)); + // TODO: generalize this to support different engines. Currently, only + // Presto "neq" is supported. Spark does not register the "neq" function + // but does support "not" and "equalto" functions. + auto isNotEqualExpr = + std::make_shared(BOOLEAN(), isNotEqualInputs, "neq"); + + disconjunctInputs.push_back(isNotEqualExpr); + } + + auto disconjunctNotEqualExpr = + std::make_shared(BOOLEAN(), disconjunctInputs, "or"); + expressionInputs.push_back(disconjunctNotEqualExpr); + } + } +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/EqualityDeleteFileReader.h b/velox/connectors/hive/iceberg/EqualityDeleteFileReader.h new file mode 100644 index 000000000000..99ce239b9f9d --- /dev/null +++ b/velox/connectors/hive/iceberg/EqualityDeleteFileReader.h @@ -0,0 +1,87 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/connectors/Connector.h" +#include "velox/connectors/hive/FileHandle.h" +#include "velox/connectors/hive/HiveConfig.h" +#include "velox/connectors/hive/HiveConnectorSplit.h" +#include "velox/dwio/common/Reader.h" +#include "velox/expression/Expr.h" + +namespace facebook::velox::connector::hive::iceberg { + +class IcebergDeleteFile; + +using SubfieldFilters = + std::unordered_map>; + +class EqualityDeleteFileReader { + public: + EqualityDeleteFileReader( + const IcebergDeleteFile& deleteFile, + std::shared_ptr baseFileSchema, + FileHandleFactory* fileHandleFactory, + folly::Executor* executor, + const ConnectorQueryCtx* connectorQueryCtx, + const std::shared_ptr& hiveConfig, + const std::shared_ptr ioStats, + const std::shared_ptr& fsStats, + const std::string& connectorId); + + /// Reads the delete values from the equality delete file, and interprets them + /// as filters for the base file reader. + /// + /// @subfieldFilters The built SubfieldFilter that can be pushed down to the + /// base file RowReader, when the equality delete file only contains one + /// single subfield of Iceberg RowId type. + /// @typedExpressions The built TypedExpr that will be evaluated by the + /// connector DataSource after rows are read from the base file RowReader. + void readDeleteValues( + SubfieldFilters& subfieldFilters, + std::vector& typedExpressions); + + private: + void buildDomainFilter(SubfieldFilters& subfieldFilters); + + void buildFilterFunctions(std::vector& expressionInputs); + + // The equality delete file to read + const IcebergDeleteFile& deleteFile_; + // The schema of the base file in terms of TypeWithId tree. In addition to the + // existing fields that were included in the base file ScanSpec, it also + // contains the extra fields that are in the equality delete file but not + // in the ScanSpec of the base file + const std::shared_ptr baseFileSchema_; + + // The cache factory of the file handles, which can be used to return the file + // handle of the delete file. + FileHandleFactory* const fileHandleFactory_; + memory::MemoryPool* const pool_; + + // The split of the equality delete file to be processed by the delete file + // RowReader. + std::shared_ptr deleteSplit_; + // The RowType of the equality delete file + RowTypePtr deleteFileRowType_; + // The RowReader to read the equality delete file + std::unique_ptr deleteRowReader_; + // The output vector to hold the delete values + VectorPtr deleteValuesOutput_; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/FilterUtil.cpp b/velox/connectors/hive/iceberg/FilterUtil.cpp new file mode 100644 index 000000000000..7ef26f5e18db --- /dev/null +++ b/velox/connectors/hive/iceberg/FilterUtil.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "velox/connectors/hive/iceberg/FilterUtil.h" + +namespace facebook::velox::connector::hive::iceberg { + +using namespace facebook::velox::exec; +using namespace facebook::velox::core; + +template +std::unique_ptr createNegatedBigintValuesFilter( + const VectorPtr& valuesVector, + vector_size_t offset, + vector_size_t size) { + auto valuesPair = + common::deDuplicateValues(valuesVector, offset, size); + + const auto& values = valuesPair.first; + bool hasNull = valuesPair.second; + + return common::createNegatedBigintValues(values, !hasNull); +} + +std::unique_ptr createNotInFilter( + const VectorPtr& elements, + vector_size_t offset, + vector_size_t size, + TypeKind type) { + std::unique_ptr filter; + switch (type) { + case TypeKind::HUGEINT: + // TODO: createNegatedHugeintValuesFilter is not implemented yet. + VELOX_NYI("createNegatedHugeintValuesFilter is not implemented yet"); + case TypeKind::BIGINT: + filter = createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::INTEGER: + filter = createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::SMALLINT: + filter = createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::TINYINT: + filter = createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::BOOLEAN: + // Hack: using BIGINT filter for bool, which is essentially "int1_t". + filter = createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::TIMESTAMP: + filter = + createNegatedBigintValuesFilter(elements, offset, size); + break; + case TypeKind::VARCHAR: + case TypeKind::VARBINARY: + // TODO: createNegatedBytesValuesFilter is not implemented yet. + VELOX_NYI("createNegatedBytesValuesFilter is not implemented yet"); + case TypeKind::REAL: + case TypeKind::DOUBLE: + case TypeKind::UNKNOWN: + [[fallthrough]]; + case TypeKind::ARRAY: + [[fallthrough]]; + case TypeKind::MAP: + [[fallthrough]]; + case TypeKind::ROW: + [[fallthrough]]; + default: + VELOX_USER_FAIL( + "Iceberg equality delete column cannot be of type ", type); + } + return filter; +} +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/FilterUtil.h b/velox/connectors/hive/iceberg/FilterUtil.h new file mode 100644 index 000000000000..96b8255511c2 --- /dev/null +++ b/velox/connectors/hive/iceberg/FilterUtil.h @@ -0,0 +1,30 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/expression/Expr.h" +#include "velox/type/Filter.h" +#include "velox/vector/ComplexVector.h" + +namespace facebook::velox::connector::hive::iceberg { +std::unique_ptr createNotInFilter( + const VectorPtr& elements, + vector_size_t offset, + vector_size_t size, + TypeKind type); + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergColumnHandle.cpp b/velox/connectors/hive/iceberg/IcebergColumnHandle.cpp new file mode 100644 index 000000000000..1b5028ae97be --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergColumnHandle.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/IcebergColumnHandle.h" + +namespace facebook::velox::connector::hive::iceberg { + +IcebergColumnHandle::IcebergColumnHandle( + const std::string& name, + ColumnType columnType, + TypePtr dataType, + TypePtr hiveType, + const IcebergNestedField& nestedField, + std::vector requiredSubfields, + ColumnParseParameters columnParseParameters) + : HiveColumnHandle( + name, + columnType, + dataType, + hiveType, + std::move(requiredSubfields), + columnParseParameters), + nestedField_(nestedField) {} + +const IcebergNestedField& IcebergColumnHandle::nestedField() const { + return nestedField_; +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergColumnHandle.h b/velox/connectors/hive/iceberg/IcebergColumnHandle.h new file mode 100644 index 000000000000..db591178122e --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergColumnHandle.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include "velox/connectors/hive/TableHandle.h" + +namespace facebook::velox::connector::hive::iceberg { + +struct IcebergNestedField { + int32_t id; + std::vector children; +}; + +class IcebergColumnHandle : public HiveColumnHandle { + public: + IcebergColumnHandle( + const std::string& name, + ColumnType columnType, + TypePtr dataType, + TypePtr hiveType, + const IcebergNestedField& nestedField, + std::vector requiredSubfields = {}, + ColumnParseParameters columnParseParameters = {}); + + const IcebergNestedField& nestedField() const; + + private: + const IcebergNestedField nestedField_; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergDataSink.cpp b/velox/connectors/hive/iceberg/IcebergDataSink.cpp new file mode 100644 index 000000000000..030ec7d6b000 --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergDataSink.cpp @@ -0,0 +1,526 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/IcebergDataSink.h" +#include "velox/common/base/Fs.h" +#include "velox/connectors/hive/HiveConnectorUtil.h" +#include "velox/connectors/hive/iceberg/DataFileStatsCollector.h" +#include "velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h" +#include "velox/dwio/common/SortingWriter.h" +#ifdef VELOX_ENABLE_PARQUET +#include "velox/dwio/parquet/writer/Writer.h" +#endif + +#include "velox/exec/OperatorUtils.h" +#include "velox/exec/SortBuffer.h" + +namespace facebook::velox::connector::hive::iceberg { + +namespace { + +#define WRITER_NON_RECLAIMABLE_SECTION_GUARD(index) \ + memory::NonReclaimableSectionGuard nonReclaimableGuard( \ + writerInfo_[(index)]->nonReclaimableSectionHolder.get()) + +std::string toJson(const std::vector& partitionValues) { + folly::dynamic jsonObject = folly::dynamic::object(); + folly::dynamic valuesArray = folly::dynamic::array(); + for (const auto& value : partitionValues) { + valuesArray.push_back(value); + } + jsonObject["partitionValues"] = valuesArray; + return folly::toJson(jsonObject); +} + +template +folly::dynamic extractPartitionValue( + const DecodedVector* block, + vector_size_t row) { + using T = typename TypeTraits::NativeType; + return block->valueAt(row); +} + +template <> +folly::dynamic extractPartitionValue( + const DecodedVector* block, + vector_size_t row) { + auto value = block->valueAt(row); + return value.str(); +} + +template <> +folly::dynamic extractPartitionValue( + const DecodedVector* block, + vector_size_t row) { + auto value = block->valueAt(row); + return value.str(); +} + +template <> +folly::dynamic extractPartitionValue( + const DecodedVector* block, + vector_size_t row) { + auto timestamp = block->valueAt(row); + return timestamp.toMicros(); +} + +class IcebergFileNameGenerator : public FileNameGenerator { + public: + IcebergFileNameGenerator() {} + + std::pair gen( + std::optional bucketId, + const std::shared_ptr insertTableHandle, + const ConnectorQueryCtx& connectorQueryCtx, + bool commitRequired) const override; + + folly::dynamic serialize() const override; + + std::string toString() const override; +}; + +std::pair IcebergFileNameGenerator::gen( + std::optional bucketId, + const std::shared_ptr insertTableHandle, + const ConnectorQueryCtx& connectorQueryCtx, + bool commitRequired) const { + auto targetFileName = insertTableHandle->locationHandle()->targetFileName(); + if (targetFileName.empty()) { + targetFileName = fmt::format("{}", makeUuid()); + } + + return { + fmt::format("{}{}", targetFileName, ".parquet"), + fmt::format("{}{}", targetFileName, ".parquet")}; +} + +folly::dynamic IcebergFileNameGenerator::serialize() const { + VELOX_UNREACHABLE("Unexpected code path, implement serialize() first."); +} + +std::string IcebergFileNameGenerator::toString() const { + return "IcebergFileNameGenerator"; +} + +} // namespace + +IcebergInsertTableHandle::IcebergInsertTableHandle( + std::vector> inputColumns, + std::shared_ptr locationHandle, + std::shared_ptr partitionSpec, + memory::MemoryPool* pool, + dwio::common::FileFormat tableStorageFormat, + const std::vector& sortedBy, + std::optional compressionKind, + const std::unordered_map& serdeParameters) + : HiveInsertTableHandle( + std::vector>( + inputColumns.begin(), + inputColumns.end()), + std::move(locationHandle), + tableStorageFormat, + nullptr, + compressionKind, + serdeParameters, + nullptr, + false, + std::make_shared()), + partitionSpec_(std::move(partitionSpec)), + columnTransforms_( + parsePartitionTransformSpecs(partitionSpec_->fields, pool)), + sortedBy_(sortedBy) {} + +IcebergDataSink::IcebergDataSink( + facebook::velox::RowTypePtr inputType, + const std::shared_ptr& insertTableHandle, + const facebook::velox::connector::ConnectorQueryCtx* connectorQueryCtx, + facebook::velox::connector::CommitStrategy commitStrategy, + const std::shared_ptr& hiveConfig) + : IcebergDataSink( + std::move(inputType), + insertTableHandle, + connectorQueryCtx, + commitStrategy, + hiveConfig, + [&insertTableHandle]() { + const auto& inputColumns = insertTableHandle->inputColumns(); + const auto& partitionSpec = insertTableHandle->partitionSpec(); + std::unordered_map partitionKeyMap; + for (auto i = 0; i < inputColumns.size(); ++i) { + if (inputColumns[i]->isPartitionKey()) { + partitionKeyMap[inputColumns[i]->name()] = i; + } + } + std::vector channels; + channels.reserve(partitionSpec->fields.size()); + for (const auto& field : partitionSpec->fields) { + if (auto it = partitionKeyMap.find(field.name); + it != partitionKeyMap.end()) { + channels.push_back(it->second); + } + } + return channels; + }(), + [&insertTableHandle]() { + std::vector channels( + insertTableHandle->inputColumns().size()); + std::iota(channels.begin(), channels.end(), 0); + return channels; + }()) {} + +IcebergDataSink::IcebergDataSink( + RowTypePtr inputType, + const std::shared_ptr& insertTableHandle, + const ConnectorQueryCtx* connectorQueryCtx, + CommitStrategy commitStrategy, + const std::shared_ptr& hiveConfig, + const std::vector& partitionChannels, + const std::vector& dataChannels) + : HiveDataSink( + inputType, + insertTableHandle, + connectorQueryCtx, + commitStrategy, + hiveConfig, + 0, + nullptr, + partitionChannels, + dataChannels, + !partitionChannels.empty() + ? std::make_unique( + partitionChannels, + hiveConfig->maxPartitionsPerWriters( + connectorQueryCtx->sessionProperties()), + connectorQueryCtx->memoryPool(), + insertTableHandle->columnTransforms(), + hiveConfig->isPartitionPathAsLowerCase( + connectorQueryCtx->sessionProperties())) + : nullptr) { + if (isPartitioned()) { + partitionData_.resize(maxOpenWriters_); + } + const auto& inputColumns = insertTableHandle_->inputColumns(); + + std::function + buildNestedField = [&](const IcebergNestedField& f, + const TypePtr& type, + bool skipBounds) -> IcebergDataFileStatsSettings { + VELOX_CHECK_NOT_NULL(type, "Input column type cannot be null."); + bool currentSkipBounds = skipBounds || type->isMap() || type->isArray(); + IcebergDataFileStatsSettings field(f.id, currentSkipBounds); + if (!f.children.empty()) { + VELOX_CHECK_EQ(f.children.size(), type->size()); + field.children.reserve(f.children.size()); + if (type->isRow()) { + auto rowType = asRowType(type); + for (size_t i = 0; i < f.children.size(); ++i) { + field.children.push_back( + std::make_unique(buildNestedField( + f.children[i], rowType->childAt(i), currentSkipBounds))); + } + } else if (type->isArray()) { + auto arrayType = type->asArray(); + field.children.push_back( + std::make_unique(buildNestedField( + f.children[0], arrayType.elementType(), currentSkipBounds))); + } else if (type->isMap()) { + auto mapType = type->asMap(); + for (size_t i = 0; i < f.children.size(); ++i) { + field.children.push_back( + std::make_unique(buildNestedField( + f.children[i], mapType.childAt(i), currentSkipBounds))); + } + } + } + return field; + }; + + statsSettings_ = std::make_shared< + std::vector>>(); + for (const auto& columnHandle : inputColumns) { + auto icebergColumnHandle = + std::dynamic_pointer_cast(columnHandle); + VELOX_CHECK_NOT_NULL(icebergColumnHandle, "Invalid IcebergColumnHandle."); + statsSettings_->push_back( + std::make_unique(buildNestedField( + icebergColumnHandle->nestedField(), + icebergColumnHandle->dataType(), + false))); + } + + icebergStatsCollector_ = + std::make_unique(statsSettings_); + + const auto& sortedBy = insertTableHandle->sortedBy(); + if (!sortedBy.empty()) { + sortColumnIndices_.reserve(sortedBy.size()); + sortCompareFlags_.reserve(sortedBy.size()); + for (auto i = 0; i < sortedBy.size(); ++i) { + auto columnIndex = + inputType_->getChildIdxIfExists(sortedBy[i].sortColumn()); + if (columnIndex.has_value()) { + sortColumnIndices_.push_back(columnIndex.value()); + sortCompareFlags_.push_back( + {sortedBy[i].sortOrder().isNullsFirst(), + sortedBy[i].sortOrder().isAscending(), + false, + CompareFlags::NullHandlingMode::kNullAsValue}); + } + } + } +} + +std::vector IcebergDataSink::commitMessage() const { + auto icebergInsertTableHandle = + std::dynamic_pointer_cast( + insertTableHandle_); + + std::vector commitTasks; + commitTasks.reserve(writerInfo_.size()); + std::string fileFormat(toString(insertTableHandle_->storageFormat())); + std::transform( + fileFormat.begin(), fileFormat.end(), fileFormat.begin(), ::toupper); + + for (auto i = 0; i < writerInfo_.size(); ++i) { + const auto& info = writerInfo_.at(i); + VELOX_CHECK_NOT_NULL(info); + // Following metadata (json format) is consumed by Presto CommitTaskData. + // It contains the minimal subset of metadata. + // Complete metrics is missing now and this could lead to suboptimal query + // plan, will collect full iceberg metrics in following PR. + // clang-format off + folly::dynamic commitData = + folly::dynamic::object + ("path", + (fs::path(info->writerParameters.writeDirectory()) / + info->writerParameters.writeFileName()).string()) + ("fileSizeInBytes", ioStats_.at(i)->rawBytesWritten()) + ("metrics", dataFileStats_[i]->toJson()) + ("splitOffsets", dataFileStats_[i]->splitOffsetsAsJson()) + // Sort order evolution is not supported. Set default id to 1. + ("sortOrderId", 1) + ("partitionSpecJson", icebergInsertTableHandle->partitionSpec()->specId) + ("fileFormat", fileFormat) + ("content", "DATA"); + // clang-format on + if (!(partitionData_.empty() || partitionData_[i].empty())) { + commitData["partitionDataJson"] = toJson(partitionData_[i]); + } + auto commitDataJson = folly::toJson(commitData); + commitTasks.push_back(commitDataJson); + } + return commitTasks; +} + +void IcebergDataSink::splitInputRowsAndEnsureWriters(RowVectorPtr input) { + VELOX_CHECK(isPartitioned()); + + std::fill(partitionSizes_.begin(), partitionSizes_.end(), 0); + + const auto numRows = partitionIds_.size(); + for (auto row = 0; row < numRows; ++row) { + auto id = getIcebergWriterId(row); + uint32_t index = ensureWriter(id); + + updatePartitionRows(index, numRows, row); + + if (!partitionData_[index].empty()) { + continue; + } + + std::vector partitionValues(partitionChannels_.size()); + auto icebergPartitionIdGenerator = + dynamic_cast( + partitionIdGenerator_.get()); + VELOX_CHECK_NOT_NULL(icebergPartitionIdGenerator); + const RowVectorPtr transformedValues = + icebergPartitionIdGenerator->partitionValues(); + for (auto i = 0; i < partitionChannels_.size(); ++i) { + auto block = transformedValues->childAt(i); + if (block->isNullAt(index)) { + partitionValues[i] = nullptr; + } else { + DecodedVector decoded(*block); + partitionValues[i] = VELOX_DYNAMIC_SCALAR_TYPE_DISPATCH( + extractPartitionValue, block->typeKind(), &decoded, index); + } + } + + partitionData_[index] = partitionValues; + } + + for (auto i = 0; i < partitionSizes_.size(); ++i) { + if (partitionSizes_[i] != 0) { + VELOX_CHECK_NOT_NULL(partitionRows_[i]); + partitionRows_[i]->setSize(partitionSizes_[i] * sizeof(vector_size_t)); + } + } +} + +void IcebergDataSink::appendData(RowVectorPtr input) { + checkRunning(); + if (!isPartitioned() || partitionIdGenerator_->numPartitions() == 1) { + const auto index = ensureWriter(HiveWriterId::unpartitionedId()); + write(index, input); + return; + } + + // Compute partition and bucket numbers. + computePartitionAndBucketIds(input); + + splitInputRowsAndEnsureWriters(input); + + for (auto index = 0; index < writers_.size(); ++index) { + const vector_size_t partitionSize = partitionSizes_[index]; + if (partitionSize == 0) { + continue; + } + + const RowVectorPtr writerInput = partitionSize == input->size() + ? input + : exec::wrap(partitionSize, partitionRows_[index], input); + write(index, writerInput); + } +} + +HiveWriterId IcebergDataSink::getIcebergWriterId(size_t row) const { + std::optional partitionId; + if (isPartitioned()) { + VELOX_CHECK_LT(partitionIds_[row], std::numeric_limits::max()); + partitionId = static_cast(partitionIds_[row]); + } + + return HiveWriterId{partitionId, std::nullopt}; +} + +std::shared_ptr +IcebergDataSink::createWriterOptions() const { + auto options = HiveDataSink::createWriterOptions(); + options->fileStatsCollector = icebergStatsCollector_.get(); + +#ifdef VELOX_ENABLE_PARQUET + + auto parquetOptions = + std::dynamic_pointer_cast(options); + VELOX_CHECK_NOT_NULL(parquetOptions); + std::function + convertField = + [&convertField](const IcebergDataFileStatsSettings& icebergField) + -> parquet::ParquetFieldId { + parquet::ParquetFieldId parquetField; + parquetField.fieldId = icebergField.fieldId; + for (const auto& child : icebergField.children) { + parquetField.children.push_back(convertField(*child)); + } + return parquetField; + }; + + std::vector parquetFieldIds; + for (const auto& setting : *statsSettings_) { + const auto* icebergSetting = + static_cast(setting.get()); + parquetFieldIds.push_back(convertField(*icebergSetting)); + } + + parquetOptions->parquetFieldIds = + std::make_shared>(parquetFieldIds); + parquetOptions->parquetWriteTimestampTimeZone = std::nullopt; + parquetOptions->parquetWriteTimestampUnit = TimestampPrecision::kMicroseconds; + +#endif + + return options; +} + +std::optional IcebergDataSink::getPartitionName( + const HiveWriterId& id) const { + std::optional partitionName; + if (isPartitioned()) { + partitionName = + partitionIdGenerator_->partitionName(id.partitionId.value()); + } + return partitionName; +} + +void IcebergDataSink::closeInternal() { + VELOX_CHECK_NE(state_, State::kRunning); + VELOX_CHECK_NE(state_, State::kFinishing); + + common::testutil::TestValue::adjust( + "facebook::velox::connector::hive::IcebergDataSink::closeInternal", this); + + if (state_ == State::kClosed) { + for (int i = 0; i < writers_.size(); ++i) { + WRITER_NON_RECLAIMABLE_SECTION_GUARD(i); + writers_[i]->close(); + dataFileStats_.push_back(writers_[i]->dataFileStats()); + } + } else { + for (int i = 0; i < writers_.size(); ++i) { + WRITER_NON_RECLAIMABLE_SECTION_GUARD(i); + writers_[i]->abort(); + } + } +} + +std::unique_ptr +IcebergDataSink::maybeCreateBucketSortWriter( + std::unique_ptr writer) { + if (!sortWrite()) { + return writer; + } + auto sortPool = writerInfo_.back()->sortPool.get(); + VELOX_CHECK_NOT_NULL(sortPool); + auto sortBuffer = std::make_unique( + inputType_, + sortColumnIndices_, + sortCompareFlags_, + sortPool, + writerInfo_.back()->nonReclaimableSectionHolder.get(), + connectorQueryCtx_->prefixSortConfig(), + spillConfig_, + writerInfo_.back()->spillStats.get()); + return std::make_unique( + std::move(writer), + std::move(sortBuffer), + hiveConfig_->sortWriterMaxOutputRows( + connectorQueryCtx_->sessionProperties()), + hiveConfig_->sortWriterMaxOutputBytes( + connectorQueryCtx_->sessionProperties()), + sortWriterFinishTimeSliceLimitMs_); +} + +IcebergSortingColumn::IcebergSortingColumn( + const std::string& sortColumn, + const core::SortOrder& sortOrder) + : sortColumn_(sortColumn), sortOrder_(sortOrder) { + VELOX_USER_CHECK(!sortColumn_.empty(), "iceberg sort column must be set."); +} + +const std::string& IcebergSortingColumn::sortColumn() const { + return sortColumn_; +} + +const core::SortOrder& IcebergSortingColumn::sortOrder() const { + return sortOrder_; +} + +folly::dynamic IcebergSortingColumn::serialize() const { + VELOX_UNREACHABLE("Unexpected code path, implement serialize() first."); +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergDataSink.h b/velox/connectors/hive/iceberg/IcebergDataSink.h new file mode 100644 index 000000000000..1ad6a143e5b4 --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergDataSink.h @@ -0,0 +1,132 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/connectors/hive/HiveDataSink.h" +#include "velox/connectors/hive/iceberg/DataFileStatsCollector.h" +#include "velox/connectors/hive/iceberg/IcebergColumnHandle.h" +#include "velox/connectors/hive/iceberg/TransformFactory.h" +#include "velox/connectors/hive/iceberg/Transforms.h" + +namespace facebook::velox::connector::hive::iceberg { + +class IcebergSortingColumn : public ISerializable { + public: + IcebergSortingColumn( + const std::string& sortColumn, + const core::SortOrder& sortOrder); + + const std::string& sortColumn() const; + + const core::SortOrder& sortOrder() const; + + folly::dynamic serialize() const override; + + private: + const std::string sortColumn_; + const core::SortOrder sortOrder_; +}; + +// Represents a request for Iceberg write. +class IcebergInsertTableHandle final : public HiveInsertTableHandle { + public: + IcebergInsertTableHandle( + std::vector> inputColumns, + std::shared_ptr locationHandle, + std::shared_ptr partitionSpec, + memory::MemoryPool* pool, + dwio::common::FileFormat tableStorageFormat = + dwio::common::FileFormat::PARQUET, + const std::vector& sortedBy = {}, + std::optional compressionKind = {}, + const std::unordered_map& serdeParameters = {}); + + ~IcebergInsertTableHandle() = default; + + std::shared_ptr partitionSpec() const { + return partitionSpec_; + } + + const std::vector>& columnTransforms() const { + return columnTransforms_; + } + + const std::vector& sortedBy() const { + return sortedBy_; + } + + private: + const std::shared_ptr partitionSpec_; + const std::vector> columnTransforms_; + const std::vector sortedBy_; +}; + +class IcebergDataSink : public HiveDataSink { + public: + IcebergDataSink( + RowTypePtr inputType, + const std::shared_ptr& insertTableHandle, + const ConnectorQueryCtx* connectorQueryCtx, + CommitStrategy commitStrategy, + const std::shared_ptr& hiveConfig); + + void appendData(RowVectorPtr input) override; + + const std::vector>& + dataFileStats() const { + return dataFileStats_; + } + + private: + IcebergDataSink( + RowTypePtr inputType, + const std::shared_ptr& insertTableHandle, + const ConnectorQueryCtx* connectorQueryCtx, + CommitStrategy commitStrategy, + const std::shared_ptr& hiveConfig, + const std::vector& partitionChannels, + const std::vector& dataChannels); + + void splitInputRowsAndEnsureWriters(RowVectorPtr input) override; + + std::vector commitMessage() const override; + + HiveWriterId getIcebergWriterId(size_t row) const; + + std::shared_ptr createWriterOptions() + const override; + + std::optional getPartitionName( + const HiveWriterId& id) const override; + + std::unique_ptr maybeCreateBucketSortWriter( + std::unique_ptr writer) override; + + void closeInternal() override; + + // Below are structures for partitions from all inputs. partitionData_ + // is indexed by partitionId. + std::vector> partitionData_; + + std::vector> dataFileStats_; + std::shared_ptr< + std::vector>> + statsSettings_; + std::unique_ptr icebergStatsCollector_; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergDeleteFile.cpp b/velox/connectors/hive/iceberg/IcebergDeleteFile.cpp new file mode 100644 index 000000000000..4237785e8ca9 --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergDeleteFile.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/IcebergDeleteFile.h" + +namespace facebook::velox::connector::hive::iceberg { + +fmt::underlying_t format_as(FileContent f) { + return fmt::underlying(f); +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergDeleteFile.h b/velox/connectors/hive/iceberg/IcebergDeleteFile.h index 2f9206dfc264..995578e129c6 100644 --- a/velox/connectors/hive/iceberg/IcebergDeleteFile.h +++ b/velox/connectors/hive/iceberg/IcebergDeleteFile.h @@ -29,6 +29,9 @@ enum class FileContent { kEqualityDeletes, }; +/// Formatter for fmt. +fmt::underlying_t format_as(FileContent f); + struct IcebergDeleteFile { FileContent content; const std::string filePath; diff --git a/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.cpp b/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.cpp new file mode 100644 index 000000000000..613ca177a5eb --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.cpp @@ -0,0 +1,209 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h" + +#include "velox/connectors/hive/HivePartitionUtil.h" +#include "velox/connectors/hive/iceberg/Transforms.h" + +namespace facebook::velox::connector::hive::iceberg { + +namespace { + +template +std::pair makePartitionKeyValueString( + const BaseVector* partitionVector, + vector_size_t row, + const std::string& name, + const std::shared_ptr& columnTransform) { + using T = typename TypeTraits::NativeType; + if (partitionVector->as>()->isNullAt(row)) { + return std::make_pair(name, "null"); + } + + return std::make_pair( + name, + columnTransform->toHumanString( + partitionVector->as>()->valueAt(row))); +} + +// Iceberg spec requires URL encoding in the partition path. +// This function matches java.net.URLEncoder.encode(string, "UTF-8"). +std::string urlEncode(const StringView& data) { + std::ostringstream ret; + + for (unsigned char c : data) { + // These characters are not encoded in Java's URLEncoder. + if (std::isalnum(c) || c == '-' || c == '_' || c == '.' || c == '*') { + ret << c; + } else if (c == ' ') { + ret << '+'; + } else { + // All other characters are percent-encoded. + ret << fmt::format("%{:02X}", c); + } + } + + return ret.str(); +} + +} // namespace + +IcebergPartitionIdGenerator::IcebergPartitionIdGenerator( + std::vector partitionChannels, + uint32_t maxPartitions, + memory::MemoryPool* pool, + const std::vector>& columnTransforms, + bool partitionPathAsLowerCase) + : PartitionIdGenerator( + partitionChannels, + maxPartitions, + pool, + partitionPathAsLowerCase), + pool_(pool), + columnTransforms_(columnTransforms) { + VELOX_USER_CHECK_GT( + columnTransforms_.size(), 0, "columnTransforms_ cannot be null"); + std::vector partitionKeyTypes; + std::vector partitionKeyNames; + column_index_t i{0}; + for (const auto& columnTransform : columnTransforms_) { + hashers_.emplace_back( + exec::VectorHasher::create(columnTransform->resultType(), i++)); + VELOX_USER_CHECK( + exec::VectorHasher::typeKindSupportsValueIds( + columnTransform->resultType()->kind()), + "Unsupported partition type: {}.", + columnTransform->resultType()->toString()); + partitionKeyTypes.emplace_back(columnTransform->resultType()); + std::string key = + columnTransform->transformType() == TransformType::kIdentity + ? columnTransform->sourceColumnName() + : fmt::format( + "{}_{}", + columnTransform->sourceColumnName(), + columnTransform->name()); + partitionKeyNames.emplace_back(std::move(key)); + } + partitionValues_ = BaseVector::create( + ROW(std::move(partitionKeyNames), std::move(partitionKeyTypes)), + maxPartitions, + pool_); + for (auto& key : partitionValues_->children()) { + key->resize(maxPartitions); + } +} + +void IcebergPartitionIdGenerator::savePartitionValues( + uint32_t partitionId, + const RowVectorPtr& input, + vector_size_t row) { + for (auto i = 0; i < partitionChannels_.size(); ++i) { + partitionValues_->childAt(i)->copy( + input->childAt(i).get(), partitionId, row, 1); + } +} + +void IcebergPartitionIdGenerator::run( + const RowVectorPtr& input, + raw_vector& result) { + const auto numRows = input->size(); + result.resize(numRows); + std::vector columns; + std::vector names; + std::vector types; + const int32_t transformCount = columnTransforms_.size(); + columns.reserve(transformCount); + names.reserve(transformCount); + types.reserve(transformCount); + for (auto i = 0; i < transformCount; i++) { + names.emplace_back(columnTransforms_[i]->sourceColumnName()); + types.emplace_back(columnTransforms_[i]->resultType()); + columns.emplace_back( + columnTransforms_[i]->apply(input->childAt(partitionChannels_[i]))); + } + const auto rowVector = std::make_shared( + pool_, + ROW(std::move(names), std::move(types)), + nullptr, + numRows, + columns); + + // Compute value IDs using VectorHashers and store these in 'result'. + computeValueIds(rowVector, result); + + // Convert value IDs in 'result' into partition IDs using partitionIds + // mapping. Update 'result' in place. + for (auto i = 0; i < numRows; ++i) { + auto valueId = result[i]; + if (auto it = partitionIds_.find(valueId); it != partitionIds_.end()) { + result[i] = it->second; + } else { + uint64_t nextPartitionId = partitionIds_.size(); + VELOX_USER_CHECK_LT( + nextPartitionId, + maxPartitions_, + "Exceeded limit of {} distinct partitions.", + maxPartitions_); + + partitionIds_.emplace(valueId, nextPartitionId); + savePartitionValues(nextPartitionId, rowVector, i); + result[i] = nextPartitionId; + } + } +} + +std::vector> +IcebergPartitionIdGenerator::extractPartitionKeyValues( + const RowVectorPtr& partitionsVector, + vector_size_t row) const { + std::vector> partitionKeyValues; + VELOX_DCHECK_EQ( + partitionsVector->childrenSize(), + columnTransforms_.size(), + "Partition values and partition transform does not match."); + for (auto i = 0; i < partitionsVector->childrenSize(); i++) { + partitionKeyValues.push_back(VELOX_DYNAMIC_SCALAR_TYPE_DISPATCH( + makePartitionKeyValueString, + partitionsVector->childAt(i)->typeKind(), + partitionsVector->childAt(i)->loadedVector(), + row, + asRowType(partitionsVector->type())->nameOf(i), + columnTransforms_[i])); + } + return partitionKeyValues; +} + +std::string IcebergPartitionIdGenerator::partitionName( + uint64_t partitionId) const { + auto keyValues = extractPartitionKeyValues(partitionValues_, partitionId); + std::ostringstream ret; + + for (auto& [key, value] : keyValues) { + if (ret.tellp() > 0) { + ret << '/'; + } + + if (partitionPathAsLowerCase_) { + folly::toLowerAscii(key); + } + ret << urlEncode(key.data()) << '=' << urlEncode(value.data()); + } + + return ret.str(); +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h b/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h new file mode 100644 index 000000000000..1f070bd6866e --- /dev/null +++ b/velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/connectors/hive/PartitionIdGenerator.h" +#include "velox/connectors/hive/iceberg/IcebergDataSink.h" + +namespace facebook::velox::connector::hive::iceberg { +class IcebergPartitionIdGenerator : public PartitionIdGenerator { + public: + IcebergPartitionIdGenerator( + std::vector partitionChannels, + uint32_t maxPartitions, + memory::MemoryPool* pool, + const std::vector>& columnTransforms, + bool partitionPathAsLowerCase); + + /// Generate sequential partition IDs for input vector. + /// @param input Input RowVector. + /// @param result Generated integer IDs indexed by input row number. + void run(const RowVectorPtr& input, raw_vector& result) override; + + /// Return partition name for the given partition id in the typical Hive + /// style. It is derived from the partitionValues_ at index partitionId. + /// Partition keys appear in the order of partition columns in the table + /// schema. + std::string partitionName(uint64_t partitionId) const override; + + /// Return the partition values for all partitions. + RowVectorPtr partitionValues() const { + return partitionValues_; + } + + private: + void savePartitionValues( + uint32_t partitionId, + const RowVectorPtr& input, + vector_size_t row) override; + + std::vector> extractPartitionKeyValues( + const RowVectorPtr& partitionsVector, + vector_size_t row) const; + + memory::MemoryPool* pool_; + const std::vector> columnTransforms_; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergSplitReader.cpp b/velox/connectors/hive/iceberg/IcebergSplitReader.cpp index 63e19d16a71f..1e8467959208 100644 --- a/velox/connectors/hive/iceberg/IcebergSplitReader.cpp +++ b/velox/connectors/hive/iceberg/IcebergSplitReader.cpp @@ -16,6 +16,7 @@ #include "velox/connectors/hive/iceberg/IcebergSplitReader.h" +#include "velox/connectors/hive/iceberg/EqualityDeleteFileReader.h" #include "velox/connectors/hive/iceberg/IcebergDeleteFile.h" #include "velox/connectors/hive/iceberg/IcebergSplit.h" #include "velox/dwio/common/BufferUtil.h" @@ -35,7 +36,9 @@ IcebergSplitReader::IcebergSplitReader( const std::shared_ptr& fsStats, FileHandleFactory* const fileHandleFactory, folly::Executor* executor, - const std::shared_ptr& scanSpec) + const std::shared_ptr& scanSpec, + core::ExpressionEvaluator* expressionEvaluator, + std::atomic& totalRemainingFilterTime) : SplitReader( hiveSplit, hiveTableHandle, @@ -50,7 +53,12 @@ IcebergSplitReader::IcebergSplitReader( scanSpec), baseReadOffset_(0), splitOffset_(0), - deleteBitmap_(nullptr) {} + deleteBitmap_(nullptr), + deleteExprSet_(nullptr), + expressionEvaluator_(expressionEvaluator), + totalRemainingFilterMs_(totalRemainingFilterTime) {} + +IcebergSplitReader::~IcebergSplitReader() {} void IcebergSplitReader::prepareSplit( std::shared_ptr metadataFilter, @@ -61,20 +69,81 @@ void IcebergSplitReader::prepareSplit( } auto rowType = getAdaptedRowType(); + std::shared_ptr icebergSplit = + std::dynamic_pointer_cast(hiveSplit_); + const auto& deleteFiles = icebergSplit->deleteFiles; + std::unordered_set equalityFieldIds; + for (const auto& deleteFile : deleteFiles) { + if (deleteFile.content == FileContent::kEqualityDeletes && + deleteFile.recordCount > 0) { + equalityFieldIds.insert( + deleteFile.equalityFieldIds.begin(), + deleteFile.equalityFieldIds.end()); + } + } + + // checkIfSplitIsEmpty needs to use the base reader's schemaWithId_. For that + // we need to update the base RowReader to include these extra fields from the + // equality delete file first, so that the schemaWithId_ of the base file is + // updated when we call baseFileSchema() later. + baseReader_->setRequiredExtraFieldIds(equalityFieldIds); + if (checkIfSplitIsEmpty(runtimeStats)) { VELOX_CHECK(emptySplit_); return; } + // Process the equality delete files to update the scan spec and remaining + // filters. It needs to be done after creating the Reader and before creating + // the RowReader. + + SubfieldFilters subfieldFilters; + std::vector conjunctInputs; + + for (const auto& deleteFile : deleteFiles) { + if (deleteFile.content == FileContent::kEqualityDeletes && + deleteFile.recordCount > 0) { + // TODO: build cache of to avoid repeating file + // parsing across partitions. Within a single partition, the splits should + // be with the same equality delete files and only need to be parsed once. + auto equalityDeleteReader = std::make_unique( + deleteFile, + baseFileSchema(), + fileHandleFactory_, + executor_, + connectorQueryCtx_, + hiveConfig_, + ioStats_, + fsStats_, + hiveSplit_->connectorId); + equalityDeleteReader->readDeleteValues(subfieldFilters, conjunctInputs); + } + } + + if (!subfieldFilters.empty()) { + for (const auto& [key, filter] : subfieldFilters) { + auto childSpec = scanSpec_->getOrCreateChild(key, true); + childSpec->addFilter(*filter); + childSpec->setHasTempFilter(true); + childSpec->setSubscript(scanSpec_->children().size() - 1); + } + } + + if (!conjunctInputs.empty()) { + core::TypedExprPtr expression = + std::make_shared(BOOLEAN(), conjunctInputs, "and"); + deleteExprSet_ = expressionEvaluator_->compile(expression); + VELOX_CHECK_EQ(deleteExprSet_->size(), 1); + } + createRowReader(std::move(metadataFilter), std::move(rowType)); - std::shared_ptr icebergSplit = - std::dynamic_pointer_cast(hiveSplit_); baseReadOffset_ = 0; splitOffset_ = baseRowReader_->nextRowNumber(); - positionalDeleteFileReaders_.clear(); - const auto& deleteFiles = icebergSplit->deleteFiles; + // Create the positional deletes file readers. They need to be created after + // the RowReader is created. + positionalDeleteFileReaders_.clear(); for (const auto& deleteFile : deleteFiles) { if (deleteFile.content == FileContent::kPositionalDeletes) { if (deleteFile.recordCount > 0) { @@ -92,12 +161,16 @@ void IcebergSplitReader::prepareSplit( splitOffset_, hiveSplit_->connectorId)); } - } else { - VELOX_NYI(); } } } +std::shared_ptr +IcebergSplitReader::baseFileSchema() { + VELOX_CHECK_NOT_NULL(baseReader_.get()); + return baseReader_->typeWithId(); +} + uint64_t IcebergSplitReader::next(uint64_t size, VectorPtr& output) { Mutation mutation; mutation.randomSkip = baseReaderOpts_.randomSkip().get(); @@ -136,9 +209,75 @@ uint64_t IcebergSplitReader::next(uint64_t size, VectorPtr& output) { : nullptr; auto rowsScanned = baseRowReader_->next(actualSize, output, &mutation); + + // Evaluate the remaining filter deleteExprSet_ for every batch and update the + // output vector if it reduces any rows. + if (deleteExprSet_) { + auto filterStartMs = getCurrentTimeMs(); + + filterRows_.resize(output->size()); + auto rowVector = std::dynamic_pointer_cast(output); + expressionEvaluator_->evaluate( + deleteExprSet_.get(), filterRows_, *rowVector, filterResult_); + auto numRemainingRows = exec::processFilterResults( + filterResult_, filterRows_, filterEvalCtx_, pool_); + + if (numRemainingRows < output->size()) { + output = exec::wrap( + numRemainingRows, filterEvalCtx_.selectedIndices, rowVector); + } + + totalRemainingFilterMs_.fetch_add( + (getCurrentTimeMs() - filterStartMs), std::memory_order_relaxed); + } + baseReadOffset_ += rowsScanned; + if (rowsScanned == 0) { + scanSpec_->deleteTempNodes(); + } + return rowsScanned; } +std::vector IcebergSplitReader::adaptColumns( + const RowTypePtr& fileType, + const std::shared_ptr& tableSchema) const { + // Keep track of schema types for columns in file, used by ColumnSelector. + std::vector columnTypes = fileType->children(); + auto& childrenSpecs = scanSpec_->children(); + // Iceberg table stores all column's data in data file. + for (size_t i = 0; i < childrenSpecs.size(); ++i) { + auto* childSpec = childrenSpecs[i].get(); + const std::string& fieldName = childSpec->fieldName(); + auto fileTypeIdx = fileType->getChildIdxIfExists(fieldName); + auto outputTypeIdx = readerOutputType_->getChildIdxIfExists(fieldName); + if (outputTypeIdx.has_value() && fileTypeIdx.has_value()) { + childSpec->setConstantValue(nullptr); + auto& outputType = readerOutputType_->childAt(*outputTypeIdx); + columnTypes[*fileTypeIdx] = outputType; + } else if (!fileTypeIdx.has_value()) { + // Handle columns missing from the data file in two scenarios: + // 1. Schema evolution: Column was added after the data file was written + // and doesn't exist in older data files. + // 2. Partition columns: In Hive-written data files, partition column + // values are stored in partition metadata rather than in the data + // file itself, following Hive's partitioning convention. + if (auto it = hiveSplit_->partitionKeys.find(fieldName); + it != hiveSplit_->partitionKeys.end()) { + setPartitionValue(childSpec, fieldName, it->second); + } else { + childSpec->setConstantValue(BaseVector::createNullConstant( + tableSchema->findChild(fieldName), + 1, + connectorQueryCtx_->memoryPool())); + } + } + } + + scanSpec_->resetCachedValues(false); + + return columnTypes; +} + } // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/IcebergSplitReader.h b/velox/connectors/hive/iceberg/IcebergSplitReader.h index 4b3c6b901048..7242c7c591bc 100644 --- a/velox/connectors/hive/iceberg/IcebergSplitReader.h +++ b/velox/connectors/hive/iceberg/IcebergSplitReader.h @@ -19,6 +19,7 @@ #include "velox/connectors/Connector.h" #include "velox/connectors/hive/SplitReader.h" #include "velox/connectors/hive/iceberg/PositionalDeleteFileReader.h" +#include "velox/exec/OperatorUtils.h" namespace facebook::velox::connector::hive::iceberg { @@ -37,9 +38,11 @@ class IcebergSplitReader : public SplitReader { const std::shared_ptr& fsStats, FileHandleFactory* fileHandleFactory, folly::Executor* executor, - const std::shared_ptr& scanSpec); + const std::shared_ptr& scanSpec, + core::ExpressionEvaluator* expressionEvaluator, + std::atomic& totalRemainingFilterTime); - ~IcebergSplitReader() override = default; + ~IcebergSplitReader() override; void prepareSplit( std::shared_ptr metadataFilter, @@ -47,6 +50,12 @@ class IcebergSplitReader : public SplitReader { uint64_t next(uint64_t size, VectorPtr& output) override; + std::vector adaptColumns( + const RowTypePtr& fileType, + const std::shared_ptr& tableSchema) const override; + + std::shared_ptr baseFileSchema(); + private: // The read offset to the beginning of the split in number of rows for the // current batch for the base data file @@ -56,5 +65,14 @@ class IcebergSplitReader : public SplitReader { std::list> positionalDeleteFileReaders_; BufferPtr deleteBitmap_; + + std::unique_ptr deleteExprSet_; + core::ExpressionEvaluator* expressionEvaluator_; + std::atomic& totalRemainingFilterMs_; + + // Reusable memory for remaining filter evaluation. + VectorPtr filterResult_; + SelectivityVector filterRows_; + exec::FilterEvalCtx filterEvalCtx_; }; } // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/Murmur3.cpp b/velox/connectors/hive/iceberg/Murmur3.cpp new file mode 100644 index 000000000000..a6558367b4fa --- /dev/null +++ b/velox/connectors/hive/iceberg/Murmur3.cpp @@ -0,0 +1,82 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/Murmur3.h" +#include "velox/type/DecimalUtil.h" +#include "velox/type/HugeInt.h" + +namespace facebook::velox::connector::hive::iceberg { + +int32_t Murmur3Hash32::hash(const char* const input, size_t len) { + uint32_t h1{kDefaultSeed}; + uint32_t k1{0}; + const uint8_t* data = reinterpret_cast(input); + const size_t nblocks = len / 4; + + // Body. + for (size_t i = 0; i < nblocks; i++) { + uint32_t k1 = *reinterpret_cast(data + i * 4); + k1 = mixK1(k1); + h1 = mixH1(h1, k1); + } + + k1 = 0; + data = data + nblocks * 4; + + // Tail. + switch (len & 3) { + case 3: + k1 ^= (static_cast(data[2])) << 16; + [[fallthrough]]; + case 2: + k1 ^= (static_cast(data[1])) << 8; + [[fallthrough]]; + case 1: + k1 ^= data[0]; + k1 = mixK1(k1); + h1 ^= k1; + }; + + // Finalization. + return fmix32(h1, len); +} + +int32_t Murmur3Hash32::hash(const StringView& value) { + return hash(value.data(), value.size()); +} + +int32_t Murmur3Hash32::hash(uint64_t value) { + auto h1 = kDefaultSeed; + const auto low = static_cast(value & 0xFFFFFFFF); + const auto high = static_cast((value >> 32) & 0xFFFFFFFF); + + auto k1 = mixK1(low); + h1 = mixH1(h1, k1); + + k1 = mixK1(high); + h1 = mixH1(h1, k1); + + return fmix32(h1, sizeof(uint64_t)); +} + +int32_t Murmur3Hash32::hashDecimal(int128_t value) { + char bytes[16]; + const auto length = DecimalUtil::getByteArrayLength(value); + DecimalUtil::toByteArray(value, bytes); + return hash(bytes, length); +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/Murmur3.h b/velox/connectors/hive/iceberg/Murmur3.h new file mode 100644 index 000000000000..7b0d3fff81ea --- /dev/null +++ b/velox/connectors/hive/iceberg/Murmur3.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/type/HugeInt.h" +#include "velox/type/StringView.h" + +namespace facebook::velox::connector::hive::iceberg { +constexpr uint32_t kDefaultSeed = 0; +constexpr uint32_t kC1 = 0xCC9E2D51; +constexpr uint32_t kC2 = 0x1B873593; + +class Murmur3Hash32 final { + public: + static int32_t hash(uint64_t value); + + static int32_t hash(const StringView& value); + + static int32_t hash(const char* const data, size_t length); + + static int32_t hashDecimal(int128_t value); + + private: + FOLLY_ALWAYS_INLINE static uint32_t mixK1(uint32_t k1) { + k1 *= kC1; + k1 = ((k1) << (15)) | ((k1) >> (32 - (15))); + k1 *= kC2; + return k1; + } + + FOLLY_ALWAYS_INLINE static uint32_t mixH1(uint32_t h1, uint32_t k1) { + h1 ^= k1; + h1 = ((h1) << (13)) | ((h1) >> (32 - (13))); + h1 = h1 * 5 + 0xE6546B64; + return h1; + } + + FOLLY_ALWAYS_INLINE static uint32_t fmix32(uint32_t h1, size_t length) { + h1 ^= length; + h1 ^= h1 >> 16; + h1 *= 0x85EBCA6B; + h1 ^= h1 >> 13; + h1 *= 0xC2B2AE35; + h1 ^= h1 >> 16; + return h1; + } +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/PartitionSpec.cpp b/velox/connectors/hive/iceberg/PartitionSpec.cpp new file mode 100644 index 000000000000..d7c50e8096b8 --- /dev/null +++ b/velox/connectors/hive/iceberg/PartitionSpec.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/PartitionSpec.h" + +namespace facebook::velox::connector::hive::iceberg { + +namespace { + +folly::F14FastMap transformTypeNames() { + return { + {TransformType::kIdentity, "identity"}, + {TransformType::kHour, "hour"}, + {TransformType::kDay, "day"}, + {TransformType::kMonth, "month"}, + {TransformType::kYear, "year"}, + {TransformType::kBucket, "bucket"}, + {TransformType::kTruncate, "trunc"}}; +} + +} // namespace + +VELOX_DEFINE_ENUM_NAME(TransformType, transformTypeNames); + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/PartitionSpec.h b/velox/connectors/hive/iceberg/PartitionSpec.h new file mode 100644 index 000000000000..915538c06b66 --- /dev/null +++ b/velox/connectors/hive/iceberg/PartitionSpec.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/common/Enums.h" +#include "velox/type/Type.h" + +namespace facebook::velox::connector::hive::iceberg { + +enum class TransformType { + kIdentity, + kHour, + kDay, + kMonth, + kYear, + kBucket, + kTruncate +}; + +VELOX_DECLARE_ENUM_NAME(TransformType); + +struct IcebergPartitionSpec { + struct Field { + // The field name of this partition field as it appears in the partition + // spec. This is the original Iceberg field name, not the transformed name + // from org.apache.iceberg.PartitionField which includes the transform as a + // suffix. + std::string name; + + // The source column type. + TypePtr type; + + // The transform type applied to the source field (e.g., kIdentity, kBucket, + // kTruncate, etc.). + TransformType transformType; + + // Optional parameter for transforms that require configuration + // (e.g., bucket count or truncate width). + std::optional parameter; + }; + + const int32_t specId; + const std::vector fields; + + IcebergPartitionSpec(int32_t _specId, const std::vector& _fields) + : specId(_specId), fields(_fields) {} +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/TransformFactory.cpp b/velox/connectors/hive/iceberg/TransformFactory.cpp new file mode 100644 index 000000000000..baef9e5e93fc --- /dev/null +++ b/velox/connectors/hive/iceberg/TransformFactory.cpp @@ -0,0 +1,282 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/TransformFactory.h" + +#include "velox/functions/lib/TimeUtils.h" +#include "velox/functions/prestosql/types/TimestampWithTimeZoneType.h" +#include "velox/vector/ComplexVector.h" + +namespace facebook::velox::connector::hive::iceberg { +namespace { +int32_t epochYear(int32_t daysSinceEpoch) { + const std::tm tm = functions::getDateTime(daysSinceEpoch); + // tm_year is the number of years since 1900. + return tm.tm_year + 1900 - 1970; +} + +int32_t epochYear(Timestamp ts) { + return functions::getYear(functions::getDateTime(ts, nullptr)) - 1970; +} + +int32_t epochMonth(int32_t daysSinceEpoch) { + const std::tm tm = functions::getDateTime(daysSinceEpoch); + return (tm.tm_year + 1900 - 1970) * 12 + tm.tm_mon; +} + +int32_t epochMonth(Timestamp ts) { + const std::tm tm = functions::getDateTime(ts, nullptr); + return (tm.tm_year + 1900 - 1970) * 12 + tm.tm_mon; +} + +int32_t epochDay(int32_t daysSinceEpoch) { + return daysSinceEpoch; +} + +int32_t epochDay(Timestamp ts) { + const auto seconds = ts.getSeconds(); + return (seconds >= 0) ? seconds / Timestamp::kSecondsInDay + : ((seconds + 1) / Timestamp::kSecondsInDay) - 1; +} + +int32_t epochHour(Timestamp ts) { + const auto seconds = ts.getSeconds(); + return (seconds >= 0) ? seconds / 3600 : ((seconds + 1) / 3600) - 1; +} + +bool isValidPartitionType(TypePtr type) { + if (type->isRow() || type->isArray() || type->isMap() || + isTimestampWithTimeZoneType(type)) { + return false; + } + return true; +} + +template +std::shared_ptr createDateTimeTransform( + TransformType transformType, + const IcebergPartitionSpec::Field& field, + std::function::NativeType)> epochFunc, + memory::MemoryPool* pool) { + using NativeType = typename TypeTraits::NativeType; + VELOX_DCHECK_EQ( + true, + field.type->isDate() || field.type->isTimestamp(), + "Unsupported column type {} for transform {}", + field.type->name(), + TransformTypeName::toName(transformType)); + return std::make_shared>( + field.type, transformType, field.name, pool, epochFunc); +} + +template +std::shared_ptr createIdentityTransform( + const IcebergPartitionSpec::Field& field, + memory::MemoryPool* pool) { + using NativeType = typename TypeTraits::NativeType; + return std::make_shared>( + field.type, field.name, pool); +} + +template +std::shared_ptr createBucketTransform( + const IcebergPartitionSpec::Field& field, + int32_t count, + memory::MemoryPool* pool) { + VELOX_USER_CHECK_GT(count, 0, "Bucket count must be positive."); + using NativeType = typename TypeTraits::NativeType; + return std::make_shared>( + count, field.type, field.name, pool); +} + +template +std::shared_ptr createTruncateTransform( + const IcebergPartitionSpec::Field& field, + int32_t width, + memory::MemoryPool* pool) { + VELOX_USER_CHECK_GT(width, 0, "Truncate width must be positive."); + using NativeType = typename TypeTraits::NativeType; + return std::make_shared>( + width, field.type, field.name, pool); +} + +std::shared_ptr buildColumnTransform( + const IcebergPartitionSpec::Field& field, + memory::MemoryPool* pool) { + if (!isValidPartitionType(field.type)) { + VELOX_USER_FAIL(fmt::format( + "Type not supported as partition column: {}.", field.type->name())); + } + switch (field.transformType) { + // Identity transform. + case TransformType::kIdentity: { + return VELOX_DYNAMIC_SCALAR_TYPE_DISPATCH( + createIdentityTransform, field.type->kind(), field, pool); + } + // Year transform. + case TransformType::kYear: { + if (field.type->isDate()) { + return createDateTimeTransform( + TransformType::kYear, + field, + [](int32_t v) { return epochYear(v); }, + pool); + } + + if (field.type->isTimestamp()) { + return createDateTimeTransform( + TransformType::kYear, + field, + [](Timestamp v) { return epochYear(v); }, + pool); + } + + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform year.", + field.type->name())); + } + // Month transform. + case TransformType::kMonth: { + if (field.type->isDate()) { + return createDateTimeTransform( + TransformType::kMonth, + field, + [](int32_t v) { return epochMonth(v); }, + pool); + } + + if (field.type->isTimestamp()) { + return createDateTimeTransform( + TransformType::kMonth, + field, + [](Timestamp v) { return epochMonth(v); }, + pool); + } + + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform month.", + field.type->name())); + } + // Day transform. + case TransformType::kDay: { + if (field.type->isDate()) { + return createDateTimeTransform( + TransformType::kDay, + field, + [](int32_t v) { return epochDay(v); }, + pool); + } + + if (field.type->isTimestamp()) { + return createDateTimeTransform( + TransformType::kDay, + field, + [](Timestamp v) { return epochDay(v); }, + pool); + } + + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform day.", field.type->name())); + } + // Hour transform. + case TransformType::kHour: { + if (field.type->isTimestamp()) { + return createDateTimeTransform( + TransformType::kHour, + field, + [](Timestamp v) { return epochHour(v); }, + pool); + } + + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform hour.", + field.type->name())); + } + // Bucket transform. + case TransformType::kBucket: { + VELOX_USER_CHECK( + field.parameter.has_value() && field.parameter.value() > 0, + "Bucket transform requires a positive parameter."); + auto numBuckets = field.parameter.value(); + + if (field.type->isInteger() || field.type->isDate()) { + return createBucketTransform( + field, numBuckets, pool); + } + if (field.type->isBigint() || field.type->isShortDecimal()) { + return createBucketTransform(field, numBuckets, pool); + } + if (field.type->isTimestamp()) { + return createBucketTransform( + field, numBuckets, pool); + } + if (field.type->isLongDecimal()) { + return createBucketTransform( + field, numBuckets, pool); + } + if (field.type->isVarchar()) { + return createBucketTransform( + field, numBuckets, pool); + } + if (field.type->isVarbinary()) { + return createBucketTransform( + field, numBuckets, pool); + } + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform bucket.", + field.type->name())); + } + // Truncate transform. + case TransformType::kTruncate: { + VELOX_USER_CHECK( + field.parameter.has_value() && field.parameter.value() > 0, + "Truncate transform requires a positive parameter."); + auto width = field.parameter.value(); + if (field.type->isInteger()) { + return createTruncateTransform(field, width, pool); + } + if (field.type->isBigint() || field.type->isShortDecimal()) { + return createTruncateTransform(field, width, pool); + } + if (field.type->isVarchar()) { + return createTruncateTransform(field, width, pool); + } + if (field.type->isVarbinary()) { + return createTruncateTransform(field, width, pool); + } + VELOX_UNREACHABLE(fmt::format( + "Unsupported column type {} for transform truncate.", + field.type->name())); + } + default: + VELOX_UNREACHABLE("Unsupported transform."); + } +} + +} // namespace + +std::vector> parsePartitionTransformSpecs( + const std::vector& fields, + memory::MemoryPool* pool) { + std::vector> transforms; + transforms.reserve(fields.size()); + for (auto& field : fields) { + transforms.emplace_back(buildColumnTransform(field, pool)); + } + return transforms; +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/TransformFactory.h b/velox/connectors/hive/iceberg/TransformFactory.h new file mode 100644 index 000000000000..ea6642db7dc1 --- /dev/null +++ b/velox/connectors/hive/iceberg/TransformFactory.h @@ -0,0 +1,27 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include "velox/connectors/hive/iceberg/PartitionSpec.h" +#include "velox/connectors/hive/iceberg/Transforms.h" + +namespace facebook::velox::connector::hive::iceberg { + +std::vector> parsePartitionTransformSpecs( + const std::vector& fields, + memory::MemoryPool* pool); + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/Transforms.cpp b/velox/connectors/hive/iceberg/Transforms.cpp new file mode 100644 index 000000000000..a400270c58d7 --- /dev/null +++ b/velox/connectors/hive/iceberg/Transforms.cpp @@ -0,0 +1,226 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/Transforms.h" + +#include "velox/connectors/hive/iceberg/Murmur3.h" +#include "velox/functions/lib/string/StringImpl.h" +#include "velox/vector/DecodedVector.h" +#include "velox/vector/FlatVector.h" + +namespace facebook::velox::connector::hive::iceberg { + +namespace { + +template +FOLLY_ALWAYS_INLINE void transformValues( + const VectorPtr& block, + const DecodedVector* decoded, + const VectorPtr& result, + ProcessFunc&& processValue) { + if (!decoded->mayHaveNulls()) { + for (auto i = 0; i < decoded->size(); ++i) { + processValue(i); + } + } else { + block->mutableNulls(block->size()); + result->setNulls(block->nulls()); + for (auto i = 0; i < decoded->size(); ++i) { + if (!decoded->isNullAt(i)) { + processValue(i); + } + } + } +} + +} // namespace + +VectorPtr Transform::transform( + const RowVectorPtr& input, + std::optional channel) const { + VectorPtr currentVector = nullptr; + if (channel.has_value()) { + currentVector = input->childAt(channel.value()); + } + VELOX_CHECK_NOT_NULL(currentVector); + return apply(currentVector); +} + +std::string Transform::toHumanString(Timestamp value) const { + TimestampToStringOptions options; + options.precision = TimestampPrecision::kMilliseconds; + options.zeroPaddingYear = true; + options.skipTrailingZeros = true; + options.leadingPositiveSign = true; + options.skipTrailingZeroSeconds = true; + return value.toString(options); +} + +template +VectorPtr IdentityTransform::apply(const VectorPtr& block) const { + if constexpr (!std::is_same_v) { + return block; + } + if (sourceType_->isVarchar()) { + return block; + } + + auto result = + BaseVector::create>(sourceType_, block->size(), pool_); + DecodedVector decoded(*block); + + auto processValue = [&](auto i) { + if constexpr (std::is_same_v) { + T value = decoded.valueAt(i); + auto encodedValue = encoding::Base64::encode(value.data(), value.size()); + result->set(i, StringView(encodedValue)); + } + }; + + transformValues(block, &decoded, result, processValue); + return result; +} + +template +VectorPtr BucketTransform::apply(const VectorPtr& block) const { + auto result = + BaseVector::create>(INTEGER(), block->size(), pool_); + + DecodedVector decoded(*block); + + auto processValue = [&](auto i) { + T value = decoded.valueAt(i); + int32_t hashValue; + if constexpr (std::is_same_v || std::is_same_v) { + if (sourceType_->isDecimal()) { + hashValue = Murmur3Hash32::hashDecimal(value); + } else { + hashValue = Murmur3Hash32::hash(value); + } + } else if constexpr (std::is_same_v) { + hashValue = Murmur3Hash32::hash(value.toMicros()); + } else { + hashValue = Murmur3Hash32::hash(value); + } + result->set(i, (hashValue & 0x7FFFFFFF) % numBuckets_); + }; + + transformValues(block, &decoded, result, processValue); + return result; +} + +template +VectorPtr TruncateTransform::apply(const VectorPtr& block) const { + auto result = + BaseVector::create>(sourceType_, block->size(), pool_); + + auto flatResult = result->template as>(); + char* rawBuffer = nullptr; + BufferPtr buffer; + if (std::is_same_v) { + if (sourceType_->isVarchar()) { + buffer = result->getBufferWithSpace(block->size() * width_); + } else { + buffer = result->getBufferWithSpace( + block->size() * encoding::Base64::calculateEncodedSize(width_)); + } + rawBuffer = buffer->asMutable() + buffer->size(); + } + + DecodedVector decoded(*block); + auto processValue = [&](auto i) { + T value = decoded.valueAt(i); + if constexpr ( + std::is_same_v || std::is_same_v || + std::is_same_v) { + flatResult->set(i, value - ((value % width_) + width_) % width_); + } else if constexpr (std::is_same_v) { + if (sourceType_->isVarchar()) { + auto length = + functions::stringImpl::cappedByteLength(value, width_); + if (StringView::isInline(length)) { + flatResult->set(i, StringView(value.data(), length)); + } else { + memcpy(rawBuffer, value.data(), length); + flatResult->setNoCopy(i, StringView(rawBuffer, length)); + rawBuffer += length; + } + } else if (sourceType_->isVarbinary()) { + auto encoded = encoding::Base64::encode( + value.data(), width_ > value.size() ? value.size() : width_); + auto length = encoded.length(); + if (StringView::isInline(length)) { + flatResult->set(i, StringView(encoded)); + } else { + memcpy(rawBuffer, encoded.data(), length); + flatResult->setNoCopy(i, StringView(rawBuffer, length)); + rawBuffer += length; + } + } + } + }; + + transformValues(block, &decoded, result, processValue); + + if constexpr (std::is_same_v) { + buffer->setSize(rawBuffer - (buffer->asMutable() + buffer->size())); + } + return result; +} + +template +VectorPtr TemporalTransform::apply(const VectorPtr& block) const { + auto result = + BaseVector::create>(INTEGER(), block->size(), pool_); + + DecodedVector decoded(*block); + auto processValue = [&](auto i) { + T value = decoded.valueAt(i); + result->set(i, epochFunc_(value)); + }; + + transformValues(block, &decoded, result, processValue); + + return result; +} + +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; +template class IdentityTransform; + +template class BucketTransform; +template class BucketTransform; +template class BucketTransform; +template class BucketTransform; +template class BucketTransform; + +template class TruncateTransform; +template class TruncateTransform; +template class TruncateTransform; +template class TruncateTransform; + +template class TemporalTransform; +template class TemporalTransform; +template class TemporalTransform; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/Transforms.h b/velox/connectors/hive/iceberg/Transforms.h new file mode 100644 index 000000000000..698c0afcbc54 --- /dev/null +++ b/velox/connectors/hive/iceberg/Transforms.h @@ -0,0 +1,241 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include "velox/common/encode/Base64.h" +#include "velox/connectors/hive/iceberg/PartitionSpec.h" +#include "velox/type/DecimalUtil.h" +#include "velox/type/Type.h" +#include "velox/vector/ComplexVector.h" + +namespace facebook::velox::connector::hive::iceberg { + +static constexpr int32_t kEpochYear = 1970; + +class Transform { + public: + Transform( + TypePtr type, + TransformType transformType, + const std::string& columnName, + memory::MemoryPool* pool) + : sourceType_(type), + transformType_(transformType), + sourceColumnName_(columnName), + pool_(pool) {} + + virtual ~Transform() = default; + + virtual VectorPtr apply(const VectorPtr& block) const = 0; + + virtual const TypePtr resultType() const = 0; + + TransformType transformType() const { + return transformType_; + } + + const std::string& sourceColumnName() const { + return sourceColumnName_; + } + + // Convert the transformed value to partition name + template + std::string toHumanString(T value) const { + return folly::to(value); + } + + virtual std::string toHumanString(int32_t value) const { + return folly::to(value); + } + + std::string toHumanString(int64_t value) const { + if (sourceType_->isShortDecimal()) { + return decimalToHumanString(value); + } + return folly::to(value); + } + + std::string toHumanString(int128_t value) const { + return decimalToHumanString(value); + } + + std::string toHumanString(bool value) const { + return value ? "true" : "false"; + } + + // Match Iceberg spec Java implementation + // DateTimeFormatter.ISO_LOCAL_DATE_TIME + std::string toHumanString(Timestamp value) const; + + std::string_view name() const { + return TransformTypeName::toName(transformType_); + } + + /// Applies the transform to the specified column in the input row vector. + /// @param input The input row vector containing the column to transform. + /// @param channel Optional column index (0-based) to transform. If not + /// provided, uses sourceColumnName_ to locate the column in the input. + /// @return The transformed vector. + [[nodiscard]] VectorPtr transform( + const RowVectorPtr& input, + std::optional channel = std::nullopt) const; + + private: + template + std::string decimalToHumanString(T value) const { + const auto [p, s] = getDecimalPrecisionScale(*sourceType_); + const auto maxSize = DecimalUtil::maxStringViewSize(p, s); + std::string buffer(maxSize, '\0'); + const auto actualSize = + DecimalUtil::castToString(value, s, maxSize, buffer.data()); + buffer.resize(actualSize); + return buffer; + } + + protected: + const TypePtr sourceType_; + const TransformType transformType_; + const std::string sourceColumnName_; + memory::MemoryPool* pool_; +}; + +template +class IdentityTransform final : public Transform { + public: + IdentityTransform( + const TypePtr& type, + const std::string& columnName, + memory::MemoryPool* pool) + : Transform(type, TransformType::kIdentity, columnName, pool) {} + + VectorPtr apply(const VectorPtr& block) const override; + + const TypePtr resultType() const override { + return sourceType_; + } + + std::string toHumanString(int32_t value) const override { + if (sourceType_->isDate()) { + return DATE()->toString(value); + } + return folly::to(value); + } +}; + +template +class BucketTransform final : public Transform { + public: + BucketTransform( + int32_t count, + const TypePtr& type, + const std::string& columnName, + memory::MemoryPool* pool) + : Transform(type, TransformType::kBucket, columnName, pool), + numBuckets_(count) {} + + VectorPtr apply(const VectorPtr& block) const override; + + const TypePtr resultType() const override { + return INTEGER(); + } + + private: + const int32_t numBuckets_; +}; + +template +class TruncateTransform final : public Transform { + public: + TruncateTransform( + int32_t width, + const TypePtr& type, + const std::string& columnName, + memory::MemoryPool* pool) + : Transform(type, TransformType::kTruncate, columnName, pool), + width_(width) {} + + VectorPtr apply(const VectorPtr& block) const override; + + const TypePtr resultType() const override { + return sourceType_; + } + + private: + const int32_t width_; +}; + +template +class TemporalTransform final : public Transform { + public: + TemporalTransform( + const TypePtr& type, + TransformType transformType, + const std::string& columnName, + memory::MemoryPool* pool, + const std::function& epochFunc) + : Transform(type, transformType, columnName, pool), + epochFunc_(epochFunc) {} + + VectorPtr apply(const VectorPtr& block) const override; + + const TypePtr resultType() const override { + return INTEGER(); + } + + std::string toHumanString(int32_t value) const override { + switch (transformType_) { + case TransformType::kYear: { + return fmt::format("{:04d}", kEpochYear + value); + } + case TransformType::kMonth: { + int32_t year = kEpochYear + value / 12; + int32_t month = 1 + value % 12; + if (month <= 0) { + month += 12; + year -= 1; + } + return fmt::format("{:04d}-{:02d}", year, month); + } + case TransformType::kHour: { + int64_t seconds = static_cast(value) * 3600; + std::tm tmValue; + VELOX_USER_CHECK( + Timestamp::epochToCalendarUtc(seconds, tmValue), + "Can't convert seconds {}*3600 to time.", + seconds); + + return fmt::format( + "{:04d}-{:02d}-{:02d}-{:02d}", + tmValue.tm_year + 1900, + tmValue.tm_mon + 1, + tmValue.tm_mday, + tmValue.tm_hour); + } + case TransformType::kDay: { + return DATE()->toString(value); + } + default: { + VELOX_UNREACHABLE("Unsupported transform type."); + } + } + } + + private: + const std::function epochFunc_; +}; + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/tests/CMakeLists.txt b/velox/connectors/hive/iceberg/tests/CMakeLists.txt index 51d1116d1845..54c8c36f4d6f 100644 --- a/velox/connectors/hive/iceberg/tests/CMakeLists.txt +++ b/velox/connectors/hive/iceberg/tests/CMakeLists.txt @@ -38,8 +38,20 @@ endif() if(NOT VELOX_DISABLE_GOOGLETEST) - add_executable(velox_hive_iceberg_test IcebergReadTest.cpp - IcebergSplitReaderBenchmarkTest.cpp) + add_executable( + velox_hive_iceberg_test + ColumnTransformTest.cpp + IcebergInsertTest.cpp + IcebergPartitionIdGeneratorTest.cpp + IcebergReadTest.cpp + IcebergSortOrderTest.cpp + IcebergSplitReaderBenchmarkTest.cpp + IcebergStatsTest.cpp + IcebergTestBase.cpp + IcebergTransformE2ETest.cpp + IcebergTransformUnitTest.cpp + Main.cpp + Murmur3Test.cpp) add_test(velox_hive_iceberg_test velox_hive_iceberg_test) target_link_libraries( @@ -51,6 +63,7 @@ if(NOT VELOX_DISABLE_GOOGLETEST) velox_dwio_common_exception velox_dwio_common_test_utils velox_vector_test_lib + velox_vector_fuzzer velox_exec velox_exec_test_lib Folly::folly @@ -58,4 +71,9 @@ if(NOT VELOX_DISABLE_GOOGLETEST) GTest::gtest GTest::gtest_main) + if(VELOX_ENABLE_PARQUET) + target_link_libraries(velox_hive_iceberg_test velox_dwio_parquet_writer + velox_dwio_parquet_reader) + endif() + endif() diff --git a/velox/connectors/hive/iceberg/tests/ColumnTransformTest.cpp b/velox/connectors/hive/iceberg/tests/ColumnTransformTest.cpp new file mode 100644 index 000000000000..1b83e1b15b56 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/ColumnTransformTest.cpp @@ -0,0 +1,120 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/Transforms.h" +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" +#include "velox/vector/tests/utils/VectorMaker.h" + +using namespace facebook::velox; +using namespace facebook::velox::connector::hive::iceberg; +using namespace facebook::velox::test; + +namespace facebook::velox::connector::hive::iceberg::test { + +class ColumnTransformTest : public IcebergTestBase {}; + +TEST_F(ColumnTransformTest, testConstructor) { + auto transform = std::make_shared>( + INTEGER(), "test_column", opPool_.get()); + + EXPECT_EQ(transform->sourceColumnName(), "test_column"); + EXPECT_EQ(transform->name(), "identity"); + EXPECT_EQ(transform->resultType(), INTEGER()); +} + +TEST_F(ColumnTransformTest, testTransformName) { + auto identityTransform = std::make_shared>( + INTEGER(), "col1", opPool_.get()); + EXPECT_EQ(identityTransform->name(), "identity"); + + auto bucketTransform = std::make_shared>( + 16, INTEGER(), "col2", opPool_.get()); + EXPECT_EQ(bucketTransform->name(), "bucket"); + + auto truncateTransform = std::make_shared>( + 10, INTEGER(), "col3", opPool_.get()); + EXPECT_EQ(truncateTransform->name(), "trunc"); + + auto yearTransform = std::make_shared>( + INTEGER(), TransformType::kYear, "col4", opPool_.get(), [](int32_t v) { + return v; + }); + EXPECT_EQ(yearTransform->name(), "year"); + + auto monthTransform = std::make_shared>( + INTEGER(), TransformType::kMonth, "col5", opPool_.get(), [](int32_t v) { + return v; + }); + EXPECT_EQ(monthTransform->name(), "month"); + + auto dayTransform = std::make_shared>( + INTEGER(), TransformType::kDay, "col6", opPool_.get(), [](int32_t v) { + return v; + }); + EXPECT_EQ(dayTransform->name(), "day"); + + auto hourTransform = std::make_shared>( + INTEGER(), TransformType::kHour, "col7", opPool_.get(), [](Timestamp v) { + return v.getSeconds() / 3600; + }); + EXPECT_EQ(hourTransform->name(), "hour"); +} + +TEST_F(ColumnTransformTest, testResultType) { + auto intTransform = std::make_shared>( + INTEGER(), "col_int", opPool_.get()); + EXPECT_EQ(intTransform->resultType(), INTEGER()); + + auto bigintTransform = std::make_shared>( + BIGINT(), "col_bigint", opPool_.get()); + EXPECT_EQ(bigintTransform->resultType(), BIGINT()); + + auto varcharTransform = std::make_shared>( + VARCHAR(), "col_varchar", opPool_.get()); + EXPECT_EQ(varcharTransform->resultType(), VARCHAR()); + + auto bucketTransform = std::make_shared>( + 16, VARCHAR(), "col_bucket", opPool_.get()); + EXPECT_EQ(bucketTransform->resultType(), INTEGER()); + + auto yearTransform = std::make_shared>( + DATE(), TransformType::kYear, "col_year", opPool_.get(), [](int32_t v) { + return v; + }); + EXPECT_EQ(yearTransform->resultType(), INTEGER()); +} + +TEST_F(ColumnTransformTest, testTransformSimpleColumn) { + auto intVector = makeFlatVector({1, 2, 3, 4, 5}); + auto rowVector = makeRowVector({"col_int"}, {intVector}); + + auto transform = std::make_shared>( + INTEGER(), "col_int", opPool_.get()); + + auto result = transform->transform(rowVector, 0); + + ASSERT_EQ(result->size(), 5); + ASSERT_EQ(result->type(), INTEGER()); + + auto resultVector = result->as>(); + EXPECT_EQ(resultVector->valueAt(0), 1); + EXPECT_EQ(resultVector->valueAt(1), 2); + EXPECT_EQ(resultVector->valueAt(2), 3); + EXPECT_EQ(resultVector->valueAt(3), 4); + EXPECT_EQ(resultVector->valueAt(4), 5); +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergInsertTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergInsertTest.cpp new file mode 100644 index 000000000000..68462df2a39c --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergInsertTest.cpp @@ -0,0 +1,233 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include "velox/common/base/tests/GTestUtils.h" +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" +#include "velox/exec/tests/utils/PlanBuilder.h" + +namespace facebook::velox::connector::hive::iceberg::test { +class IcebergInsertTest : public IcebergTestBase { + protected: + void SetUp() override { + IcebergTestBase::SetUp(); + rowType_ = + ROW({"c1", "c2", "c3", "c4", "c5", "c6"}, + {BIGINT(), + INTEGER(), + SMALLINT(), + DECIMAL(18, 5), + BOOLEAN(), + VARCHAR()}); + } +}; + +TEST_F(IcebergInsertTest, testIcebergTableWrite) { + const auto outputDirectory = exec::test::TempDirectoryPath::create(); + constexpr int32_t numBatches = 10; + constexpr int32_t vectorSize = 5'000; + const auto vectors = createTestData(numBatches, vectorSize, 0.5); + auto dataSink = + createIcebergDataSink(rowType_, outputDirectory->getPath(), {}); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + const auto commitTasks = dataSink->close(); + createDuckDbTable(vectors); + auto splits = createSplitsForDirectory(outputDirectory->getPath()); + ASSERT_EQ(splits.size(), commitTasks.size()); + auto plan = exec::test::PlanBuilder().tableScan(rowType_).planNode(); + assertQuery(plan, splits, fmt::format("SELECT * FROM tmp")); +} + +TEST_F(IcebergInsertTest, testSingleColumnAsPartition) { + for (auto colIndex = 0; colIndex < rowType_->size() - 1; colIndex++) { + const auto& colName = rowType_->nameOf(colIndex); + const auto outputDirectory = exec::test::TempDirectoryPath::create(); + constexpr int32_t numBatches = 2; + constexpr int32_t vectorSize = 50; + + const auto vectors = createTestData(numBatches, vectorSize, 0.5); + std::vector partitionTransforms = { + {colIndex, TransformType::kIdentity, std::nullopt}}; + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + const auto commitTasks = dataSink->close(); + createDuckDbTable(vectors); + auto splits = createSplitsForDirectory(outputDirectory->getPath()); + + ASSERT_GT(commitTasks.size(), 0); + ASSERT_EQ(splits.size(), commitTasks.size()); + + for (const auto& task : commitTasks) { + auto taskJson = folly::parseJson(task); + ASSERT_TRUE(taskJson.count("partitionDataJson") > 0); + ASSERT_FALSE(taskJson["partitionDataJson"].empty()); + } + + connector::ColumnHandleMap assignments; + for (auto i = 0; i < rowType_->size(); i++) { + const auto& name = rowType_->nameOf(i); + if (i != colIndex) { + assignments.insert( + {name, + std::make_shared( + name, + HiveColumnHandle::ColumnType::kRegular, + rowType_->childAt(i), + rowType_->childAt(i))}); + } + } + + // Add partition column. + assignments.insert( + {colName, + std::make_shared( + colName, + HiveColumnHandle::ColumnType::kPartitionKey, + rowType_->childAt(colIndex), + rowType_->childAt(colIndex))}); + + auto plan = exec::test::PlanBuilder(pool_.get()) + .tableScan(rowType_, {}, "", nullptr, assignments) + .planNode(); + + assertQuery(plan, splits, fmt::format("SELECT * FROM tmp")); + } +} + +TEST_F(IcebergInsertTest, testPartitionNullColumn) { + for (auto colIndex = 0; colIndex < rowType_->size() - 1; colIndex++) { + const auto& colName = rowType_->nameOf(colIndex); + const auto colType = rowType_->childAt(colIndex); + const auto outputDirectory = exec::test::TempDirectoryPath::create(); + constexpr int32_t numBatches = 2; + constexpr int32_t vectorSize = 100; + + const auto vectors = createTestData(numBatches, vectorSize, 1.0); + std::vector partitionTransforms = { + {colIndex, TransformType::kIdentity, std::nullopt}}; + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + const auto commitTasks = dataSink->close(); + ASSERT_EQ(1, commitTasks.size()); + auto taskJson = folly::parseJson(commitTasks.at(0)); + ASSERT_EQ(1, taskJson.count("partitionDataJson")); + auto partitionDataStr = taskJson["partitionDataJson"].asString(); + auto partitionData = folly::parseJson(partitionDataStr); + ASSERT_EQ(1, partitionData.count("partitionValues")); + auto partitionValues = partitionData["partitionValues"]; + ASSERT_TRUE(partitionValues.isArray()); + ASSERT_TRUE(partitionValues[0].isNull()); + + auto files = listFiles(outputDirectory->getPath()); + ASSERT_EQ(files.size(), 1); + + for (const auto& file : files) { + std::vector pathComponents; + folly::split("/", file, pathComponents); + bool foundPartitionDir = false; + for (const auto& component : pathComponents) { + if (component.find('=') != std::string::npos) { + foundPartitionDir = true; + std::vector parts; + folly::split('=', component, parts); + ASSERT_EQ(parts.size(), 2); + ASSERT_EQ(parts[0], colName); + ASSERT_EQ(parts[1], "null"); + } + } + ASSERT_TRUE(foundPartitionDir) + << "No partition directory found in path: " << file; + } + } +} + +TEST_F(IcebergInsertTest, testColumnCombinationsAsPartition) { + std::vector> columnCombinations = { + {0, 1}, // BIGINT, INTEGER. + {2, 1}, // SMALLINT, INTEGER. + {2, 3}, // SMALLINT, DECIMAL. + {0, 2, 1} // BIGINT, SMALLINT, INTEGER. + }; + + for (const auto& combination : columnCombinations) { + const auto outputDirectory = exec::test::TempDirectoryPath::create(); + constexpr int32_t numBatches = 2; + constexpr int32_t vectorSize = 50; + const auto vectors = createTestData(numBatches, vectorSize); + std::vector partitionTransforms; + for (auto colIndex : combination) { + partitionTransforms.push_back( + {colIndex, TransformType::kIdentity, std::nullopt}); + } + + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + const auto commitTasks = dataSink->close(); + createDuckDbTable(vectors); + auto splits = createSplitsForDirectory(outputDirectory->getPath()); + + ASSERT_GT(commitTasks.size(), 0); + ASSERT_EQ(splits.size(), commitTasks.size()); + + connector::ColumnHandleMap assignments; + std::unordered_set partitionColumns( + combination.begin(), combination.end()); + + for (auto i = 0; i < rowType_->size(); i++) { + const auto& name = rowType_->nameOf(i); + auto columnType = partitionColumns.count(i) > 0 + ? HiveColumnHandle::ColumnType::kPartitionKey + : HiveColumnHandle::ColumnType::kRegular; + + assignments.insert( + {name, + std::make_shared( + name, columnType, rowType_->childAt(i), rowType_->childAt(i))}); + } + + auto plan = exec::test::PlanBuilder(pool_.get()) + .tableScan(rowType_, {}, "", nullptr, assignments) + .planNode(); + + assertQuery(plan, splits, fmt::format("SELECT * FROM tmp")); + } +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergPartitionIdGeneratorTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergPartitionIdGeneratorTest.cpp new file mode 100644 index 000000000000..44fde9b2e610 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergPartitionIdGeneratorTest.cpp @@ -0,0 +1,363 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/IcebergPartitionIdGenerator.h" +#include "velox/connectors/hive/iceberg/Transforms.h" +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" + +using namespace facebook::velox; + +namespace facebook::velox::connector::hive::iceberg::test { + +class IcebergPartitionIdGeneratorTest : public IcebergTestBase { + protected: + std::vector> createColumnTransforms( + const std::vector& columnNames, + const std::vector& types, + const std::vector& transformTypes, + const std::vector>& parameters = {}) { + std::vector fields; + fields.reserve(columnNames.size()); + + for (size_t i = 0; i < columnNames.size(); ++i) { + std::optional parameter = + parameters.size() > i ? parameters[i] : std::nullopt; + + fields.emplace_back( + columnNames[i], types[i], transformTypes[i], parameter); + } + + return parsePartitionTransformSpecs(fields, pool_.get()); + } + + std::unique_ptr createGenerator( + const std::vector>& transforms, + bool partitionPathAsLowerCase = false) { + std::vector partitionChannels; + for (size_t i = 0; i < transforms.size(); ++i) { + partitionChannels.push_back(i); + } + + return std::make_unique( + partitionChannels, + 128, + pool_.get(), + transforms, + partitionPathAsLowerCase); + } + + void verifyPartitionComponents( + const std::string& partitionName, + const std::vector& expectedComponents) { + std::vector actualComponents; + folly::split('/', partitionName, actualComponents); + ASSERT_EQ(actualComponents.size(), expectedComponents.size()); + for (size_t i = 0; i < expectedComponents.size(); ++i) { + ASSERT_EQ(actualComponents[i], expectedComponents[i]); + } + } +}; + +TEST_F(IcebergPartitionIdGeneratorTest, partitionNameWithIdentityTransforms) { + std::vector columnNames = { + "c_int", "c_bigint", "c_varchar", "c_decimal", "c_bool", "c_date"}; + + std::vector columns = { + makeConstant(42, 1), + makeConstant(9'876'543'210, 1), + makeConstant("test string", 1), + makeConstant(12'345'678'901'234, 1, DECIMAL(18, 4)), + makeConstant(true, 1), + makeConstant(18'262, 1, DATE())}; + + std::vector types = { + INTEGER(), BIGINT(), VARCHAR(), DECIMAL(18, 4), BOOLEAN(), DATE()}; + auto rowVector = makeRowVector(columnNames, columns); + std::vector transformTypes( + columnNames.size(), TransformType::kIdentity); + auto transforms = createColumnTransforms(columnNames, types, transformTypes); + auto generator = createGenerator(transforms); + raw_vector partitionIds(1); + generator->run(rowVector, partitionIds); + + std::string partitionName = generator->partitionName(partitionIds[0]); + std::vector expectedComponents = { + "c_int=42", + "c_bigint=9876543210", + "c_varchar=test+string", + "c_decimal=1234567890.1234", + "c_bool=true", + "c_date=2020-01-01"}; + verifyPartitionComponents(partitionName, expectedComponents); +} + +TEST_F( + IcebergPartitionIdGeneratorTest, + partitionNameWithTimestampIdentitySpecialValues) { + std::vector timestamps = { + Timestamp(253402300800, 100000000), // +10000-01-01T00:00:00.1. + Timestamp(-62170000000, 0), // -0001-11-29T19:33:20. + Timestamp(-62135577748, 999000000), // 0001-01-01T05:17:32.999. + Timestamp(0, 0), // 1970-01-01T00:00. + Timestamp(1609459200, 999000000), // 2021-01-01T00:00. + Timestamp(1640995200, 500000000), // 2022-01-01T00:00:00.5. + Timestamp(1672531200, 123000000), // 2023-01-01T00:00:00.123. + Timestamp(-1, 999000000), // 1969-12-31T23:59:59.999. + Timestamp(1, 1000000), // 1970-01-01T00:00:01.001. + Timestamp(-62167219199, 0), // 0000-01-01T00:00:01. + Timestamp(-377716279140, 321000000), // -10000-01-01T01:01:00.321. + Timestamp(253402304660, 321000000), // +10000-01-01T01:01:00.321. + Timestamp(951782400, 0), // 2000-02-29T00:00:00 (leap year). + Timestamp(4107456000, 0), // 2100-02-28T00:00:00 (not leap year). + Timestamp(86400, 0), // 1970-01-02T00:00:00. + Timestamp(-86400, 0), // 1969-12-31T00:00:00. + Timestamp(1672531200, 456000000), // 2023-01-01T00:00:00.456. + Timestamp(1672531200, 789000000), // 2023-01-01T00:00:00.789. + }; + + std::vector expectedPartitionNames = { + "c_timestamp=%2B10000-01-01T00%3A00%3A00.1", + "c_timestamp=-0001-11-29T19%3A33%3A20", + "c_timestamp=0001-01-01T05%3A17%3A32.999", + "c_timestamp=1970-01-01T00%3A00", + "c_timestamp=2021-01-01T00%3A00%3A00.999", + "c_timestamp=2022-01-01T00%3A00%3A00.5", + "c_timestamp=2023-01-01T00%3A00%3A00.123", + "c_timestamp=1969-12-31T23%3A59%3A59.999", + "c_timestamp=1970-01-01T00%3A00%3A01.001", + "c_timestamp=0000-01-01T00%3A00%3A01", + "c_timestamp=-10000-08-24T19%3A21%3A00.321", + "c_timestamp=%2B10000-01-01T01%3A04%3A20.321", + "c_timestamp=2000-02-29T00%3A00", + "c_timestamp=2100-02-28T00%3A00", + "c_timestamp=1970-01-02T00%3A00", + "c_timestamp=1969-12-31T00%3A00", + "c_timestamp=2023-01-01T00%3A00%3A00.456", + "c_timestamp=2023-01-01T00%3A00%3A00.789", + }; + + auto timestampVector = makeFlatVector(timestamps); + std::vector columnNames = {"c_timestamp"}; + std::vector columns = {timestampVector}; + std::vector types = {TIMESTAMP()}; + auto rowVector = makeRowVector(columnNames, columns); + + std::vector transformTypes = {TransformType::kIdentity}; + auto transforms = createColumnTransforms(columnNames, types, transformTypes); + auto generator = createGenerator(transforms); + raw_vector partitionIds(timestamps.size()); + generator->run(rowVector, partitionIds); + + for (size_t i = 0; i < timestamps.size(); ++i) { + std::string partitionName = generator->partitionName(partitionIds[i]); + ASSERT_EQ(partitionName, expectedPartitionNames[i]); + } +} + +TEST_F(IcebergPartitionIdGeneratorTest, partitionNameWithMixedTransforms) { + std::vector columnNames = { + "c_int", + "c_bigint", + "c_varchar", + "c_year", + "c_month", + "c_day", + "c_hour", + "c_bool"}; + + std::vector columns = { + makeConstant(42, 1), + makeConstant(9'876'543'210, 1), + makeConstant("test string", 1), + makeConstant(Timestamp(1'577'836'800, 0), 1), + makeConstant(Timestamp(1'578'836'800, 0), 1), + makeConstant(Timestamp(1'579'836'800, 0), 1), + makeConstant(Timestamp(1'57'936'800, 0), 1), + makeConstant(true, 1)}; + + std::vector types = { + INTEGER(), + BIGINT(), + VARCHAR(), + TIMESTAMP(), + TIMESTAMP(), + TIMESTAMP(), + TIMESTAMP(), + BOOLEAN()}; + + auto rowVector = makeRowVector(columnNames, columns); + + std::vector transformTypes = { + TransformType::kBucket, + TransformType::kTruncate, + TransformType::kTruncate, + TransformType::kYear, + TransformType::kMonth, + TransformType::kDay, + TransformType::kHour, + TransformType::kIdentity}; + + std::vector> parameters = {4, 1'000, 5, std::nullopt}; + auto transforms = + createColumnTransforms(columnNames, types, transformTypes, parameters); + + auto generator = createGenerator(transforms); + raw_vector partitionIds(1); + generator->run(rowVector, partitionIds); + + std::string partitionName = generator->partitionName(partitionIds[0]); + std::vector expectedComponents = { + "c_int_bucket=2", + "c_bigint_trunc=9876543000", + "c_varchar_trunc=test+", + "c_year_year=2020", + "c_month_month=2020-01", + "c_day_day=2020-01-24", + "c_hour_hour=1975-01-02-23", + "c_bool=true"}; + verifyPartitionComponents(partitionName, expectedComponents); +} + +TEST_F(IcebergPartitionIdGeneratorTest, partitionNameWithNullValues) { + std::vector columnNames = {"c_int", "c_varchar", "c_decimal"}; + std::vector columns = { + makeConstant(std::nullopt, 1), + makeConstant(std::nullopt, 1), + makeConstant(std::nullopt, 1, DECIMAL(18, 4))}; + std::vector types = {INTEGER(), VARCHAR(), DECIMAL(18, 3)}; + auto rowVector = makeRowVector(columnNames, columns); + + std::vector transformTypes = { + TransformType::kBucket, + TransformType::kTruncate, + TransformType::kIdentity}; + std::vector> parameters = {4, 1'000, std::nullopt}; + auto transforms = + createColumnTransforms(columnNames, types, transformTypes, parameters); + auto generator = createGenerator(transforms); + raw_vector partitionIds(1); + generator->run(rowVector, partitionIds); + + std::string partitionName = generator->partitionName(partitionIds[0]); + std::vector expectedComponents = { + "c_int_bucket=null", "c_varchar_trunc=null", "c_decimal=null"}; + verifyPartitionComponents(partitionName, expectedComponents); +} + +TEST_F(IcebergPartitionIdGeneratorTest, partitionNameWithLowerCase) { + auto varcharVector = makeConstant("MiXeD_CaSe", 1); + std::vector columnNames = {"MiXeD_CoLuMn"}; + std::vector columns = {varcharVector}; + std::vector types = {VARCHAR()}; + auto rowVector = makeRowVector(columnNames, columns); + std::vector transformTypes = {TransformType::kIdentity}; + auto transforms = createColumnTransforms(columnNames, types, transformTypes); + auto generator = createGenerator(transforms, true); + raw_vector partitionIds(1); + generator->run(rowVector, partitionIds); + std::string partitionName = generator->partitionName(partitionIds[0]); + std::vector expectedPartitionName = {"mixed_column=MiXeD_CaSe"}; + verifyPartitionComponents(partitionName, expectedPartitionName); + + generator = createGenerator(transforms); + generator->run(rowVector, partitionIds); + partitionName = generator->partitionName(partitionIds[0]); + expectedPartitionName = {"MiXeD_CoLuMn=MiXeD_CaSe"}; + verifyPartitionComponents(partitionName, expectedPartitionName); +} + +TEST_F(IcebergPartitionIdGeneratorTest, urlEncodingForSpecialChars) { + std::vector> testCases = { + {"space test", "space+test"}, + {"slash/test", "slash%2Ftest"}, + {"question?test", "question%3Ftest"}, + {"percent%test", "percent%25test"}, + {"hash#test", "hash%23test"}, + {"ampersand&test", "ampersand%26test"}, + {"equals=test", "equals%3Dtest"}, + {"plus+test", "plus%2Btest"}, + {"comma,test", "comma%2Ctest"}, + {"semicolon;test", "semicolon%3Btest"}, + {"at@test", "at%40test"}, + {"dollar$test", "dollar%24test"}, + {"backslash\\test", "backslash%5Ctest"}, + {"quote\"test", "quote%22test"}, + {"apostrophe'test", "apostrophe%27test"}, + {"lessthan", "greater%3Ethan"}, + {"colon:test", "colon%3Atest"}, + {"pipe|test", "pipe%7Ctest"}, + {"bracket[test", "bracket%5Btest"}, + {"bracket]test", "bracket%5Dtest"}, + {"brace{test", "brace%7Btest"}, + {"brace}test", "brace%7Dtest"}, + {"caret^test", "caret%5Etest"}, + {"tilde~test", "tilde%7Etest"}, + {"backtick`test", "backtick%60test"}, + {"unicode\u00A9test", "unicode%C2%A9test"}, + {"email@example.com", "email%40example.com"}, + {"user:password@host:port/path", "user%3Apassword%40host%3Aport%2Fpath"}, + {"https://github.ibm.com/IBM/velox", + "https%3A%2F%2Fgithub.ibm.com%2FIBM%2Fvelox"}, + {"a+b=c&d=e+f", "a%2Bb%3Dc%26d%3De%2Bf"}, + {"special!@#$%^&*()_+", "special%21%40%23%24%25%5E%26*%28%29_%2B"}, + }; + + std::vector transformTypes = {TransformType::kIdentity}; + std::vector types = {VARCHAR()}; + std::vector columnNames = {"ColumnWithSpecialChars"}; + auto transforms = createColumnTransforms(columnNames, types, transformTypes); + raw_vector partitionIds(1); + auto generator = createGenerator(transforms); + + for (const auto& [input, expectedEncoded] : testCases) { + auto varcharVector = makeConstant(StringView(input), 1); + auto rowVector = makeRowVector(columnNames, {varcharVector}); + generator->run(rowVector, partitionIds); + std::string partitionName = generator->partitionName(partitionIds[0]); + std::string expectedPartitionName = + fmt::format("{}={}", columnNames[0], expectedEncoded); + ASSERT_EQ(partitionName, expectedPartitionName); + } +} + +TEST_F(IcebergPartitionIdGeneratorTest, multipleRows) { + std::vector columnNames = {"c_int", "c_varchar"}; + auto rowVector = makeRowVector( + columnNames, + {makeFlatVector({10, 20, 30}), + makeFlatVector({"value1", "value2", "value3"})}); + + std::vector types = {INTEGER(), VARCHAR()}; + std::vector transformTypes( + columnNames.size(), TransformType::kIdentity); + auto transforms = createColumnTransforms(columnNames, types, transformTypes); + auto generator = createGenerator(transforms); + raw_vector partitionIds(3); + generator->run(rowVector, partitionIds); + + std::vector expectedNames = { + "c_int=10/c_varchar=value1", + "c_int=20/c_varchar=value2", + "c_int=30/c_varchar=value3"}; + + for (size_t i = 0; i < 3; ++i) { + std::string partitionName = generator->partitionName(partitionIds[i]); + ASSERT_EQ(partitionName, expectedNames[i]); + } +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergReadTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergReadTest.cpp index 8bf51afb25ed..46544873bfaf 100644 --- a/velox/connectors/hive/iceberg/tests/IcebergReadTest.cpp +++ b/velox/connectors/hive/iceberg/tests/IcebergReadTest.cpp @@ -208,7 +208,7 @@ class HiveIcebergTest : public HiveConnectorTestBase { IcebergDeleteFile icebergDeleteFile( FileContent::kPositionalDeletes, deleteFilePath, - fileFomat_, + fileFormat_, deleteFilePaths[deleteFileName].first, testing::internal::GetFileSize( std::fopen(deleteFilePath.c_str(), "r"))); @@ -223,7 +223,7 @@ class HiveIcebergTest : public HiveConnectorTestBase { std::string duckdbSql = getDuckDBQuery(rowGroupSizesForFiles, deleteFilesForBaseDatafiles); - auto plan = tableScanNode(); + auto plan = tableScanNode(rowType_); auto task = HiveConnectorTestBase::assertQuery( plan, splits, duckdbSql, numPrefetchSplits); @@ -234,6 +234,125 @@ class HiveIcebergTest : public HiveConnectorTestBase { ASSERT_TRUE(it->second.peakMemoryBytes > 0); } + void assertEqualityDeletes( + const std::unordered_map>>& + equalityDeleteVectorMap, + const std::unordered_map>& + equalityFieldIdsMap, + std::string duckDbSql = "", + std::vector dataVectors = {}) { + VELOX_CHECK_EQ(equalityDeleteVectorMap.size(), equalityFieldIdsMap.size()); + // We will create data vectors with numColumns number of columns that is the + // max field Id in equalityFieldIds + int32_t numDataColumns = 0; + + for (auto it = equalityFieldIdsMap.begin(); it != equalityFieldIdsMap.end(); + ++it) { + auto equalityFieldIds = it->second; + auto currentMax = + *std::max_element(equalityFieldIds.begin(), equalityFieldIds.end()); + numDataColumns = std::max(numDataColumns, currentMax); + } + + VELOX_CHECK_GT(numDataColumns, 0); + VELOX_CHECK_GE(numDataColumns, equalityDeleteVectorMap.size()); + VELOX_CHECK_GT(equalityDeleteVectorMap.size(), 0); + + VELOX_CHECK_LE(equalityFieldIdsMap.size(), numDataColumns); + + std::shared_ptr dataFilePath = + writeDataFiles(rowCount, numDataColumns, 1, dataVectors)[0]; + + std::vector deleteFiles; + std::string predicates = ""; + unsigned long numDeletedValues = 0; + + std::vector> deleteFilePaths; + for (auto it = equalityFieldIdsMap.begin(); + it != equalityFieldIdsMap.end();) { + auto equalityFieldIds = it->second; + auto equalityDeleteVector = equalityDeleteVectorMap.at(it->first); + VELOX_CHECK_GT(equalityDeleteVector.size(), 0); + numDeletedValues = + std::max(numDeletedValues, equalityDeleteVector[0].size()); + deleteFilePaths.push_back(writeEqualityDeleteFile(equalityDeleteVector)); + IcebergDeleteFile deleteFile( + FileContent::kEqualityDeletes, + deleteFilePaths.back()->getPath(), + fileFormat_, + equalityDeleteVector[0].size(), + testing::internal::GetFileSize( + std::fopen(deleteFilePaths.back()->getPath().c_str(), "r")), + equalityFieldIds); + deleteFiles.push_back(deleteFile); + predicates += makePredicates(equalityDeleteVector, equalityFieldIds); + ++it; + if (it != equalityFieldIdsMap.end()) { + predicates += " AND "; + } + } + + // The default split count is 1. + auto icebergSplits = + makeIcebergSplits(dataFilePath->getPath(), deleteFiles); + + // If the caller passed in a query, use that. + if (duckDbSql == "") { + // Select all columns + duckDbSql = "SELECT * FROM tmp "; + if (numDeletedValues > 0) { + duckDbSql += fmt::format("WHERE {}", predicates); + } + } + + assertEqualityDeletes( + icebergSplits.back(), + !dataVectors.empty() ? asRowType(dataVectors[0]->type()) : rowType_, + duckDbSql); + + // Select a column that's not in the filter columns + if (numDataColumns > 1 && + equalityDeleteVectorMap.at(0).size() < numDataColumns) { + std::string duckDbQuery = "SELECT c0 FROM tmp"; + if (numDeletedValues > 0) { + duckDbQuery += fmt::format(" WHERE {}", predicates); + } + + std::vector names({"c0"}); + std::vector types(1, BIGINT()); + assertEqualityDeletes( + icebergSplits.back(), + std::make_shared(std::move(names), std::move(types)), + duckDbQuery); + } + } + + std::vector makeSequenceValues(int32_t numRows, int8_t repeat = 1) { + VELOX_CHECK_GT(repeat, 0); + + auto maxValue = std::ceil((double)numRows / repeat); + std::vector values; + values.reserve(numRows); + for (int32_t i = 0; i < maxValue; i++) { + for (int8_t j = 0; j < repeat; j++) { + values.push_back(i); + } + } + values.resize(numRows); + return values; + } + + std::vector makeRandomDeleteValues(int32_t maxRowNumber) { + std::mt19937 gen{0}; + std::vector deleteRows; + for (int i = 0; i < maxRowNumber; i++) { + if (folly::Random::rand32(0, 10, gen) > 8) { + deleteRows.push_back(i); + } + } + return deleteRows; + } + const static int rowCount = 20000; protected: @@ -259,7 +378,7 @@ class HiveIcebergTest : public HiveConnectorTestBase { splits.emplace_back(std::make_shared( kHiveConnectorId, dataFilePath, - fileFomat_, + fileFormat_, i * splitSize, splitSize, partitionKeys, @@ -273,6 +392,20 @@ class HiveIcebergTest : public HiveConnectorTestBase { return splits; } + void assertEqualityDeletes( + std::shared_ptr split, + RowTypePtr outputRowType, + const std::string& duckDbSql) { + auto plan = tableScanNode(outputRowType); + auto task = OperatorTestBase::assertQuery(plan, {split}, duckDbSql); + + auto planStats = toPlanStats(task->taskStats()); + auto scanNodeId = plan->id(); + auto it = planStats.find(scanNodeId); + ASSERT_TRUE(it != planStats.end()); + ASSERT_TRUE(it->second.peakMemoryBytes > 0); + } + private: std::map> writeDataFiles( std::map> rowGroupSizesForFiles) { @@ -494,17 +627,151 @@ class HiveIcebergTest : public HiveConnectorTestBase { }); } - core::PlanNodePtr tableScanNode() { - return PlanBuilder(pool_.get()).tableScan(rowType_).planNode(); + core::PlanNodePtr tableScanNode(RowTypePtr outputRowType) { + return PlanBuilder(pool_.get()).tableScan(outputRowType).planNode(); } - dwio::common::FileFormat fileFomat_{dwio::common::FileFormat::DWRF}; + std::string makePredicates( + const std::vector>& equalityDeleteVector, + const std::vector& equalityFieldIds) { + std::string predicates(""); + int32_t numDataColumns = + *std::max_element(equalityFieldIds.begin(), equalityFieldIds.end()); + + VELOX_CHECK_GT(numDataColumns, 0); + VELOX_CHECK_GE(numDataColumns, equalityDeleteVector.size()); + VELOX_CHECK_GT(equalityDeleteVector.size(), 0); + + auto numDeletedValues = equalityDeleteVector[0].size(); + + if (numDeletedValues == 0) { + return predicates; + } + + // If all values for a column are deleted, just return an always-false + // predicate + for (auto i = 0; i < equalityDeleteVector.size(); i++) { + auto equalityFieldId = equalityFieldIds[i]; + auto deleteValues = equalityDeleteVector[i]; + + auto lastIter = std::unique(deleteValues.begin(), deleteValues.end()); + auto numDistinctValues = lastIter - deleteValues.begin(); + auto minValue = 1; + auto maxValue = *std::max_element(deleteValues.begin(), lastIter); + if (maxValue - minValue + 1 == numDistinctValues && + maxValue == (rowCount - 1) / equalityFieldId) { + return "1 = 0"; + } + } + + if (equalityDeleteVector.size() == 1) { + std::string name = fmt::format("c{}", equalityFieldIds[0] - 1); + predicates = fmt::format( + "{} NOT IN ({})", name, makeNotInList({equalityDeleteVector[0]})); + } else { + for (int i = 0; i < numDeletedValues; i++) { + std::string oneRow(""); + for (int j = 0; j < equalityFieldIds.size(); j++) { + std::string name = fmt::format("c{}", equalityFieldIds[j] - 1); + std::string predicate = + fmt::format("({} <> {})", name, equalityDeleteVector[j][i]); + + oneRow = oneRow == "" ? predicate + : fmt::format("({} OR {})", oneRow, predicate); + } + + predicates = predicates == "" + ? oneRow + : fmt::format("{} AND {}", predicates, oneRow); + } + } + return predicates; + } - RowTypePtr rowType_{ROW({"c0"}, {BIGINT()})}; std::shared_ptr pathColumn_ = IcebergMetadataColumn::icebergDeleteFilePathColumn(); std::shared_ptr posColumn_ = IcebergMetadataColumn::icebergDeletePosColumn(); + + protected: + RowTypePtr rowType_{ROW({"c0"}, {BIGINT()})}; + dwio::common::FileFormat fileFormat_{dwio::common::FileFormat::DWRF}; + + std::shared_ptr writeEqualityDeleteFile( + const std::vector>& equalityDeleteVector) { + std::vector names; + std::vector vectors; + for (int i = 0; i < equalityDeleteVector.size(); i++) { + names.push_back(fmt::format("c{}", i)); + vectors.push_back(makeFlatVector(equalityDeleteVector[i])); + } + + RowVectorPtr deleteFileVectors = makeRowVector(names, vectors); + + auto deleteFilePath = TempFilePath::create(); + writeToFile(deleteFilePath->getPath(), deleteFileVectors); + + return deleteFilePath; + } + + std::vector> writeDataFiles( + uint64_t numRows, + int32_t numColumns = 1, + int32_t splitCount = 1, + std::vector dataVectors = {}) { + if (dataVectors.empty()) { + dataVectors = makeVectors(splitCount, numRows, numColumns); + } + VELOX_CHECK_EQ(dataVectors.size(), splitCount); + + std::vector> dataFilePaths; + dataFilePaths.reserve(splitCount); + for (auto i = 0; i < splitCount; i++) { + dataFilePaths.emplace_back(TempFilePath::create()); + writeToFile(dataFilePaths.back()->getPath(), dataVectors[i]); + } + + createDuckDbTable(dataVectors); + return dataFilePaths; + } + + std::vector + makeVectors(int32_t count, int32_t rowsPerVector, int32_t numColumns = 1) { + std::vector types(numColumns, BIGINT()); + std::vector names; + for (int j = 0; j < numColumns; j++) { + names.push_back(fmt::format("c{}", j)); + } + + std::vector rowVectors; + for (int i = 0; i < count; i++) { + std::vector vectors; + + // Create the column values like below: + // c0 c1 c2 + // 0 0 0 + // 1 0 0 + // 2 1 0 + // 3 1 1 + // 4 2 1 + // 5 2 1 + // 6 3 2 + // ... + // In the first column c0, the values are continuously increasing and not + // repeating. In the second column c1, the values are continuously + // increasing and each value repeats once. And so on. + for (int j = 0; j < numColumns; j++) { + auto data = makeSequenceValues(rowsPerVector, j + 1); + vectors.push_back(vectorMaker_.flatVector(data)); + } + + rowVectors.push_back(makeRowVector(names, vectors)); + } + + rowType_ = std::make_shared(std::move(names), std::move(types)); + + return rowVectors; + } }; /// This test creates one single data file and one delete file. The parameter @@ -774,4 +1041,232 @@ TEST_F(HiveIcebergTest, testPartitionedRead) { HiveConnectorTestBase::assertQuery(plan, splits, "SELECT 0, '2018-04-06'"); } + +// Delete values from a single column file +TEST_F(HiveIcebergTest, equalityDeletesSingleFileColumn1) { + folly::SingletonVault::singleton()->registrationComplete(); + + std::unordered_map> equalityFieldIdsMap; + std::unordered_map>> + equalityDeleteVectorMap; + equalityFieldIdsMap.insert({0, {1}}); + + // Delete row 0, 1, 2, 3 from the first batch out of two. + equalityDeleteVectorMap.insert({0, {{0, 1, 2, 3}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete the first and last row in each batch (10000 rows per batch) + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{0, 9999, 10000, 19999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete several rows in the second batch (10000 rows per batch) + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{10000, 10002, 19999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete random rows + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {makeRandomDeleteValues(rowCount)}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete 0 rows + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete all rows + equalityDeleteVectorMap.insert({0, {makeSequenceValues(rowCount)}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete rows that don't exist + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{20000, 29999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); +} + +// Delete values from the second column in a 2-column file +// +// c1 c2 +// 0 0 +// 1 0 +// 2 1 +// 3 1 +// 4 2 +// ... ... +// 19999 9999 +TEST_F(HiveIcebergTest, equalityDeletesSingleFileColumn2) { + folly::SingletonVault::singleton()->registrationComplete(); + + std::unordered_map> equalityFieldIdsMap; + std::unordered_map>> + equalityDeleteVectorMap; + equalityFieldIdsMap.insert({0, {2}}); + + // Delete values 0, 1, 2, 3 from the second column + equalityDeleteVectorMap.insert({0, {{0, 1, 2, 3}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete the smallest value 0 and the largest value 9999 from the second + // column, which has the range [0, 9999] + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{0, 9999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete non-existent values from the second column + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{10000, 10002, 19999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete random rows from the second column + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {makeSequenceValues(rowCount)}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete 0 values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete all values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {makeSequenceValues(rowCount / 2)}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); +} + +// Delete values from 2 columns with the following data: +// +// c1 c2 +// 0 0 +// 1 0 +// 2 1 +// 3 1 +// 4 2 +// ... ... +// 19999 9999 +TEST_F(HiveIcebergTest, equalityDeletesSingleFileMultipleColumns) { + folly::SingletonVault::singleton()->registrationComplete(); + + std::unordered_map> equalityFieldIdsMap; + std::unordered_map>> + equalityDeleteVectorMap; + equalityFieldIdsMap.insert({0, {1, 2}}); + + // Delete rows 0, 1 + equalityDeleteVectorMap.insert({0, {{0, 1}, {0, 0}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete rows 0, 2, 4, 6 + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{0, 2, 4, 6}, {0, 1, 2, 3}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete the last row + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{19999}, {9999}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete non-existent values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{20000, 30000}, {10000, 1500}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete 0 values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({0, {{}, {}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete all values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert( + {0, {makeSequenceValues(rowCount), makeSequenceValues(rowCount, 2)}}); + assertEqualityDeletes( + equalityDeleteVectorMap, + equalityFieldIdsMap, + "SELECT * FROM tmp WHERE 1 = 0"); +} + +TEST_F(HiveIcebergTest, equalityDeletesMultipleFiles) { + folly::SingletonVault::singleton()->registrationComplete(); + + std::unordered_map> equalityFieldIdsMap; + std::unordered_map>> + equalityDeleteVectorMap; + equalityFieldIdsMap.insert({{0, {1}}, {1, {2}}}); + + // Delete rows {0, 1} from c0, {2, 3} from c1, with two equality delete files + equalityDeleteVectorMap.insert({{0, {{0, 1}}}, {1, {{2, 3}}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete using 3 equality delete files + equalityFieldIdsMap.insert({{2, {3}}}); + equalityDeleteVectorMap.insert({{0, {{0, 1}}}, {1, {{2, 3}}}, {2, {{4, 5}}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete 0 values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert({{0, {{}}}, {1, {{}}}, {2, {{}}}}); + assertEqualityDeletes(equalityDeleteVectorMap, equalityFieldIdsMap); + + // Delete all values + equalityDeleteVectorMap.clear(); + equalityDeleteVectorMap.insert( + {{0, {makeSequenceValues(rowCount)}}, + {1, {makeSequenceValues(rowCount)}}, + {2, {makeSequenceValues(rowCount)}}}); + assertEqualityDeletes( + equalityDeleteVectorMap, + equalityFieldIdsMap, + "SELECT * FROM tmp WHERE 1 = 0"); +} + +TEST_F(HiveIcebergTest, TestSubFieldEqualityDelete) { + folly::SingletonVault::singleton()->registrationComplete(); + + // Write the base file + std::shared_ptr dataFilePath = TempFilePath::create(); + std::vector dataVectors = {makeRowVector( + {"c_bigint", "c_row"}, + {makeFlatVector(20, [](auto row) { return row + 1; }), + makeRowVector( + {"c0", "c1", "c2"}, + {makeFlatVector(20, [](auto row) { return row + 1; }), + makeFlatVector(20, [](auto row) { return row + 1; }), + makeFlatVector(20, [](auto row) { return row + 1; })})})}; + int32_t numDataColumns = 1; + dataFilePath = writeDataFiles(rowCount, numDataColumns, 1, dataVectors)[0]; + + // Write the delete file. Equality delete field is c_row.c1 + std::vector deleteFiles; + // Delete rows {0, 1} from c_row.c1, whose schema Id is 4 + std::vector deleteDataVectors = {makeRowVector( + {"c1"}, {makeFlatVector(2, [](auto row) { return row + 1; })})}; + + std::vector> deleteFilePaths; + auto equalityFieldIds = std::vector({4}); + auto deleteFilePath = TempFilePath::create(); + writeToFile(deleteFilePath->getPath(), deleteDataVectors.back()); + deleteFilePaths.push_back(deleteFilePath); + IcebergDeleteFile deleteFile( + FileContent::kEqualityDeletes, + deleteFilePaths.back()->getPath(), + fileFormat_, + 2, + testing::internal::GetFileSize( + std::fopen(deleteFilePaths.back()->getPath().c_str(), "r")), + equalityFieldIds); + deleteFiles.push_back(deleteFile); + + auto icebergSplits = makeIcebergSplits(dataFilePath->getPath(), deleteFiles); + + // Select both c_bigint and c_row column columns + std::string duckDbSql = "SELECT * FROM tmp WHERE c_row.c0 not in (1, 2)"; + assertEqualityDeletes( + icebergSplits.back(), asRowType(dataVectors[0]->type()), duckDbSql); + + // SELECT only c_bigint column + duckDbSql = "SELECT c_bigint FROM tmp WHERE c_row.c0 not in (1, 2)"; + assertEqualityDeletes( + icebergSplits.back(), ROW({"c_bigint"}, {BIGINT()}), duckDbSql); +} } // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/connectors/hive/iceberg/tests/IcebergSortOrderTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergSortOrderTest.cpp new file mode 100644 index 000000000000..45f017ea3208 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergSortOrderTest.cpp @@ -0,0 +1,592 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include "velox/connectors/hive/iceberg/IcebergSplit.h" +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" +#include "velox/exec/tests/utils/PlanBuilder.h" + +using namespace facebook::velox::exec::test; + +namespace facebook::velox::connector::hive::iceberg::test { + +class IcebergSortOrderTest : public IcebergTestBase { + protected: + void SetUp() override { + IcebergTestBase::SetUp(); + rowType_ = ROW( + {"c_int", + "c_bigint", + "c_varchar", + "c_date", + "c_decimal", + "c_varbinary"}, + {INTEGER(), BIGINT(), VARCHAR(), DATE(), DECIMAL(18, 3), VARBINARY()}); + } + + // Verify data in the file is sorted according to the specified sort columns. + void verifySortOrder( + const std::string& dataPath, + const std::vector& sortColumns) { + auto splits = createSplitsForDirectory(dataPath); + ASSERT_FALSE(splits.empty()) << "No data files found in " << dataPath; + + // Create a projection that selects all columns. + std::vector allColumns; + for (auto i = 0; i < rowType_->size(); ++i) { + allColumns.push_back(rowType_->nameOf(i)); + } + + auto plan = + PlanBuilder().tableScan(rowType_).project(allColumns).planNode(); + auto result = + AssertQueryBuilder(plan).splits(splits).copyResults(opPool_.get()); + + ASSERT_GT(result->size(), 0) << "No rows found in the data file"; + + // For each sort column, verify the data is sorted. + for (const auto& sortExpr : sortColumns) { + std::string columnName; + bool isAscending = true; + bool isNullsFirst = true; + + std::istringstream iss(sortExpr); + iss >> columnName; + std::string token; + if (iss >> token) { + if (token == "DESC") { + isAscending = false; + } else if (token != "ASC") { + iss.seekg(-(int32_t)token.length(), std::ios_base::cur); + } + + if (iss >> token && token == "NULLS") { + if (iss >> token && token == "LAST") { + isNullsFirst = false; + } else if (token != "FIRST") { + ASSERT_TRUE(token == "FIRST") + << "Invalid NULLS ordering: " << token; + } + } + } + + int32_t columnIndex = -1; + for (auto i = 0; i < rowType_->size(); ++i) { + if (rowType_->nameOf(i) == columnName) { + columnIndex = i; + break; + } + } + ASSERT_NE(columnIndex, -1) + << "Column " << columnName << " not found in row type"; + + auto columnVector = result->childAt(columnIndex); + bool hasNulls = false; + bool hasNonNulls = false; + vector_size_t firstNonNullIndex = 0; + vector_size_t lastNullIndex = 0; + + for (auto i = 0; i < columnVector->size(); ++i) { + if (columnVector->isNullAt(i)) { + hasNulls = true; + lastNullIndex = i; + } else { + if (!hasNonNulls) { + firstNonNullIndex = i; + hasNonNulls = true; + } + } + } + + if (hasNulls && hasNonNulls) { + if (isNullsFirst) { + ASSERT_LT(lastNullIndex, firstNonNullIndex) + << "NULL values should come before non-NULL values when NULLS FIRST is specified"; + } else { + ASSERT_GT(lastNullIndex, firstNonNullIndex) + << "NULL values should come after non-NULL values when NULLS LAST is specified"; + } + } + + DecodedVector decoded; + SelectivityVector rows(columnVector->size()); + decoded.decode(*columnVector, rows); + + for (auto i = 1; i < columnVector->size(); ++i) { + // Skip if either current or previous is null. + if (columnVector->isNullAt(i) || columnVector->isNullAt(i - 1)) { + continue; + } + + // Compare values based on type. + int32_t comparison = 0; + switch (auto kind = rowType_->childAt(columnIndex)->kind()) { + case TypeKind::INTEGER: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::BIGINT: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::VARCHAR: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::VARBINARY: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::HUGEINT: { + if (rowType_->childAt(columnIndex)->isLongDecimal()) { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + } + break; + } + default: + ASSERT_TRUE(false) + << "Unsupported column type for sorting verification: " << kind; + } + + if (isAscending) { + ASSERT_LE(comparison, 0) + << "Data not sorted in ascending order at row " << i + << " for column " << columnName; + } else { + ASSERT_GE(comparison, 0) + << "Data not sorted in descending order at row " << i + << " for column " << columnName; + } + + // If values are equal, continue to next row. + if (comparison == 0) { + continue; + } + break; + } + } + } + + // Verify that data is sorted according to multiple sort columns. + void verifyMultiColumnSortOrder( + const std::string& dataPath, + const std::vector& sortColumns) { + auto splits = createSplitsForDirectory(dataPath); + ASSERT_FALSE(splits.empty()) << "No data files found in " << dataPath; + std::vector allColumns; + for (auto i = 0; i < rowType_->size(); ++i) { + allColumns.push_back(rowType_->nameOf(i)); + } + + auto plan = + PlanBuilder().tableScan(rowType_).project(allColumns).planNode(); + auto result = + AssertQueryBuilder(plan).splits(splits).copyResults(opPool_.get()); + + ASSERT_GT(result->size(), 0) << "No rows found in the data file"; + + std::vector columnNames; + std::vector isAscending; + std::vector isNullsFirst; + std::vector columnIndices; + + for (const auto& sortExpr : sortColumns) { + std::string columnName; + bool ascending = true; + bool nullsFirst = true; + + std::istringstream iss(sortExpr); + iss >> columnName; + std::string token; + if (iss >> token) { + if (token == "DESC") { + ascending = false; + } else if (token != "ASC") { + iss.seekg(-(int32_t)token.length(), std::ios_base::cur); + } + + if (iss >> token && token == "NULLS") { + if (iss >> token && token == "LAST") { + nullsFirst = false; + } else if (token != "FIRST") { + ASSERT_TRUE(token == "FIRST") + << "Invalid NULLS ordering: " << token; + } + } + } + + int32_t columnIndex = -1; + for (auto i = 0; i < rowType_->size(); ++i) { + if (rowType_->nameOf(i) == columnName) { + columnIndex = i; + break; + } + } + ASSERT_NE(columnIndex, -1) + << "Column " << columnName << " not found in row type"; + + columnNames.push_back(columnName); + isAscending.push_back(ascending); + isNullsFirst.push_back(nullsFirst); + columnIndices.push_back(columnIndex); + } + + // Verify the sort order row by row. + for (auto i = 1; i < result->size(); ++i) { + // Compare row i-1 with row i using all sort columns in order. + for (size_t colIdx = 0; colIdx < columnIndices.size(); ++colIdx) { + int32_t columnIndex = columnIndices[colIdx]; + auto columnVector = result->childAt(columnIndex); + bool ascending = isAscending[colIdx]; + bool nullsFirst = isNullsFirst[colIdx]; + bool prevIsNull = columnVector->isNullAt(i - 1); + bool currIsNull = columnVector->isNullAt(i); + + if (prevIsNull && currIsNull) { + // Both null, continue to next column. + continue; + } else if (prevIsNull) { + // Previous is null, current is not. + ASSERT_TRUE(nullsFirst) + << "NULL values should come last at row " << (i - 1) + << " for column " << columnNames[colIdx] << " in " << dataPath; + break; + } else if (currIsNull) { + // Current is null, previous is not. + ASSERT_FALSE(nullsFirst) + << "NULL values should come first at row " << i << " for column " + << columnNames[colIdx] << " in " << dataPath; + break; + } + + // Both values are non-null, compare them. + DecodedVector decoded; + SelectivityVector rows(columnVector->size()); + decoded.decode(*columnVector, rows); + + int32_t comparison = 0; + switch (auto kind = rowType_->childAt(columnIndex)->kind()) { + case TypeKind::INTEGER: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::BIGINT: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::VARCHAR: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::VARBINARY: { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + break; + } + case TypeKind::HUGEINT: { + if (rowType_->childAt(columnIndex)->isLongDecimal()) { + auto prev = decoded.valueAt(i - 1); + auto curr = decoded.valueAt(i); + comparison = prev < curr ? -1 : (prev > curr ? 1 : 0); + } + break; + } + default: + ASSERT_TRUE(false) + << "Unsupported column type for sorting verification: " << kind; + } + + if (comparison != 0) { + if (ascending) { + ASSERT_LE(comparison, 0) + << "Data not sorted in ascending order at row " << i + << " for column " << columnNames[colIdx] << " in " << dataPath + << ". Previous value: " << columnVector->toString(i - 1) + << ", Current value: " << columnVector->toString(i); + } else { + ASSERT_GE(comparison, 0) + << "Data not sorted in descending order at row " << i + << " for column " << columnNames[colIdx] << " in " << dataPath + << ". Previous value: " << columnVector->toString(i - 1) + << ", Current value: " << columnVector->toString(i); + } + // Found definitive ordering, no need to check further columns. + break; + } + // If values are equal, continue to next column. + } + // Rows can be equal across all sort columns. + } + } + + std::vector stringBuffer_; + VectorFuzzer::Options fuzzerOptions_; + std::unique_ptr fuzzer_; + static constexpr auto numBatches = 10; + static constexpr auto rowsPerBatch = 1'000; + + void testSorting( + const std::vector& sortExpressions, + double nullRatio = 0.0) { + std::vector vectors = + createTestData(numBatches, rowsPerBatch, nullRatio); + auto outputDirectory = TempDirectoryPath::create(); + + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), {}, sortExpressions); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + if (sortExpressions.size() == 1) { + verifySortOrder(outputDirectory->getPath(), sortExpressions); + } else { + verifyMultiColumnSortOrder(outputDirectory->getPath(), sortExpressions); + } + } + + void testSortingWithPartitioning( + const std::vector& partitionTransforms, + const std::vector& sortExpressions, + const double nullRatio = 0.0) { + std::vector vectors = + createTestData(numBatches, rowsPerBatch, nullRatio); + const auto outputDirectory = TempDirectoryPath::create(); + + auto dataSink = createIcebergDataSink( + rowType_, + outputDirectory->getPath(), + partitionTransforms, + sortExpressions); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + // For partitioned data, we need to find all partition directories. + std::vector partitionDirs; + std::function findLeafDataDirs = + [&partitionDirs, &findLeafDataDirs](const std::string& dir) { + bool hasSubDirs = false; + + for (const auto& entry : std::filesystem::directory_iterator(dir)) { + if (entry.is_directory()) { + hasSubDirs = true; + findLeafDataDirs(entry.path().string()); + } + } + if (!hasSubDirs) { + partitionDirs.push_back(dir); + } + }; + + // Start the recursive search from the data directory. + if (std::filesystem::exists(outputDirectory->getPath())) { + findLeafDataDirs(outputDirectory->getPath()); + } + if (partitionDirs.empty()) { + partitionDirs.push_back(outputDirectory->getPath()); + } + + // Verify each partition directory has properly sorted data. + ASSERT_FALSE(partitionDirs.empty()) << "No partition directories found"; + for (const auto& partitionDir : partitionDirs) { + if (sortExpressions.size() == 1) { + verifySortOrder(partitionDir, sortExpressions); + } else { + verifyMultiColumnSortOrder(partitionDir, sortExpressions); + } + } + } +}; + +TEST_F(IcebergSortOrderTest, singleColumnSortDefault) { + testSorting({"c_int"}); + testSorting({"c_bigint"}); + testSorting({"c_varchar"}); + testSorting({"c_date"}); + testSorting({"c_decimal"}); + testSorting({"c_varbinary"}); +} + +TEST_F(IcebergSortOrderTest, singleColumnSortDesc) { + testSorting({"c_int DESC"}); + testSorting({"c_bigint DESC"}); + testSorting({"c_varchar DESC"}); + testSorting({"c_date DESC"}); + testSorting({"c_decimal DESC"}); + testSorting({"c_varbinary DESC"}); +} + +TEST_F(IcebergSortOrderTest, nullOrderingFirst) { + testSorting({"c_int ASC NULLS FIRST"}, 0.2); + testSorting({"c_bigint ASC NULLS FIRST"}, 0.2); + testSorting({"c_varchar ASC NULLS FIRST"}, 0.3); + testSorting({"c_date ASC NULLS FIRST"}, 0.3); + testSorting({"c_decimal ASC NULLS FIRST"}, 0.2); + testSorting({"c_varbinary ASC NULLS FIRST"}, 0.2); + + testSorting({"c_varbinary DESC NULLS FIRST"}, 0.2); + testSorting({"c_int DESC NULLS FIRST"}, 0.2); + testSorting({"c_bigint DESC NULLS FIRST"}, 0.2); + testSorting({"c_varchar DESC NULLS FIRST"}, 0.3); + testSorting({"c_date DESC NULLS FIRST"}, 0.3); + testSorting({"c_decimal DESC NULLS FIRST"}, 0.2); +} + +TEST_F(IcebergSortOrderTest, nullOrderingLast) { + testSorting({"c_int ASC NULLS LAST"}, 0.2); + testSorting({"c_bigint ASC NULLS LAST"}, 0.2); + testSorting({"c_varchar ASC NULLS LAST"}, 0.2); + testSorting({"c_date ASC NULLS LAST"}, 0.2); + testSorting({"c_decimal ASC NULLS LAST"}, 0.2); + testSorting({"c_varbinary ASC NULLS LAST"}, 0.2); + + testSorting({"c_varbinary DESC NULLS LAST"}, 0.2); + testSorting({"c_int DESC NULLS LAST"}, 0.2); + testSorting({"c_bigint DESC NULLS LAST"}, 0.2); + testSorting({"c_varchar DESC NULLS LAST"}, 0.2); + testSorting({"c_date DESC NULLS LAST"}, 0.2); + testSorting({"c_decimal DESC NULLS LAST"}, 0.2); +} + +TEST_F(IcebergSortOrderTest, multiColumnSort) { + testSorting({"c_int ASC", "c_bigint DESC"}); + testSorting({"c_int ASC", "c_bigint ASC"}); + testSorting({"c_int DESC", "c_bigint DESC"}); + testSorting({"c_int DESC", "c_bigint ASC"}); + + testSorting({"c_int ASC", "c_varchar DESC"}); + testSorting({"c_int ASC", "c_varchar ASC"}); + testSorting({"c_int DESC", "c_varchar DESC"}); + testSorting({"c_int DESC", "c_varchar ASC"}); + + testSorting({"c_varchar ASC", "c_date DESC"}); + testSorting({"c_varchar ASC", "c_date ASC"}); + testSorting({"c_varchar DESC", "c_date DESC"}); + testSorting({"c_varchar DESC", "c_date ASC"}); + + testSorting({"c_int ASC", "c_decimal DESC"}); + testSorting({"c_decimal ASC", "c_varbinary DESC"}); + testSorting({"c_varbinary ASC", "c_decimal DESC"}); +} + +TEST_F(IcebergSortOrderTest, multiColumnSortWithNull) { + testSorting({"c_int", "c_bigint", "c_varchar"}, 0.2); + testSorting({"c_int", "c_bigint DESC NULLS LAST"}, 0.4); + testSorting( + {"c_int ASC NULLS FIRST", + "c_bigint DESC NULLS LAST", + "c_varchar ASC NULLS FIRST"}, + 0.2); + testSorting( + {"c_int ASC NULLS FIRST", "c_bigint", "c_varchar ASC NULLS FIRST"}, 0.2); + testSorting( + {"c_int ASC NULLS LAST", "c_bigint", "c_varchar ASC NULLS FIRST"}, 0.2); + testSorting( + {"c_int DESC NULLS LAST", "c_bigint", "c_varchar ASC NULLS LAST"}, 0.2); + testSorting( + {"c_int ASC NULLS FIRST", "c_bigint DESC", "c_varchar ASC NULLS FIRST"}, + 0.2); + testSorting( + {"c_int ASC NULLS FIRST", + "c_bigint DESC NULLS LAST", + "c_varchar ASC NULLS FIRST"}, + 0.2); + testSorting( + {"c_int ASC NULLS FIRST", "c_bigint DESC", "c_varchar ASC NULLS LAST"}, + 0.2); + testSorting( + {"c_int ASC NULLS FIRST", + "c_bigint DESC NULLS LAST", + "c_varchar DESC NULLS FIRST"}, + 0.2); + + testSorting( + {"c_int ASC NULLS FIRST", + "c_decimal DESC NULLS LAST", + "c_varbinary ASC NULLS FIRST"}, + 0.2); +} + +TEST_F(IcebergSortOrderTest, sortWithSinglePartitioning) { + testSortingWithPartitioning({{3, TransformType::kBucket, 5}}, {"c_int ASC"}); + testSortingWithPartitioning( + {{0, TransformType::kBucket, 7}}, {"c_varchar ASC"}); +} + +TEST_F(IcebergSortOrderTest, sortWithPartitioningOnSameColumn) { + testSortingWithPartitioning({{3, TransformType::kBucket, 5}}, {"c_date ASC"}); + testSortingWithPartitioning({{0, TransformType::kBucket, 7}}, {"c_int ASC"}); + testSortingWithPartitioning( + {{2, TransformType::kBucket, 4}}, {"c_varchar DESC"}); +} + +TEST_F(IcebergSortOrderTest, sortWithMultiPartitioning) { + testSortingWithPartitioning( + {{3, TransformType::kBucket, 3}, {2, TransformType::kBucket, 4}}, + {"c_int ASC", "c_bigint DESC"}); + + testSortingWithPartitioning( + {{3, TransformType::kTruncate, 10000}}, {"c_int ASC", "c_bigint DESC"}); +} + +TEST_F(IcebergSortOrderTest, sortWithPartitioningAndNulls) { + testSortingWithPartitioning( + {{0, TransformType::kBucket, 8}}, + {"c_int ASC NULLS FIRST", "c_bigint DESC NULLS LAST"}, + 0.2); + + testSortingWithPartitioning( + {{2, TransformType::kBucket, 8}}, + {"c_varchar ASC NULLS FIRST", "c_int DESC NULLS LAST"}, + 0.2); + + testSortingWithPartitioning( + {{4, TransformType::kBucket, 8}}, + {"c_decimal ASC NULLS FIRST", "c_int DESC NULLS LAST"}, + 0.3); + + testSortingWithPartitioning( + {{5, TransformType::kBucket, 8}}, + {"c_varbinary ASC NULLS FIRST", "c_int DESC NULLS LAST"}, + 0.3); +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergSplitReaderBenchmark.cpp b/velox/connectors/hive/iceberg/tests/IcebergSplitReaderBenchmark.cpp index 2c2c26297fd2..d835d7239b71 100644 --- a/velox/connectors/hive/iceberg/tests/IcebergSplitReaderBenchmark.cpp +++ b/velox/connectors/hive/iceberg/tests/IcebergSplitReaderBenchmark.cpp @@ -15,8 +15,13 @@ */ #include "velox/connectors/hive/iceberg/tests/IcebergSplitReaderBenchmark.h" + #include +#include + +#include "velox/vector/tests/utils/VectorMaker.h" + using namespace facebook::velox; using namespace facebook::velox::dwio; using namespace facebook::velox::dwio::common; @@ -330,9 +335,14 @@ void IcebergSplitReaderBenchmark::readSingleColumn( suspender.dismiss(); + auto ioExecutor = std::make_unique(3); + std::shared_ptr remainingFilterExprSet{nullptr}; + std::atomic totalRemainingFilterMs; + uint64_t resultSize = 0; for (std::shared_ptr split : splits) { scanSpec->resetCachedValues(true); + std::unique_ptr icebergSplitReader = std::make_unique( split, @@ -344,8 +354,10 @@ void IcebergSplitReaderBenchmark::readSingleColumn( ioStats, fsStats, &fileHandleFactory, - nullptr, - scanSpec); + ioExecutor.get(), + scanSpec, + connectorQueryCtx_->expressionEvaluator(), + totalRemainingFilterMs); std::shared_ptr randomSkip; icebergSplitReader->configureReaderOptions(randomSkip); diff --git a/velox/connectors/hive/iceberg/tests/IcebergStatsTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergStatsTest.cpp new file mode 100644 index 000000000000..2d0ea85b33a4 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergStatsTest.cpp @@ -0,0 +1,1303 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" + +namespace facebook::velox::connector::hive::iceberg::test { +class IcebergStatsTest : public IcebergTestBase { + protected: + void SetUp() override { + IcebergTestBase::SetUp(); + rowType_ = + ROW({"c_int", "c_bigint", "c_varchar", "c_date", "c_decimal"}, + {INTEGER(), BIGINT(), VARCHAR(), DATE(), DECIMAL(18, 3)}); + } + + void TearDown() override { + IcebergTestBase::TearDown(); + } +}; + +TEST_F(IcebergStatsTest, mixedNullTest) { + auto rowType = ROW({"int_col"}, {INTEGER()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedIntNulls = 34; + + auto rowVector = makeRowVector({makeFlatVector( + size, [](vector_size_t row) { return row * 10; }, nullEvery(3))}); + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t intColId = 1; + EXPECT_EQ(stats->valueCounts.at(intColId), size) + << "Int column value count incorrect"; + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + + EXPECT_EQ(stats->nullValueCounts.at(intColId), expectedIntNulls) + << "Int column null count incorrect"; + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(intColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_EQ(lb, 10); + EXPECT_FALSE(stats->lowerBounds.at(intColId).empty()) + << "Int column should have non-empty lower bound"; + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(intColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_EQ(ub, 980); + EXPECT_FALSE(stats->upperBounds.at(intColId).empty()) + << "Int column should have non-empty upper bound"; +} + +TEST_F(IcebergStatsTest, bigintStatsTest) { + auto rowType = ROW({"bigint_col"}, {BIGINT()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 25; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [](vector_size_t row) { return row * 1'000'000'000LL; }, + nullEvery(4))}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t bigintColId = 1; + EXPECT_EQ(stats->valueCounts.at(bigintColId), size) + << "Bigint column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(bigintColId), expectedNulls) + << "Bigint column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(bigintColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_EQ(lb, 1'000'000'000LL); + + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(bigintColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_EQ(ub, 99'000'000'000LL); + folly::dynamic json = stats->toJson(); + std::string jsonstring = folly::toJson(json); +} + +TEST_F(IcebergStatsTest, decimalStatsTest) { + auto rowType = ROW({"decimal_col"}, {DECIMAL(38, 3)}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 20; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [](vector_size_t row) { return HugeInt::build(row, row * 123); }, + nullEvery(5), + DECIMAL(38, 3))}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t decimalColId = 1; + EXPECT_EQ(stats->valueCounts.at(decimalColId), size) + << "Decimal column value count incorrect"; + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(decimalColId), expectedNulls) + << "Decimal column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + EXPECT_FALSE(stats->lowerBounds.at(decimalColId).empty()) + << "Decimal column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(decimalColId).empty()) + << "Decimal column should have non-empty upper bound"; +} + +TEST_F(IcebergStatsTest, varcharStatsTest) { + auto rowType = ROW({"varchar_col"}, {VARCHAR()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 0; + + auto varcharVector = BaseVector::create(VARCHAR(), size, opPool_.get()); + auto flatVarcharVector = varcharVector->asFlatVector(); + for (auto i = 0; i < size; ++i) { + if (i % 6 == 0) { + flatVarcharVector->setNull(i, true); + expectedNulls++; + } else { + std::string value = + "Customer#00000" + std::to_string(i) + "_" + std::string(i % 10, 'a'); + flatVarcharVector->set(i, StringView(value)); + } + } + + auto rowVector = makeRowVector({varcharVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t varcharColId = 1; + EXPECT_EQ(stats->valueCounts.at(varcharColId), size) + << "Varchar column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(varcharColId), expectedNulls) + << "Varchar column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + EXPECT_FALSE(stats->lowerBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty upper bound"; + + // Decode and verify string bounds. + std::string lowerBound = + encoding::Base64::decode(stats->lowerBounds.at(varcharColId)); + std::string upperBound = + encoding::Base64::decode(stats->upperBounds.at(varcharColId)); + EXPECT_TRUE(lowerBound.find("Customer#00000") != std::string::npos) + << "Lower bound should contain 'Customer#00000'"; + EXPECT_TRUE(upperBound.find("Customer#000009") != std::string::npos) + << "Upper bound should contain 'Customer#000009'"; +} + +TEST_F(IcebergStatsTest, varbinaryStatsTest) { + auto rowType = ROW({"varbinary_col"}, {VARBINARY()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 0; + + auto varbinaryVector = BaseVector::create(VARBINARY(), size, opPool_.get()); + auto flatVarbinaryVector = varbinaryVector->asFlatVector(); + for (auto i = 0; i < size; ++i) { + if (i % 5 == 0) { + flatVarbinaryVector->setNull(i, true); + expectedNulls++; + } else { + // Create binary values with varying content. + std::string value(17, 11); + value[0] = static_cast(i % 256); + value[1] = static_cast((i * 3) % 256); + value[2] = static_cast((i * 7) % 256); + value[3] = static_cast((i * 11) % 256); + flatVarbinaryVector->set(i, StringView(value)); + } + } + + auto rowVector = makeRowVector({varbinaryVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t varbinaryColId = 1; + EXPECT_EQ(stats->valueCounts.at(varbinaryColId), size) + << "Varbinary column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(varbinaryColId), expectedNulls) + << "Varbinary column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + EXPECT_FALSE(stats->lowerBounds.at(varbinaryColId).empty()) + << "Varbinary column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(varbinaryColId).empty()) + << "Varbinary column should have non-empty upper bound"; +} + +TEST_F(IcebergStatsTest, varbinaryStatsTest2) { + auto rowType = ROW({"varbinary_col"}, {VARBINARY()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + std::vector partitionTransforms = { + {0, TransformType::kBucket, 4}}; + auto dataSink = + createIcebergDataSink(rowType, outputDir->getPath(), partitionTransforms); + constexpr vector_size_t size = 10; + + auto varbinaryVector = BaseVector::create(VARBINARY(), size, opPool_.get()); + auto flatVarbinaryVector = varbinaryVector->asFlatVector(); + std::string values[] = { + "01020304", + "05060708", + "090A0B0C", + "0D0E0F10", + "11121314", + "15161718", + "191A1B1C", + "1D1E1F20", + "21222324", + "25262728"}; + for (auto i = 0; i < size; ++i) { + flatVarbinaryVector->set(i, StringView(values[i])); + } + + auto rowVector = makeRowVector({"varbinary_col"}, {varbinaryVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 3) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, 5) << "Record count should match input size"; + + constexpr int32_t varbinaryColId = 1; + EXPECT_EQ(stats->valueCounts.at(varbinaryColId), 5); +} + +TEST_F(IcebergStatsTest, multipleDataTypesTest) { + auto rowType = ROW( + {"int_col", "bigint_col", "decimal_col", "varchar_col", "varbinary_col"}, + {INTEGER(), BIGINT(), DECIMAL(38, 3), VARCHAR(), VARBINARY()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + + int32_t expectedIntNulls = 34; + int32_t expectedBigintNulls = 25; + int32_t expectedDecimalNulls = 20; + int32_t expectedVarcharNulls = 0; + int32_t expectedVarbinaryNulls = 0; + // Create columns with different null patterns + auto intVector = makeFlatVector( + size, [](vector_size_t row) { return row * 10; }, nullEvery(3)); + + auto bigintVector = makeFlatVector( + size, + [](vector_size_t row) { return row * 1'000'000'000LL; }, + nullEvery(4)); + + auto decimalVector = makeFlatVector( + size, + [](vector_size_t row) { return HugeInt::build(row, row * 12'345); }, + nullEvery(5), + DECIMAL(38, 3)); + + auto varcharVector = BaseVector::create(VARCHAR(), size, opPool_.get()); + auto flatVarcharVector = varcharVector->asFlatVector(); + for (auto i = 0; i < size; ++i) { + if (i % 6 == 0) { + flatVarcharVector->setNull(i, true); + expectedVarcharNulls++; + } else { + std::string value = "str_" + std::to_string(i); + flatVarcharVector->set(i, StringView(value)); + } + } + + auto varbinaryVector = BaseVector::create(VARBINARY(), size, opPool_.get()); + auto flatVarbinaryVector = varbinaryVector->asFlatVector(); + for (auto i = 0; i < size; ++i) { + if (i % 7 == 0) { + flatVarbinaryVector->setNull(i, true); + expectedVarbinaryNulls++; + } else { + std::string value(4, 0); + value[0] = static_cast(i % 256); + value[1] = static_cast((i * 3) % 256); + value[2] = static_cast((i * 7) % 256); + value[3] = static_cast((i * 11) % 256); + flatVarbinaryVector->set(i, StringView(value)); + } + } + + auto rowVector = makeRowVector( + {intVector, bigintVector, decimalVector, varcharVector, varbinaryVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + + constexpr int32_t intColId = 1; + constexpr int32_t bigintColId = 2; + constexpr int32_t decimalColId = 3; + constexpr int32_t varcharColId = 4; + constexpr int32_t varbinaryColId = 5; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(intColId), expectedIntNulls) + << "Int column null count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(bigintColId), expectedBigintNulls) + << "Bigint column null count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(decimalColId), expectedDecimalNulls) + << "Decimal column null count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(varcharColId), expectedVarcharNulls) + << "Varchar column null count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(varbinaryColId), expectedVarbinaryNulls) + << "Varbinary column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + // Verify all columns have non-empty bounds. + EXPECT_FALSE(stats->lowerBounds.at(intColId).empty()) + << "Int column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(intColId).empty()) + << "Int column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(bigintColId).empty()) + << "Bigint column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(bigintColId).empty()) + << "Bigint column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(decimalColId).empty()) + << "Decimal column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(decimalColId).empty()) + << "Decimal column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(varbinaryColId).empty()) + << "Varbinary column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(varbinaryColId).empty()) + << "Varbinary column should have non-empty upper bound"; +} + +TEST_F(IcebergStatsTest, dateStatsTest) { + auto rowType = ROW({"date_col"}, {DATE()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 20; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [](vector_size_t row) { return 18262 + row; }, + nullEvery(5), + DATE())}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t dateColId = 1; + EXPECT_EQ(stats->valueCounts.at(dateColId), size) + << "Date column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(dateColId), expectedNulls) + << "Date column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + EXPECT_FALSE(stats->lowerBounds.at(dateColId).empty()) + << "Date column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(dateColId).empty()) + << "Date column should have non-empty upper bound"; + + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(dateColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_EQ(lb, 18263) << "Lower bound should be 2020-01-02"; + + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(dateColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_EQ(ub, 18262 + 99) << "Upper bound should be 2020-04-09"; +} + +TEST_F(IcebergStatsTest, booleanStatsTest) { + auto rowType = ROW({"boolean_col"}, {BOOLEAN()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 10; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [](vector_size_t row) { return row % 2 == 1; }, + nullEvery(10), + BOOLEAN())}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t boolColId = 1; + EXPECT_EQ(stats->valueCounts.at(boolColId), size) + << "Boolean column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(boolColId), expectedNulls) + << "Boolean column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + EXPECT_FALSE(stats->lowerBounds.at(boolColId).empty()) + << "Boolean column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(boolColId).empty()) + << "Boolean column should have non-empty upper bound"; + + // For boolean, the lower bound should be false (0) and upper bound should be + // true (1) if both values are present. + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(boolColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_FALSE(lb) << "Lower bound should be false"; + + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(boolColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_TRUE(ub) << "Upper bound should be true"; +} + +TEST_F(IcebergStatsTest, emptyStatsTest) { + auto rowType = ROW({"int_col", "varchar_col"}, {INTEGER(), VARCHAR()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + // Create an empty row vector (0 rows) + constexpr vector_size_t size = 0; + auto rowVector = makeRowVector( + {makeFlatVector(0), makeFlatVector(0)}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + EXPECT_EQ(stats->numRecords, size) << "Record count should be 0"; + ASSERT_TRUE(stats->valueCounts.empty()) + << "Should no value counts for columns"; +} + +TEST_F(IcebergStatsTest, nullValuesTest) { + auto rowType = ROW({"int_col", "varchar_col"}, {INTEGER(), VARCHAR()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + // Create an empty row vector (0 rows) + constexpr vector_size_t size = 100; + auto rowVector = makeRowVector( + {makeNullConstant(TypeKind::INTEGER, size), + makeNullConstant(TypeKind::VARCHAR, size)}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + EXPECT_EQ(stats->numRecords, size) << "Record count should be 0"; + ASSERT_EQ(stats->nullValueCounts.at(1), size) << "All values is NULL."; + // Do not collect lower and upper bounds for NULLs. + ASSERT_EQ(stats->lowerBounds.size(), 0); + ASSERT_EQ(stats->upperBounds.size(), 0); +} + +TEST_F(IcebergStatsTest, realStatsTest) { + auto rowType = ROW({"real_col"}, {REAL()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 20; + auto expectedNaNs = 0; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [&](vector_size_t row) { + if (row % 6 == 0) { + expectedNaNs++; + return std::numeric_limits::quiet_NaN(); + } + return row * 1.5f; + }, + nullEvery(5), + REAL())}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t realColId = 1; + EXPECT_EQ(stats->valueCounts.at(realColId), size) + << "Real column value count incorrect"; + + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(realColId), expectedNulls) + << "Real column null count incorrect"; + EXPECT_EQ(stats->nanValueCounts.at(realColId), expectedNaNs) + << "Real column null count incorrect"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(realColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_FLOAT_EQ(lb, 1.5f) << "Lower bound should be 1.5"; + + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(realColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_FLOAT_EQ(ub, 148.5f) << "Upper bound should be 148.5"; +} + +TEST_F(IcebergStatsTest, doubleStatsTest) { + auto rowType = ROW({"double_col"}, {DOUBLE()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 15; + auto expectedNaNs = 0; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [&](vector_size_t row) { + if (row % 3 == 0) { + expectedNaNs++; + return std::numeric_limits::quiet_NaN(); + } + if (row % 4 == 0) { + return std::numeric_limits::infinity(); + } + if (row % 5 == 0) { + return -std::numeric_limits::infinity(); + } + return row * 2.5; + }, + nullEvery(7), + DOUBLE())}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t doubleColId = 1; + EXPECT_EQ(stats->valueCounts.at(doubleColId), size) + << "Double column value count incorrect"; + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(doubleColId), expectedNulls) + << "Double column null count incorrect"; + EXPECT_EQ(stats->nanValueCounts.at(doubleColId), expectedNaNs) + << "Double column null count incorrect"; + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + // Verify bounds are set correctly and NaN/infinity values don't affect + // min/max incorrectly. + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(doubleColId)); + auto lb = *reinterpret_cast(lowerBounds.data()); + EXPECT_DOUBLE_EQ(lb, -std::numeric_limits::infinity()) + << "Lower bound should be -infinity"; + + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(doubleColId)); + auto ub = *reinterpret_cast(upperBounds.data()); + EXPECT_DOUBLE_EQ(ub, std::numeric_limits::infinity()) + << "Upper bound should be infinity"; +} + +TEST_F(IcebergStatsTest, NaNStatsTest) { + auto rowType = ROW({"double_col"}, {DOUBLE()}); + + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 1'000; + auto expectedNulls = 500; + auto expectedNaNs = 0; + + auto rowVector = makeRowVector({makeFlatVector( + size, + [&](vector_size_t row) { + expectedNaNs++; + return std::numeric_limits::quiet_NaN(); + }, + nullEvery(2), + DOUBLE())}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + constexpr int32_t doubleColId = 1; + EXPECT_EQ(stats->valueCounts.at(doubleColId), size) + << "Double column value count incorrect"; + ASSERT_FALSE(stats->nullValueCounts.empty()) + << "Should have null counts for columns"; + EXPECT_EQ(stats->nullValueCounts.at(doubleColId), expectedNulls) + << "Double column null count incorrect"; + EXPECT_EQ(stats->nanValueCounts.at(doubleColId), expectedNaNs) + << "Double column null count incorrect"; + + // Do not collect bounds for NULLs and NaNs. + ASSERT_TRUE(stats->lowerBounds.empty()) + << "Should not have lower bounds for columns"; + ASSERT_TRUE(stats->upperBounds.empty()) + << "Should not have upper bounds for columns"; +} + +TEST_F(IcebergStatsTest, partitionedTableStatsTest) { + auto rowType = ROW( + {"int_col", "date_col", "varchar_col"}, {INTEGER(), DATE(), VARCHAR()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + std::vector partitionTransforms = { + {0, TransformType::kBucket, 4}, + {1, TransformType::kDay, std::nullopt}, + {2, TransformType::kTruncate, 2}}; + + auto dataSink = + createIcebergDataSink(rowType, outputDir->getPath(), partitionTransforms); + + constexpr vector_size_t size = 100; + + auto intVector = + makeFlatVector(size, [](vector_size_t row) { return row; }); + + auto dateVector = makeFlatVector( + size, + [](vector_size_t row) { return 18262 + (row % 5); }, + nullptr, + DATE()); + + auto varcharVector = BaseVector::create(VARCHAR(), size, opPool_.get()); + auto flatVarcharVector = varcharVector->asFlatVector(); + + for (auto i = 0; i < size; ++i) { + std::string str = fmt::format("str{}", i % 10); + flatVarcharVector->set(i, StringView(str.c_str(), str.size())); + } + + auto rowVector = + makeRowVector(rowType->names(), {intVector, dateVector, varcharVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + // We should have multiple files due to partitioning. + ASSERT_FALSE(fileStats.empty()) << "Should have statistics for files"; + EXPECT_GT(fileStats.size(), 1) + << "Expected multiple files due to partitioning"; + + for (const auto& stats : fileStats) { + EXPECT_GE(stats->numRecords, 0) + << "Each partition file should have records"; + ASSERT_FALSE(stats->valueCounts.empty()) + << "Should have value counts for columns"; + + constexpr int32_t intColId = 1; + constexpr int32_t dateColId = 2; + constexpr int32_t varcharColId = 3; + EXPECT_EQ(stats->valueCounts.at(intColId), stats->numRecords) + << "Integer column value count should match record count"; + EXPECT_EQ(stats->valueCounts.at(dateColId), stats->numRecords) + << "Date column value count should match record count"; + EXPECT_EQ(stats->valueCounts.at(varcharColId), stats->numRecords) + << "Varchar column value count should match record count"; + + ASSERT_FALSE(stats->lowerBounds.empty()) + << "Should have lower bounds for columns"; + ASSERT_FALSE(stats->upperBounds.empty()) + << "Should have upper bounds for columns"; + + EXPECT_FALSE(stats->lowerBounds.at(intColId).empty()) + << "Int column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(intColId).empty()) + << "Int column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(dateColId).empty()) + << "Date column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(dateColId).empty()) + << "Date column should have non-empty upper bound"; + + EXPECT_FALSE(stats->lowerBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty lower bound"; + EXPECT_FALSE(stats->upperBounds.at(varcharColId).empty()) + << "Varchar column should have non-empty upper bound"; + } + + // Verify total record count across all partitions. + auto totalRecords = 0; + for (const auto& stats : fileStats) { + totalRecords += stats->numRecords; + } + EXPECT_EQ(totalRecords, size) + << "Total records across all partitions should match input size"; +} + +TEST_F(IcebergStatsTest, multiplePartitionTransformsStatsTest) { + auto rowType = + ROW({"int_col", "date_col", "varchar_col", "bigint_col"}, + {INTEGER(), DATE(), VARCHAR(), BIGINT()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + + std::vector partitionTransforms = { + {0, TransformType::kBucket, 2}, + {1, TransformType::kYear, std::nullopt}, + {2, TransformType::kTruncate, 3}, + {3, TransformType::kIdentity, std::nullopt}}; + + auto dataSink = + createIcebergDataSink(rowType, outputDir->getPath(), partitionTransforms); + + constexpr vector_size_t size = 100; + auto intVector = + makeFlatVector(size, [](vector_size_t row) { return row * 10; }); + auto flatIntVector = intVector->asFlatVector(); + + auto dateVector = makeFlatVector( + size, + [](vector_size_t row) { return 18262 + (row * 100); }, + nullptr, + DATE()); + + auto varcharVector = BaseVector::create(VARCHAR(), size, opPool_.get()); + auto flatVarcharVector = varcharVector->asFlatVector(); + for (auto i = 0; i < size; ++i) { + std::string str = fmt::format("prefix{}_value", i % 5); + flatVarcharVector->set(i, StringView(str.c_str(), str.size())); + } + auto bigintVector = makeFlatVector( + size, [](vector_size_t row) { return (row % 3) * 1'000; }); + + auto rowVector = makeRowVector( + rowType->names(), {intVector, dateVector, varcharVector, bigintVector}); + + dataSink->appendData(rowVector); + + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_FALSE(fileStats.empty()) << "Should have statistics for files"; + EXPECT_GT(fileStats.size(), 1) + << "Expected multiple files due to partitioning"; + // Check each file's stats + for (const auto& stats : fileStats) { + EXPECT_GT(stats->numRecords, 0) + << "Each partition file should have records"; + constexpr int32_t intColId = 1; + constexpr int32_t dateColId = 2; + constexpr int32_t bigintColId = 4; + + if (stats->lowerBounds.find(intColId) != stats->lowerBounds.end()) { + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(intColId)); + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(intColId)); + + auto lb = *reinterpret_cast(lowerBounds.data()); + auto ub = *reinterpret_cast(upperBounds.data()); + + EXPECT_LE(lb, ub) + << "Lower bound should be <= upper bound for int column"; + } + + if (stats->lowerBounds.find(dateColId) != stats->lowerBounds.end()) { + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(dateColId)); + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(dateColId)); + + auto lb = *reinterpret_cast(lowerBounds.data()); + auto ub = *reinterpret_cast(upperBounds.data()); + + EXPECT_LE(lb, ub) + << "Lower bound should be <= upper bound for date column"; + } + + if (stats->lowerBounds.find(bigintColId) != stats->lowerBounds.end()) { + std::string lowerBounds = + encoding::Base64::decode(stats->lowerBounds.at(bigintColId)); + std::string upperBounds = + encoding::Base64::decode(stats->upperBounds.at(bigintColId)); + + auto lb = *reinterpret_cast(lowerBounds.data()); + auto ub = *reinterpret_cast(upperBounds.data()); + + EXPECT_LE(lb, ub) + << "Lower bound should be <= upper bound for bigint column"; + } + } + auto totalRecords = 0; + for (const auto& stats : fileStats) { + totalRecords += stats->numRecords; + } + EXPECT_EQ(totalRecords, size) + << "Total records across all partitions should match input size"; +} + +TEST_F(IcebergStatsTest, partitionedTableWithNullsStatsTest) { + auto rowType = ROW( + {"int_col", "date_col", "varchar_col"}, {INTEGER(), DATE(), VARCHAR()}); + auto outputDir = exec::test::TempDirectoryPath::create(); + std::vector partitionTransforms = { + {0, TransformType::kIdentity, std::nullopt}, + {1, TransformType::kMonth, std::nullopt}, + {2, TransformType::kTruncate, 2}}; + auto dataSink = + createIcebergDataSink(rowType, outputDir->getPath(), partitionTransforms); + + constexpr vector_size_t size = 100; + auto expectedNulls = 20; + auto dateNulls = 15; + auto intVector = makeFlatVector( + size, + [](vector_size_t row) { return row % 10; }, + nullEvery(5), + INTEGER()); + auto dateVector = makeFlatVector( + size, + [](vector_size_t row) { return 18262 + (row % 3) * 30; }, + nullEvery(7), + DATE()); + auto varcharVector = BaseVector::create(VARCHAR(), size, opPool_.get()); + auto flatVarcharVector = varcharVector->asFlatVector(); + auto varcharNulls = 0; + for (auto i = 0; i < size; ++i) { + if (i % 11 == 0) { + flatVarcharVector->setNull(i, true); + varcharNulls++; + } else { + std::string str = fmt::format("val{}", i % 5); + flatVarcharVector->set(i, StringView(str.c_str(), str.size())); + } + } + + auto rowVector = + makeRowVector(rowType->names(), {intVector, dateVector, varcharVector}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_FALSE(fileStats.empty()) << "Should have statistics for files"; + auto totalIntNulls = 0; + auto totalDateNulls = 0; + auto totalVarcharNulls = 0; + auto totalRecords = 0; + + constexpr int32_t intColId = 1; + constexpr int32_t dateColId = 2; + constexpr int32_t varcharColId = 3; + + for (const auto& stats : fileStats) { + totalRecords += stats->numRecords; + // Add null counts if present. + if (stats->nullValueCounts.find(intColId) != stats->nullValueCounts.end()) { + totalIntNulls += stats->nullValueCounts.at(intColId); + } + + if (stats->nullValueCounts.find(dateColId) != + stats->nullValueCounts.end()) { + totalDateNulls += stats->nullValueCounts.at(dateColId); + } + + if (stats->nullValueCounts.find(varcharColId) != + stats->nullValueCounts.end()) { + totalVarcharNulls += stats->nullValueCounts.at(varcharColId); + } + + // Check that null count is less than or equal to value count for each + // column. + if (stats->nullValueCounts.find(intColId) != stats->nullValueCounts.end() && + stats->valueCounts.find(intColId) != stats->valueCounts.end()) { + EXPECT_LE( + stats->nullValueCounts.at(intColId), stats->valueCounts.at(intColId)) + << "Null count should be <= value count for int column"; + } + + if (stats->nullValueCounts.find(dateColId) != + stats->nullValueCounts.end() && + stats->valueCounts.find(dateColId) != stats->valueCounts.end()) { + EXPECT_LE( + stats->nullValueCounts.at(dateColId), + stats->valueCounts.at(dateColId)) + << "Null count should be <= value count for date column"; + } + + if (stats->nullValueCounts.find(varcharColId) != + stats->nullValueCounts.end() && + stats->valueCounts.find(varcharColId) != stats->valueCounts.end()) { + EXPECT_LE( + stats->nullValueCounts.at(varcharColId), + stats->valueCounts.at(varcharColId)) + << "Null count should be <= value count for varchar column"; + } + } + + // Verify total counts match expected. + EXPECT_EQ(totalRecords, size) + << "Total records across all partitions should match input size"; + EXPECT_EQ(totalIntNulls, expectedNulls) + << "Total int nulls should match expected"; + EXPECT_EQ(totalDateNulls, dateNulls) + << "Total date nulls should match expected"; + EXPECT_EQ(totalVarcharNulls, varcharNulls) + << "Total varchar nulls should match expected"; +} + +TEST_F(IcebergStatsTest, mapTypeTest) { + auto rowType = + ROW({"int_col", "map_col"}, {INTEGER(), MAP(INTEGER(), VARCHAR())}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 0; + + std::vector< + std::optional>>>> + mapData; + for (auto i = 0; i < size; ++i) { + std::vector>> mapRow; + for (auto j = 0; j < 5; ++j) { + mapRow.emplace_back(j, StringView("test_value")); + } + mapData.push_back(mapRow); + } + + auto rowVector = makeRowVector( + {makeFlatVector(size, [&](auto row) { return row * 10; }), + makeNullableMapVector(mapData)}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + constexpr int32_t intColId = 1; + constexpr int32_t mapColId = 3; + + EXPECT_EQ(stats->valueCounts.at(intColId), size) + << "Int column value count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(intColId), expectedNulls) + << "Int column null count incorrect"; + EXPECT_EQ(stats->valueCounts.at(mapColId), size * 5) + << "Map column value count incorrect"; + EXPECT_TRUE(stats->lowerBounds.find(mapColId) == stats->lowerBounds.end()) + << "Map column should not have lower bounds"; + EXPECT_TRUE(stats->upperBounds.find(mapColId) == stats->upperBounds.end()) + << "Map column should not have upper bounds"; +} + +TEST_F(IcebergStatsTest, arrayTypeTest) { + auto rowType = ROW({"int_col", "array_col"}, {INTEGER(), ARRAY(VARCHAR())}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 0; + + std::vector>> arrayData; + for (auto i = 0; i < size; ++i) { + std::vector> arrayRow; + for (auto j = 0; j < 3; ++j) { + auto v = fmt::format("item_{}", i * 3 + j); + arrayRow.emplace_back(StringView(v)); + } + arrayData.push_back(arrayRow); + } + + auto rowVector = makeRowVector( + {makeFlatVector(size, [](auto row) { return row * 10; }), + makeNullableArrayVector(arrayData)}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + constexpr int32_t intColId = 1; + constexpr int32_t arrayColId = 3; + + EXPECT_EQ(stats->valueCounts.at(intColId), size) + << "Int column value count incorrect"; + EXPECT_EQ(stats->nullValueCounts.at(intColId), expectedNulls) + << "Int column null count incorrect"; + EXPECT_EQ(stats->valueCounts.at(arrayColId), size * 3) + << "Array column value count incorrect"; + EXPECT_TRUE(stats->lowerBounds.find(arrayColId) == stats->lowerBounds.end()) + << "Array column should not have lower bounds"; + EXPECT_TRUE(stats->upperBounds.find(arrayColId) == stats->upperBounds.end()) + << "Array column should not have upper bounds"; +} + +// Test statistics collection for nested struct fields. +// Assume int_col's ID start with 1. +// Struct definition with field IDs: +// struct { +// int_col: INTEGER (id: 1) +// struct_col (id: 2) { +// first_level_id: INTEGER (id: 3) +// first_level_name: VARCHAR (id: 4) +// nested_struct (id: 5) { +// second_level_id: INTEGER (id: 6) +// second_level_name: VARCHAR (id: 7) +// } +// } +// } +// Need to collect statistics for field IDs [1, 3, 4, 6, 7] +TEST_F(IcebergStatsTest, structTypeTest) { + auto rowType = + ROW({"int_col", "struct_col"}, + {INTEGER(), + ROW({"first_level_id", "first_level_name", "nested_struct"}, + {INTEGER(), + VARCHAR(), + ROW({"second_level_id", "second_level_name"}, + {INTEGER(), VARCHAR()})})}); + auto outputDir = exec::test::TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink(rowType, outputDir->getPath()); + constexpr vector_size_t size = 100; + auto expectedNulls = 0; + + auto intVector = + makeFlatVector(size, [](auto row) { return row * 10; }); + auto firstLevelId = makeFlatVector( + size, [](vector_size_t row) { return row % size; }, nullEvery(5)); + auto firstLevelName = makeFlatVector( + size, + [](vector_size_t row) { + auto v = fmt::format("name_{}", row * 10); + return StringView(v); + }, + nullEvery(7)); + + auto secondLevelId = makeFlatVector( + size, [](vector_size_t row) { return row * size; }, nullEvery(6)); + auto secondLevelName = makeFlatVector( + size, + [](vector_size_t row) { + auto v = fmt::format("nested_{}", row * 100); + return StringView(v); + }, + nullEvery(8)); + + auto nestedStruct = makeRowVector({secondLevelId, secondLevelName}); + auto structVector = + makeRowVector({firstLevelId, firstLevelName, nestedStruct}); + + auto rowVector = makeRowVector({intVector, structVector}); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + auto commitTasks = dataSink->close(); + ASSERT_FALSE(commitTasks.empty()); + + const auto& fileStats = dataSink->dataFileStats(); + ASSERT_EQ(fileStats.size(), 1) << "Expected exactly one file with stats"; + const auto& stats = fileStats[0]; + + EXPECT_EQ(stats->numRecords, size) << "Record count should match input size"; + + constexpr int32_t intColId = 1; + constexpr int32_t tier1ColId = 3; + constexpr int32_t tier2ColId = 6; + constexpr int32_t tier2ColId2 = 7; + + EXPECT_EQ(stats->valueCounts.size(), 5); + EXPECT_EQ(stats->lowerBounds.size(), 5); + EXPECT_EQ(stats->valueCounts.at(intColId), size); + EXPECT_EQ(stats->nullValueCounts.at(intColId), expectedNulls); + EXPECT_EQ(stats->valueCounts.at(tier1ColId), size); + EXPECT_EQ(stats->valueCounts.at(tier2ColId), size); + EXPECT_EQ(stats->nullValueCounts.at(tier1ColId), 20); + EXPECT_EQ( + encoding::Base64::decode(stats->lowerBounds.at(tier2ColId2)), + "nested_100"); + EXPECT_EQ( + encoding::Base64::decode(stats->upperBounds.at(tier2ColId2)), + "nested_9900"); +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergTestBase.cpp b/velox/connectors/hive/iceberg/tests/IcebergTestBase.cpp new file mode 100644 index 000000000000..27ec7578b3cf --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergTestBase.cpp @@ -0,0 +1,311 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" + +#include + +#include "velox/connectors/hive/iceberg/PartitionSpec.h" + +namespace facebook::velox::connector::hive::iceberg::test { +void IcebergTestBase::SetUp() { + HiveConnectorTestBase::SetUp(); + parquet::registerParquetReaderFactory(); + parquet::registerParquetWriterFactory(); + Type::registerSerDe(); + + // Initialize session properties and config. + connectorSessionProperties_ = std::make_shared( + std::unordered_map(), true); + + connectorConfig_ = + std::make_shared(std::make_shared( + std::unordered_map())); + + setupMemoryPools("IcebergTestBase"); + + // Initialize vector fuzzer for test data generation. + fuzzerOptions_.vectorSize = 100; + fuzzerOptions_.nullRatio = 0.1; + fuzzer_ = std::make_unique(fuzzerOptions_, opPool_.get()); + + vectorMaker_ = + std::make_unique(opPool_.get()); +} + +void IcebergTestBase::TearDown() { + vectorMaker_.reset(); + fuzzer_.reset(); + connectorQueryCtx_.reset(); + connectorPool_.reset(); + opPool_.reset(); + root_.reset(); + HiveConnectorTestBase::TearDown(); +} + +void IcebergTestBase::setupMemoryPools(const std::string& name) { + root_.reset(); + opPool_.reset(); + connectorPool_.reset(); + connectorQueryCtx_.reset(); + + root_ = memory::memoryManager()->addRootPool( + name, 1L << 30, exec::MemoryReclaimer::create()); + opPool_ = root_->addLeafChild("operator"); + connectorPool_ = + root_->addAggregateChild("connector", exec::MemoryReclaimer::create()); + + connectorQueryCtx_ = std::make_unique( + opPool_.get(), + connectorPool_.get(), + connectorSessionProperties_.get(), + nullptr, + common::PrefixSortConfig(), + nullptr, + nullptr, + "query" + name, + "task" + name, + "planNodeId" + name, + 0, + ""); +} + +std::vector IcebergTestBase::createTestData( + int32_t numBatches, + vector_size_t rowsPerBatch, + double nullRatio) { + std::vector vectors; + vectors.reserve(numBatches); + + fuzzerOptions_.nullRatio = nullRatio; + fuzzerOptions_.allowDictionaryVector = false; + fuzzer_->setOptions(fuzzerOptions_); + + for (auto i = 0; i < numBatches; ++i) { + vectors.push_back(fuzzer_->fuzzRow(rowType_, rowsPerBatch, false)); + } + + return vectors; +} + +std::shared_ptr IcebergTestBase::createPartitionSpec( + const std::vector& partitionFields, + const RowTypePtr& rowType) { + std::vector fields; + for (const auto& partitionField : partitionFields) { + fields.push_back(IcebergPartitionSpec::Field( + rowType->nameOf(partitionField.id), + rowType->childAt(partitionField.id), + partitionField.type, + partitionField.parameter)); + } + + return std::make_shared(1, fields); +} + +void addColumnHandles( + const RowTypePtr& rowType, + const std::vector& partitionFields, + std::vector>& columnHandles) { + std::unordered_set partitionColumnIds; + for (const auto& field : partitionFields) { + partitionColumnIds.insert(field.id); + } + HiveColumnHandle::ColumnParseParameters columnParseParameters; + + std::function + collectNestedField = [&](const TypePtr& type, + int32_t& columnOrdinal) -> IcebergNestedField { + int32_t currentId = columnOrdinal++; + std::vector children; + if (type->isRow()) { + auto rowType = asRowType(type); + for (auto i = 0; i < rowType->size(); ++i) { + children.push_back( + collectNestedField(rowType->childAt(i), columnOrdinal)); + } + } else if (type->isArray()) { + auto arrayType = std::dynamic_pointer_cast(type); + for (auto i = 0; i < arrayType->size(); ++i) { + children.push_back( + collectNestedField(arrayType->childAt(i), columnOrdinal)); + } + } else if (type->isMap()) { + auto mapType = std::dynamic_pointer_cast(type); + for (auto i = 0; i < mapType->size(); ++i) { + children.push_back( + collectNestedField(mapType->childAt(i), columnOrdinal)); + } + } + + return IcebergNestedField{currentId, children}; + }; + + int32_t startIndex = 1; + for (auto i = 0; i < rowType->size(); ++i) { + auto columnName = rowType->nameOf(i); + auto type = rowType->childAt(i); + auto field = collectNestedField(type, startIndex); + columnHandles.push_back(std::make_shared( + columnName, + partitionColumnIds.count(i) > 0 + ? HiveColumnHandle::ColumnType::kPartitionKey + : HiveColumnHandle::ColumnType::kRegular, + type, + type, + field, + std::vector{}, + columnParseParameters)); + } +} + +std::shared_ptr +IcebergTestBase::createIcebergInsertTableHandle( + const RowTypePtr& rowType, + const std::string& outputDirectoryPath, + const std::vector& partitionFields, + const std::vector& sortedBy) { + std::vector> columnHandles; + addColumnHandles(rowType, partitionFields, columnHandles); + + auto locationHandle = std::make_shared( + outputDirectoryPath, + outputDirectoryPath, + LocationHandle::TableType::kNew); + + auto partitionSpec = createPartitionSpec(partitionFields, rowType); + + // Create sorting columns if specified + std::vector sortingColumns; + for (const auto& sortExpr : sortedBy) { + std::string columnName; + bool isAscending = true; + bool isNullsFirst = true; + + // Parse sort expression + std::istringstream iss(sortExpr); + iss >> columnName; + + std::string token; + if (iss >> token) { + if (token == "DESC") { + isAscending = false; + } else if (token != "ASC") { + // If not ASC, put it back (might be NULLS) + iss.seekg(-(int)token.length(), std::ios_base::cur); + } + + if (iss >> token && token == "NULLS") { + if (iss >> token && token == "LAST") { + isNullsFirst = false; + } + } + } + + core::SortOrder sortOrder(isAscending, isNullsFirst); + IcebergSortingColumn(columnName, sortOrder); + sortingColumns.push_back(IcebergSortingColumn(columnName, sortOrder)); + } + + return std::make_shared( + columnHandles, + locationHandle, + partitionSpec, + opPool_.get(), + fileFormat_, + sortingColumns, + common::CompressionKind::CompressionKind_ZSTD); +} + +std::shared_ptr IcebergTestBase::createIcebergDataSink( + const RowTypePtr& rowType, + const std::string& outputDirectoryPath, + const std::vector& partitionFields, + const std::vector& sortedBy) { + auto tableHandle = createIcebergInsertTableHandle( + rowType, outputDirectoryPath, partitionFields, sortedBy); + return std::make_shared( + rowType, + tableHandle, + connectorQueryCtx_.get(), + connector::CommitStrategy::kNoCommit, + connectorConfig_); +} + +std::vector IcebergTestBase::listFiles( + const std::string& dirPath) { + std::vector files; + if (!std::filesystem::exists(dirPath)) { + return files; + } + + for (auto& dirEntry : + std::filesystem::recursive_directory_iterator(dirPath)) { + if (dirEntry.is_regular_file()) { + files.push_back(dirEntry.path().string()); + } + } + return files; +} + +std::vector> +IcebergTestBase::createSplitsForDirectory(const std::string& directory) { + std::vector> splits; + std::unordered_map customSplitInfo; + customSplitInfo["table_format"] = "hive-iceberg"; + + auto files = listFiles(directory); + for (const auto& filePath : files) { + std::unordered_map> partitionKeys; + + // Extract partition keys from path if any. + std::vector pathComponents; + folly::split("/", filePath, pathComponents); + for (const auto& component : pathComponents) { + if (component.find('=') != std::string::npos) { + std::vector keys; + folly::split('=', component, keys); + if (keys.size() == 2) { + partitionKeys[keys[0]] = keys[1]; + if (keys[1] == "null") { + partitionKeys[keys[0]] = std::nullopt; + } + } + } + } + + const auto file = filesystems::getFileSystem(filePath, nullptr) + ->openFileForRead(filePath); + const auto fileSize = file->size(); + + splits.push_back(std::make_shared( + kHiveConnectorId, + filePath, + fileFormat_, + 0, + fileSize, + partitionKeys, + std::nullopt, + customSplitInfo, + nullptr, + true, + std::vector())); + } + + return splits; +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergTestBase.h b/velox/connectors/hive/iceberg/tests/IcebergTestBase.h new file mode 100644 index 000000000000..17ea4eea319f --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergTestBase.h @@ -0,0 +1,97 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +#include "velox/connectors/hive/iceberg/IcebergDataSink.h" +#include "velox/connectors/hive/iceberg/IcebergSplit.h" +#include "velox/exec/tests/utils/HiveConnectorTestBase.h" +#include "velox/exec/tests/utils/TempDirectoryPath.h" +#include "velox/vector/fuzzer/VectorFuzzer.h" +#ifdef VELOX_ENABLE_PARQUET +#include "velox/dwio/parquet/RegisterParquetWriter.h" +#include "velox/dwio/parquet/reader/ParquetReader.h" +#endif + +namespace facebook::velox::connector::hive::iceberg::test { + +struct PartitionField { + int32_t id; // Index of column in RowType, start from 0. + TransformType type; + std::optional parameter; // Optional parameter of transform. +}; + +class IcebergTestBase : public exec::test::HiveConnectorTestBase { + protected: + void SetUp() override; + + void TearDown() override; + + std::vector createTestData( + int32_t numBatches, + vector_size_t rowsPerBatch, + double nullRatio = 0.0); + + std::shared_ptr createIcebergDataSink( + const RowTypePtr& rowType, + const std::string& outputDirectoryPath, + const std::vector& partitionTransforms = {}, + const std::vector& sortedBy = {}); + + std::vector> createSplitsForDirectory( + const std::string& directory); + + std::vector listFiles(const std::string& dirPath); + + std::shared_ptr createPartitionSpec( + const std::vector& transformSpecs, + const RowTypePtr& rowType); + + private: + std::shared_ptr createIcebergInsertTableHandle( + const RowTypePtr& rowType, + const std::string& outputDirectoryPath, + const std::vector& partitionTransforms = {}, + const std::vector& sortedBy = {}); + + std::vector listPartitionDirectories( + const std::string& dataPath); + + void setupMemoryPools(const std::string& name); + + protected: + RowTypePtr rowType_; + std::shared_ptr opPool_; + + private: + static constexpr const char* kHiveConnectorId = "test-hive"; + + // The only supported file format is PARQUET. + dwio::common::FileFormat fileFormat_ = dwio::common::FileFormat::PARQUET; + + std::shared_ptr root_; + std::shared_ptr connectorPool_; + std::shared_ptr connectorSessionProperties_; + std::shared_ptr connectorConfig_; + std::unique_ptr connectorQueryCtx_; + VectorFuzzer::Options fuzzerOptions_; + std::unique_ptr fuzzer_; + std::unique_ptr vectorMaker_; +}; + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergTransformE2ETest.cpp b/velox/connectors/hive/iceberg/tests/IcebergTransformE2ETest.cpp new file mode 100644 index 000000000000..ea40b5c5a290 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergTransformE2ETest.cpp @@ -0,0 +1,941 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" +#include "velox/exec/tests/utils/PlanBuilder.h" +#include "velox/exec/tests/utils/TempDirectoryPath.h" + +using namespace facebook::velox::exec::test; + +namespace facebook::velox::connector::hive::iceberg::test { +class IcebergTransformE2ETest : public IcebergTestBase { + protected: + void SetUp() override { + IcebergTestBase::SetUp(); + rowType_ = + ROW({"c_int", + "c_bigint", + "c_varchar", + "c_date", + "c_decimal", + "c_varbinary", + "c_timestamp"}, + {INTEGER(), + BIGINT(), + VARCHAR(), + DATE(), + DECIMAL(18, 3), + VARBINARY(), + TIMESTAMP()}); + rng_.seed(1); + } + + std::pair buildFilter( + const std::string& partitionDir) { + const auto eq = partitionDir.find('='); + const auto us = partitionDir.rfind('_', eq - 1); + const auto column = partitionDir.substr(0, us); + const auto value = partitionDir.substr(eq + 1); + return {column, value}; + } + + std::vector createTestData( + int32_t numBatches, + int32_t rowsPerBatch) { + std::vector batches; + for (auto batchIdx = 0; batchIdx < numBatches; ++batchIdx) { + std::vector columns; + columns.push_back(makeFlatVector( + rowsPerBatch, [](auto row) { return row % 100; })); + columns.push_back(makeFlatVector( + rowsPerBatch, [](auto row) { return row * 1'000; })); + auto varcharVector = BaseVector::create>( + VARCHAR(), rowsPerBatch, opPool_.get()); + for (auto i = 0; i < rowsPerBatch; i++) { + std::string s = + fmt::format("string_long_data_test__{}__{}", i % 10, i % 100); + varcharVector->set(i, StringView(s)); + } + columns.push_back(varcharVector); + + auto dateVector = BaseVector::create>( + DATE(), rowsPerBatch, opPool_.get()); + for (auto i = 0; i < rowsPerBatch; i++) { + static const std::vector dates = { + 18'262, 18'628, 18'993, 19'358, 19'723, 20'181}; + dateVector->set(i, dates[i % dates.size()]); + } + columns.push_back(dateVector); + + auto decimalVector = BaseVector::create>( + DECIMAL(18, 3), rowsPerBatch, opPool_.get()); + for (auto i = 0; i < rowsPerBatch; i++) { + decimalVector->set(i, (i % 10 + 1) * 123'456); + } + columns.push_back(decimalVector); + + auto varbinaryVector = BaseVector::create>( + VARBINARY(), rowsPerBatch, opPool_.get()); + std::vector binaryData; + for (auto i = 0; i < rowsPerBatch; i++) { + if (i % 5 == 0) { + for (int32_t j = 0; j < 40; j++) { + binaryData.push_back(static_cast(j + (i % 10))); + } + } else if (i % 5 == 1) { + for (int32_t j = 0; j < 40; j++) { + binaryData.push_back(static_cast(i % 256)); + } + } else if (i % 5 == 2) { + for (int32_t j = 0; j < 40; j++) { + binaryData.push_back( + static_cast(j % 2 == 0 ? 0xAA : 0x55)); + } + } else { + for (int32_t j = 0; j < 34; j++) { + binaryData.push_back(static_cast(255 - j - (i % 10))); + } + } + + varbinaryVector->set( + i, + StringView( + reinterpret_cast(binaryData.data()), + binaryData.size())); + } + columns.push_back(varbinaryVector); + + // Add timestamp column with different time values. + auto timestampVector = BaseVector::create>( + TIMESTAMP(), rowsPerBatch, opPool_.get()); + for (auto i = 0; i < rowsPerBatch; i++) { + // Create timestamps for different years, months, days, and hours + // to test various transforms. + static const std::vector timestamps = { + Timestamp(0, 0), // 1970-01-01 00:00:00 + Timestamp(3600, 0), // 1970-01-01 01:00:00 + Timestamp(86400, 0), // 1970-01-02 00:00:00 + Timestamp(2592000, 0), // 1970-01-31 00:00:00 + Timestamp(31536000, 0), // 1971-01-01 00:00:00 + Timestamp(1609459200, 0), // 2021-01-01 00:00:00 + Timestamp(1609545600, 0), // 2021-01-02 00:00:00 + Timestamp(1612224000, 0), // 2021-02-01 00:00:00 + Timestamp(1640995200, 0), // 2022-01-01 00:00:00 + Timestamp(1672531200, 0) // 2023-01-01 00:00:00 + }; + timestampVector->set(i, timestamps[i % timestamps.size()]); + } + columns.push_back(timestampVector); + + batches.push_back(makeRowVector(rowType_->names(), columns)); + } + return batches; + } + + std::vector listFirstLevelDirectories( + const std::string& basePath) { + std::vector partitionDirs; + for (const auto& entry : std::filesystem::directory_iterator(basePath)) { + if (entry.is_directory()) { + partitionDirs.push_back(entry.path().string()); + } + } + return partitionDirs; + } + + std::vector listDirectoriesRecursively(const std::string& path) { + std::vector allDirs; + auto firstLevelDirs = listFirstLevelDirectories(path); + allDirs.insert(allDirs.end(), firstLevelDirs.begin(), firstLevelDirs.end()); + + for (const auto& dir : firstLevelDirs) { + if (std::filesystem::is_directory(dir)) { + auto subDirs = listDirectoriesRecursively(dir); + allDirs.insert(allDirs.end(), subDirs.begin(), subDirs.end()); + } + } + + return allDirs; + } + + // Verify the number of partitions and their naming convention. + void verifyPartitionCount( + const std::string& outputPath, + const std::vector& partitionTransforms, + const int32_t expectedPartitionCount) { + const auto partitionDirs = listFirstLevelDirectories(outputPath); + + if (partitionTransforms.empty()) { + ASSERT_EQ(partitionDirs.size(), 1) + << "Expected 1 directory for no partitioning, got " + << partitionDirs.size(); + } else { + ASSERT_EQ(partitionDirs.size(), expectedPartitionCount) + << "Expected " << expectedPartitionCount << " partitions, got " + << partitionDirs.size(); + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + ASSERT_TRUE(dirName.find('=') != std::string::npos) + << "Partition directory " << dirName + << " does not follow Iceberg naming convention"; + } + } + } + + // Verify the total row count across all partitions. + void verifyTotalRowCount( + RowTypePtr rowType, + const std::string& outputPath, + int32_t expectedRowCount) { + auto splits = createSplitsForDirectory(outputPath); + + const auto plan = PlanBuilder() + .tableScan(rowType) + .singleAggregation({}, {"count(1)"}) + .planNode(); + + const auto result = + AssertQueryBuilder(plan).splits(splits).copyResults(opPool_.get()); + + ASSERT_EQ(result->size(), 1); + ASSERT_EQ( + result->childAt(0)->asFlatVector()->valueAt(0), + expectedRowCount); + } + + // Verify data in a specific partition. + void verifyPartitionData( + RowTypePtr rowType, + const std::string& partitionPath, + const std::string& partitionFilter, + const int32_t expectedRowCount, + bool skipRowCountCheck = false) { + const auto splits = createSplitsForDirectory(partitionPath); + + const auto countPlan = PlanBuilder() + .tableScan(rowType) + .singleAggregation({}, {"count(1)"}) + .planNode(); + + const auto countResult = + AssertQueryBuilder(countPlan).splits(splits).copyResults(opPool_.get()); + + ASSERT_EQ(countResult->size(), 1); + const auto actualRowCount = + countResult->childAt(0)->asFlatVector()->valueAt(0); + + if (!skipRowCountCheck) { + ASSERT_EQ(actualRowCount, expectedRowCount); + } else { + // Just verify that we have some data. + ASSERT_GT(actualRowCount, 0); + } + + const auto dataPlan = PlanBuilder() + .tableScan(rowType) + .filter(partitionFilter) + .singleAggregation({}, {"count(1)"}) + .planNode(); + const auto dataResult = + AssertQueryBuilder(dataPlan).splits(splits).copyResults(opPool_.get()); + ASSERT_EQ(dataResult->size(), 1); + const auto filteredRowCount = + dataResult->childAt(0)->asFlatVector()->valueAt(0); + if (!skipRowCountCheck) { + ASSERT_EQ(filteredRowCount, expectedRowCount); + } else { + // Just verify that the filter matches all rows in the partition. + ASSERT_EQ(filteredRowCount, actualRowCount); + } + } + + folly::Random::DefaultGenerator rng_; +}; + +TEST_F(IcebergTransformE2ETest, identityPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink( + rowType_, + outputDirectory->getPath(), + {{0, TransformType::kIdentity, std::nullopt}}); + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + verifyPartitionCount(outputDirectory->getPath(), {"c_int"}, rowsPerBatch); + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + verifyPartitionData(rowType_, dir, dirName, numBatches); + } +} + +TEST_F(IcebergTransformE2ETest, truncatePartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink( + rowType_, + outputDirectory->getPath(), + {{0, TransformType::kTruncate, 10}}); + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyPartitionCount(outputDirectory->getPath(), {"truncate(c_int, 10)"}, 10); + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + const auto partitionDirs = + listFirstLevelDirectories(outputDirectory->getPath()); + + for (const auto& dir : partitionDirs) { + const std::string dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + const std::string filter = + c + ">=" + v + " AND " + c + "<" + std::to_string(std::stoi(v) + 10); + verifyPartitionData( + rowType_, dir, filter, 20); // 10 values per batch * 2 batches. + } +} + +TEST_F(IcebergTransformE2ETest, bucketPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), {{2, TransformType::kBucket, 4}}); + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + // Verify the number of partitions (should be at most 4 buckets). + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + ASSERT_EQ(partitionDirs.size(), 4); + + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + int32_t totalRowsInPartitions = 0; + for (const auto& dir : partitionDirs) { + auto splits = createSplitsForDirectory(dir); + auto countPlan = PlanBuilder() + .tableScan(rowType_) + .singleAggregation({}, {"count(1)"}) + .planNode(); + auto countResult = + AssertQueryBuilder(countPlan).splits(splits).copyResults(opPool_.get()); + + auto partitionRowCount = + countResult->childAt(0)->asFlatVector()->valueAt(0); + totalRowsInPartitions += partitionRowCount; + ASSERT_GE(partitionRowCount, 0); + } + + ASSERT_EQ(totalRowsInPartitions, numBatches * rowsPerBatch); + + // Verify that each partition contains only rows with the same bucket value. + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + const auto equalsPos = dirName.find('='); + ASSERT_NE(equalsPos, std::string::npos); + auto dataPlan = + PlanBuilder().tableScan(rowType_).project({"c_varchar"}).planNode(); + auto dataResult = AssertQueryBuilder(dataPlan) + .splits(createSplitsForDirectory(dir)) + .copyResults(opPool_.get()); + // Verify that all rows in this partition have the same bucket hash value. + auto varcharColumn = dataResult->childAt(0)->asFlatVector(); + for (auto i = 0; i < dataResult->size(); i++) { + StringView value = varcharColumn->valueAt(i); + auto valuePlan = PlanBuilder() + .tableScan(rowType_) + .filter(fmt::format("c_varchar = '{}'", value.str())) + .project({"c_varchar"}) + .planNode(); + + auto valueResult = + AssertQueryBuilder(valuePlan) + .splits(createSplitsForDirectory(outputDirectory->getPath())) + .copyResults(opPool_.get()); + auto valueCount = valueResult->size(); + auto partitionValuePlan = + PlanBuilder() + .tableScan(rowType_) + .filter(fmt::format("c_varchar = '{}'", value.str())) + .project({"c_varchar"}) + .planNode(); + auto partitionValueResult = AssertQueryBuilder(partitionValuePlan) + .splits(createSplitsForDirectory(dir)) + .copyResults(opPool_.get()); + + ASSERT_EQ(partitionValueResult->size(), valueCount); + } + } +} + +TEST_F(IcebergTransformE2ETest, yearPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + auto dataSink = createIcebergDataSink( + rowType_, + outputDirectory->getPath(), + {{3, TransformType::kYear, std::nullopt}}); + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + // Verify the number of partitions (should be 6 for years 2020-2025). + verifyPartitionCount(outputDirectory->getPath(), {"year(c_date)"}, 6); + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + + for (int32_t year = 2020; year <= 2025; year++) { + const auto expectedDirName = fmt::format("c_date_year={}", year); + bool foundPartition = false; + auto yearFilter = [](const int32_t year) -> std::string { + return fmt::format( + "YEAR(DATE '{}-01-01')={}", + std::to_string(year), + std::to_string(year)); + }; + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + if (dirName == expectedDirName) { + foundPartition = true; + auto datePlan = PlanBuilder() + .tableScan(rowType_) + .filter(yearFilter(year)) + .singleAggregation({}, {"count(1)"}) + .planNode(); + + auto dateResult = AssertQueryBuilder(datePlan) + .splits(createSplitsForDirectory(dir)) + .copyResults(opPool_.get()); + + auto partitionRowCount = + dateResult->childAt(0)->asFlatVector()->valueAt(0); + auto countPlan = PlanBuilder() + .tableScan(rowType_) + .singleAggregation({}, {"count(1)"}) + .planNode(); + auto countResult = AssertQueryBuilder(countPlan) + .splits(createSplitsForDirectory(dir)) + .copyResults(opPool_.get()); + auto totalPartitionCount = + countResult->childAt(0)->asFlatVector()->valueAt(0); + ASSERT_EQ(partitionRowCount, totalPartitionCount); + break; + } + } + ASSERT_TRUE(foundPartition) + << "Partition for year " << year << " not found"; + } +} + +TEST_F(IcebergTransformE2ETest, varbinaryTruncatePartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + + std::vector partitionTransforms = { + {5, TransformType::kTruncate, 36}}; + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + // For binary data, we need to use a different approach for filtering. + const auto filter = c + " IS NOT NULL"; + // Verify the partition has data. + verifyPartitionData(rowType_, dir, filter, 0, true); + } +} + +TEST_F(IcebergTransformE2ETest, multipleTransformsOnSameColumn) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto vectors = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {0, TransformType::kIdentity, std::nullopt}, // c_int. + {0, TransformType::kTruncate, 10}, // truncate(c_int, 10). + {0, TransformType::kBucket, 4}}; // bucket(c_int, 4). + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + for (const auto& vector : vectors) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto firstLevelDirs = listFirstLevelDirectories(outputDirectory->getPath()); + ASSERT_GT(firstLevelDirs.size(), 0); + for (const auto& dir : firstLevelDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + ASSERT_TRUE(dirName.find("c_int=") != std::string::npos) + << "First level directory " << dirName + << " should use identity transform"; + + auto secondLevelDirs = listFirstLevelDirectories(dir); + ASSERT_GT(secondLevelDirs.size(), 0) + << "No second level directories found in " << dir; + + for (const auto& secondDir : secondLevelDirs) { + const auto secondDirName = + std::filesystem::path(secondDir).filename().string(); + ASSERT_TRUE(secondDirName.find("c_int_trunc=") != std::string::npos) + << "Second level directory " << secondDirName + << " should use truncate transform"; + + auto thirdLevelDirs = listFirstLevelDirectories(secondDir); + ASSERT_GT(thirdLevelDirs.size(), 0) + << "No third level directories found in " << secondDir; + + for (const auto& thirdDir : thirdLevelDirs) { + const auto thirdDirName = + std::filesystem::path(thirdDir).filename().string(); + ASSERT_TRUE(thirdDirName.find("c_int_bucket=") != std::string::npos) + << "Third level directory " << thirdDirName + << " should use bucket transform"; + + auto leafDir = thirdDir; + auto intValue = + std::stoi(std::filesystem::path(dir).filename().string().substr( + 6)); // c_int=X. + auto truncValue = std::stoi( + std::filesystem::path(secondDir).filename().string().substr( + 12)); // c_int_trunc=X. + std::string filter = fmt::format( + "c_int = {} AND c_int >= {} AND c_int < {}", + intValue, + truncValue, + truncValue + 10); + + // Verify the partition has data. + auto splits = createSplitsForDirectory(leafDir); + auto countPlan = PlanBuilder() + .tableScan(rowType_) + .singleAggregation({}, {"count(1)"}) + .planNode(); + auto countResult = + AssertQueryBuilder(countPlan).splits(splits).copyResults( + opPool_.get()); + ASSERT_GT( + countResult->childAt(0)->asFlatVector()->valueAt(0), 0) + << "Leaf partition directory " << leafDir << " has no data"; + } + } + } +} + +TEST_F(IcebergTransformE2ETest, timestampYearPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto batches = createTestData(numBatches, rowsPerBatch); + + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {6, TransformType::kYear, std::nullopt}}; // c_timestamp column. + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : batches) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + std::unordered_map yearToExpectedCount; + + for (const auto& batch : batches) { + auto timestampVector = batch->childAt(6)->as>(); + for (auto i = 0; i < batch->size(); i++) { + if (!timestampVector->isNullAt(i)) { + Timestamp ts = timestampVector->valueAt(i); + std::tm tm; + if (Timestamp::epochToCalendarUtc(ts.getSeconds(), tm)) { + int32_t year = tm.tm_year + 1900; + yearToExpectedCount[year]++; + } + } + } + } + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + auto year = std::stoi(v); + std::string filter = fmt::format("YEAR(c_timestamp) = {}", year); + auto expectedRowCount = yearToExpectedCount.at(year); + verifyPartitionData(rowType_, dir, filter, expectedRowCount); + } +} + +TEST_F(IcebergTransformE2ETest, timestampMonthPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + + auto batches = createTestData(numBatches, rowsPerBatch); + + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {6, TransformType::kMonth, std::nullopt}}; // c_timestamp column. + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : batches) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + std::unordered_map monthToExpectedCount; + + for (const auto& batch : batches) { + auto timestampVector = batch->childAt(6)->as>(); + for (auto i = 0; i < batch->size(); i++) { + if (!timestampVector->isNullAt(i)) { + Timestamp ts = timestampVector->valueAt(i); + std::tm tm; + if (Timestamp::epochToCalendarUtc(ts.getSeconds(), tm)) { + int32_t year = tm.tm_year + 1900; + int32_t month = tm.tm_mon + 1; + std::string monthKey = fmt::format("{:04d}-{:02d}", year, month); + monthToExpectedCount[monthKey]++; + } + } + } + } + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + size_t dashPos = v.find('-'); + ASSERT_NE(dashPos, std::string::npos) << "Invalid month format: " << v; + + int32_t year = std::stoi(v.substr(0, dashPos)); + int32_t month = std::stoi(v.substr(dashPos + 1)); + std::string filter = fmt::format( + "YEAR(c_timestamp) = {} AND MONTH(c_timestamp) = {}", year, month); + std::string monthKey = fmt::format("{:04d}-{:02d}", year, month); + auto expectedCount = monthToExpectedCount[monthKey]; + verifyPartitionData(rowType_, dir, filter, expectedCount); + } +} + +TEST_F(IcebergTransformE2ETest, timestampDayPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto batches = createTestData(numBatches, rowsPerBatch); + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {6, TransformType::kDay, std::nullopt}}; // c_timestamp column + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : batches) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + std::unordered_map dayToExpectedCount; + for (const auto& batch : batches) { + auto timestampVector = batch->childAt(6)->as>(); + for (auto i = 0; i < batch->size(); i++) { + if (!timestampVector->isNullAt(i)) { + Timestamp ts = timestampVector->valueAt(i); + std::tm tm; + if (Timestamp::epochToCalendarUtc(ts.getSeconds(), tm)) { + int32_t year = tm.tm_year + 1900; + int32_t month = tm.tm_mon + 1; + int32_t day = tm.tm_mday; + std::string dayKey = + fmt::format("{:04d}-{:02d}-{:02d}", year, month, day); + dayToExpectedCount[dayKey]++; + } + } + } + } + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + std::vector dateParts; + folly::split('-', v, dateParts); + ASSERT_EQ(dateParts.size(), 3) << "Invalid day format: " << v; + + int32_t year = std::stoi(dateParts[0]); + int32_t month = std::stoi(dateParts[1]); + int32_t day = std::stoi(dateParts[2]); + + std::string filter = fmt::format( + "YEAR(c_timestamp) = {} AND MONTH(c_timestamp) = {} AND DAY(c_timestamp) = {}", + year, + month, + day); + + // Get expected count for this day. + std::string dayKey = fmt::format("{:04d}-{:02d}-{:02d}", year, month, day); + auto expectedCount = dayToExpectedCount[dayKey]; + verifyPartitionData(rowType_, dir, filter, expectedCount); + } +} + +TEST_F(IcebergTransformE2ETest, timestampHourPartitioning) { + constexpr auto numBatches = 2; + constexpr auto rowsPerBatch = 100; + auto batches = createTestData(numBatches, rowsPerBatch); + + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {6, TransformType::kHour, std::nullopt}}; // c_timestamp column. + auto dataSink = createIcebergDataSink( + rowType_, outputDirectory->getPath(), partitionTransforms); + + for (const auto& vector : batches) { + dataSink->appendData(vector); + } + + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyTotalRowCount( + rowType_, outputDirectory->getPath(), numBatches * rowsPerBatch); + + auto partitionDirs = listFirstLevelDirectories(outputDirectory->getPath()); + std::unordered_map hourToExpectedCount; + + for (const auto& batch : batches) { + auto timestampVector = batch->childAt(6)->as>(); + for (auto i = 0; i < batch->size(); i++) { + if (!timestampVector->isNullAt(i)) { + Timestamp ts = timestampVector->valueAt(i); + std::tm tm; + if (Timestamp::epochToCalendarUtc(ts.getSeconds(), tm)) { + int32_t year = tm.tm_year + 1900; + int32_t month = tm.tm_mon + 1; + int32_t day = tm.tm_mday; + int32_t hour = tm.tm_hour; + std::string hourKey = fmt::format( + "{:04d}-{:02d}-{:02d}-{:02d}", year, month, day, hour); + hourToExpectedCount[hourKey]++; + } + } + } + } + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + auto [c, v] = buildFilter(dirName); + std::vector dateParts; + folly::split('-', v, dateParts); + ASSERT_EQ(dateParts.size(), 4) << "Invalid hour format: " << v; + + int32_t year = std::stoi(dateParts[0]); + int32_t month = std::stoi(dateParts[1]); + int32_t day = std::stoi(dateParts[2]); + int32_t hour = std::stoi(dateParts[3]); + + std::string filter = fmt::format( + "YEAR(c_timestamp) = {} AND MONTH(c_timestamp) = {} AND " + "DAY(c_timestamp) = {} AND HOUR(c_timestamp) = {}", + year, + month, + day, + hour); + std::string hourKey = + fmt::format("{:04d}-{:02d}-{:02d}-{:02d}", year, month, day, hour); + auto expectedCount = hourToExpectedCount[hourKey]; + verifyPartitionData(rowType_, dir, filter, expectedCount); + } +} + +TEST_F(IcebergTransformE2ETest, partitionFolderNamingConventions) { + auto intVector = makeFlatVector(1, [](auto) { return 42; }); + auto bigintVector = + makeFlatVector(1, [](auto) { return 9'876'543'210; }); + auto varcharVector = + BaseVector::create>(VARCHAR(), 1, opPool_.get()); + varcharVector->set(0, StringView("test string")); + + auto varcharVector2 = + BaseVector::create>(VARCHAR(), 1, opPool_.get()); + varcharVector2->setNull(0, true); + + auto decimalVector = + BaseVector::create>(DECIMAL(18, 3), 1, opPool_.get()); + decimalVector->set(0, 1'234'567'890); + + auto varbinaryVector = + BaseVector::create>(VARBINARY(), 1, opPool_.get()); + std::string binaryData = "binary\0data\1\2\3"; + varbinaryVector->set(0, StringView(binaryData)); + + auto rowVector = makeRowVector( + {"c_int", + "c_bigint", + "c_varchar", + "c_varchar2", + "c_decimal", + "c_varbinary"}, + {intVector, + bigintVector, + varcharVector, + varcharVector2, + decimalVector, + varbinaryVector}); + auto outputDirectory = TempDirectoryPath::create(); + std::vector partitionTransforms = { + {0, TransformType::kIdentity, std::nullopt}, // c_int. + {1, TransformType::kIdentity, std::nullopt}, // c_bigint. + {2, TransformType::kIdentity, std::nullopt}, // c_varchar. + {4, TransformType::kIdentity, std::nullopt}, // c_decimal. + {5, TransformType::kIdentity, std::nullopt}, // c_varbinary. + {3, TransformType::kIdentity, std::nullopt} // c_varchar2. + }; + auto dataSink = createIcebergDataSink( + asRowType(rowVector->type()), + outputDirectory->getPath(), + partitionTransforms); + + dataSink->appendData(rowVector); + ASSERT_TRUE(dataSink->finish()); + dataSink->close(); + + verifyTotalRowCount( + asRowType(rowVector->type()), outputDirectory->getPath(), 1); + auto partitionDirs = listDirectoriesRecursively(outputDirectory->getPath()); + + const std::string expectedIntFolder = "c_int=42"; + const std::string expectedBigintFolder = "c_bigint=9876543210"; + const std::string expectedVarcharFolder = "c_varchar=test+string"; + const std::string expectedVarcharFolder2 = "c_varchar2=null"; + const std::string expectedDecimalFolder = "c_decimal=1234567.890"; + const std::string expectedVarbinary = "c_varbinary=" + + encoding::Base64::encode(binaryData.data(), binaryData.size()); + + bool foundIntPartition = false; + bool foundBigintPartition = false; + bool foundVarcharPartition = false; + bool foundVarcharPartition2 = false; + bool foundDecimalPartition = false; + bool foundVarbinaryPartition = false; + + for (const auto& dir : partitionDirs) { + const auto dirName = std::filesystem::path(dir).filename().string(); + + if (dirName == expectedIntFolder) { + foundIntPartition = true; + verifyPartitionData(asRowType(rowVector->type()), dir, "c_int = 42", 1); + } else if (dirName == expectedBigintFolder) { + foundBigintPartition = true; + verifyPartitionData( + asRowType(rowVector->type()), dir, "c_bigint = 9876543210", 1); + } else if (dirName == expectedVarcharFolder) { + foundVarcharPartition = true; + verifyPartitionData( + asRowType(rowVector->type()), dir, "c_varchar = 'test string'", 1); + } else if (dirName == expectedVarcharFolder2) { + foundVarcharPartition2 = true; + verifyPartitionData( + asRowType(rowVector->type()), dir, "c_varchar2 IS NULL", 1); + } else if (dirName == expectedDecimalFolder) { + foundDecimalPartition = true; + verifyPartitionData( + asRowType(rowVector->type()), + dir, + "c_decimal = DECIMAL '1234567.890'", + 1); + } else if (dirName.find(expectedVarbinary) == 0) { + foundVarbinaryPartition = true; + verifyPartitionData( + asRowType(rowVector->type()), dir, "c_varbinary IS NOT NULL", 1); + } + } + + ASSERT_TRUE(foundIntPartition) + << "Integer partition folder not found: " << expectedIntFolder; + ASSERT_TRUE(foundBigintPartition) + << "Bigint partition folder not found: " << expectedBigintFolder; + ASSERT_TRUE(foundVarcharPartition) + << "Varchar partition folder not found: " << expectedVarcharFolder; + ASSERT_TRUE(foundVarcharPartition2) + << "Varchar2 partition folder not found: " << expectedVarcharFolder2; + ASSERT_TRUE(foundDecimalPartition) + << "Decimal partition folder not found: " << expectedDecimalFolder; + ASSERT_TRUE(foundVarbinaryPartition) + << "Varbinary partition folder not found with prefix: " + << expectedVarbinary; +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/IcebergTransformUnitTest.cpp b/velox/connectors/hive/iceberg/tests/IcebergTransformUnitTest.cpp new file mode 100644 index 000000000000..da0116c1c333 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/IcebergTransformUnitTest.cpp @@ -0,0 +1,801 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/common/encode/Base64.h" +#include "velox/connectors/hive/iceberg/tests/IcebergTestBase.h" + +namespace facebook::velox::connector::hive::iceberg::test { + +class IcebergTransformUnitTest : public IcebergTestBase { + protected: + template + void testTransform( + const IcebergPartitionSpec::Field& field, + const std::vector& inputValues, + const std::vector>& expectedValues, + const TypePtr& type = nullptr) { + VectorPtr inputVector; + std::vector> transforms = + parsePartitionTransformSpecs({field}, opPool_.get()); + auto transform = transforms[0]; + if constexpr (std::is_same_v) { + auto size = inputValues.size(); + auto vectorType = type ? type : VARCHAR(); + inputVector = BaseVector::create>( + vectorType, size, opPool_.get()); + const auto flatVector = inputVector->asFlatVector(); + for (vector_size_t i = 0; i < size; i++) { + if (i < inputValues.size()) { + flatVector->set(i, inputValues[i]); + } else { + flatVector->setNull(i, true); + } + } + } else { + auto size = inputValues.size(); + inputVector = BaseVector::create>( + type ? type : CppToType::create(), size, opPool_.get()); + const auto flatVector = inputVector->asFlatVector(); + for (vector_size_t i = 0; i < size; i++) { + if (i < inputValues.size()) { + flatVector->set(i, inputValues[i]); + } else { + flatVector->setNull(i, true); + } + } + } + + std::vector children = {inputVector}; + std::vector names = {field.name}; + auto rowVector = makeRowVector(names, children); + const auto resultVector = transform->transform(rowVector, 0); + + ASSERT_EQ(resultVector->size(), expectedValues.size()); + for (vector_size_t i = 0; i < resultVector->size(); i++) { + if (expectedValues[i].has_value()) { + if constexpr ( + std::is_same_v && + std::is_same_v) { + if (type && type->isVarbinary()) { + EXPECT_EQ( + resultVector->as>()->valueAt(i).str(), + encoding::Base64::encode(expectedValues[i].value().str())); + } else { + EXPECT_EQ( + resultVector->as>()->valueAt(i).str(), + expectedValues[i].value().str()); + } + } else { + EXPECT_EQ( + resultVector->as>()->valueAt(i), + expectedValues[i].value()); + } + } else { + EXPECT_TRUE(resultVector->isNullAt(i)); + } + } + } +}; + +TEST_F(IcebergTransformUnitTest, testIdentityTransform) { + rowType_ = + ROW({"c_int", + "c_bigint", + "c_varchar", + "c_date", + "c_varbinary", + "c_decimal", + "c_timestamp"}, + {INTEGER(), + BIGINT(), + VARCHAR(), + DATE(), + VARBINARY(), + DECIMAL(18, 3), + TIMESTAMP()}); + + // Create partition spec with identity transforms. + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kIdentity, std::nullopt}, // c_int. + {1, TransformType::kIdentity, std::nullopt}, // c_bigint. + {2, TransformType::kIdentity, std::nullopt}, // c_varchar. + {4, TransformType::kIdentity, std::nullopt}, // c_varbinary. + {5, TransformType::kIdentity, std::nullopt}, // c_decimal. + {6, TransformType::kIdentity, std::nullopt}}, // c_timestamp. + rowType_); + + auto& intTransform = partitionSpec->fields[0]; + EXPECT_EQ(intTransform.transformType, TransformType::kIdentity); + testTransform( + intTransform, + {1, + 0, + -1, + std::numeric_limits::min(), + std::numeric_limits::max()}, + {1, + 0, + -1, + std::numeric_limits::min(), + std::numeric_limits::max()}); + + auto& bigintTransform = partitionSpec->fields[1]; + EXPECT_EQ(bigintTransform.transformType, TransformType::kIdentity); + EXPECT_EQ(bigintTransform.type->kind(), TypeKind::BIGINT); + testTransform( + bigintTransform, + {1L, + 0L, + -1L, + std::numeric_limits::min(), + std::numeric_limits::max()}, + {1, + 0, + -1, + std::numeric_limits::min(), + std::numeric_limits::max()}); + + auto& varcharTransform = partitionSpec->fields[2]; + EXPECT_EQ(varcharTransform.transformType, TransformType::kIdentity); + EXPECT_EQ(varcharTransform.type->kind(), TypeKind::VARCHAR); + testTransform( + varcharTransform, + {StringView("a"), + StringView(""), + StringView("velox"), + StringView( + "Velox is a composable execution engine distributed as an open source C++ library. It provides reusable, extensible, and high-performance data processing components that can be (re-)used to build data management systems focused on different analytical workloads, including batch, interactive, stream processing, and AI/ML. Velox was created by Meta and it is currently developed in partnership with IBM/Ahana, Intel, Voltron Data, Microsoft, ByteDance and many other companies.")}, + {StringView("a"), + StringView(""), + StringView("velox"), + StringView( + "Velox is a composable execution engine distributed as an open source C++ library. It provides reusable, extensible, and high-performance data processing components that can be (re-)used to build data management systems focused on different analytical workloads, including batch, interactive, stream processing, and AI/ML. Velox was created by Meta and it is currently developed in partnership with IBM/Ahana, Intel, Voltron Data, Microsoft, ByteDance and many other companies.")}); + + auto& varbinaryTransform = partitionSpec->fields[3]; + EXPECT_EQ(varbinaryTransform.transformType, TransformType::kIdentity); + EXPECT_EQ(varbinaryTransform.type->kind(), TypeKind::VARBINARY); + testTransform( + varbinaryTransform, + { + StringView("\x01\x02\x03", 3), + StringView("\x04\x05\x06\x07", 4), + StringView("\x08\x09", 2), + StringView("", 0), + StringView("\xFF\xFE\xFD\xFC", 4), + }, + { + StringView("\x01\x02\x03", 3), + StringView("\x04\x05\x06\x07", 4), + StringView("\x08\x09", 2), + StringView("", 0), + StringView("\xFF\xFE\xFD\xFC", 4), + }, + VARBINARY()); + + auto& timestampTransform = partitionSpec->fields[5]; + EXPECT_EQ(timestampTransform.transformType, TransformType::kIdentity); + EXPECT_EQ(timestampTransform.type->kind(), TypeKind::TIMESTAMP); + testTransform( + timestampTransform, + { + Timestamp(0, 0), + Timestamp(1609459200, 0), + Timestamp(1640995200, 0), + Timestamp(1672531200, 0), + Timestamp(9223372036854775, 999999999), + }, + { + Timestamp(0, 0), + Timestamp(1609459200, 0), + Timestamp(1640995200, 0), + Timestamp(1672531200, 0), + Timestamp(9223372036854775, 999999999), + }); +} + +TEST_F(IcebergTransformUnitTest, testTruncateTransform) { + rowType_ = + ROW({"c_int", "c_decimal", "c_varchar", "c_varbinary"}, + {INTEGER(), DECIMAL(18, 3), VARCHAR(), VARBINARY()}); + + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kTruncate, 10}, + {1, TransformType::kTruncate, 10}, + {2, TransformType::kTruncate, 2}, + {3, TransformType::kTruncate, 3}}, + rowType_); + + auto& intTruncateTransform = partitionSpec->fields[0]; + testTransform( + intTruncateTransform, + { + std::numeric_limits::min(), + std::numeric_limits::min() + 1, + std::numeric_limits::min() + 9, + std::numeric_limits::min() + 10, + -1, + 0, + 1, + 9, + std::numeric_limits::max() - 10, + std::numeric_limits::max() - 9, + std::numeric_limits::max() - 1, + std::numeric_limits::max(), + }, + { + 2'147'483'646, + 2'147'483'646, + -2'147'483'640, + -2'147'483'640, + -10, + 0, + 0, + 0, + 2'147'483'630, + 2'147'483'630, + 2'147'483'640, + 2'147'483'640, + }); + + auto& decimalTruncateTransform = partitionSpec->fields[1]; + testTransform( + decimalTruncateTransform, + { + 5000, + 5010, + 5011, + 5019, + 5020, + 5021, + -5000, + -5010, + -5011, + -5019, + -5020, + -5021, + 1234, + 1230, + 1229, + 5, + -5, + -10, + -9, + -1, + 0, + 1, + 9, + 10, + 995, + 1000, + 1005, + 1010, + + // Large values. + 999'999'999'999'999'990L, + 999'999'999'999'999'995L, + 999'999'999'999'999'999L, + // Small values. + -999'999'999'999'999'990L, + -999'999'999'999'999'995L, + -999'999'999'999'999'999L, + }, + { + 5000, + 5010, + 5010, + 5010, + 5020, + 5020, + -5000, + -5010, + -5020, + -5020, + -5020, + -5030, + 1230, + 1230, + 1220, + 0, + -10, + -10, + -10, + -10, + 0, + 0, + 0, + 10, + 990, + 1000, + 1000, + 1010, + // Expected results for large values. + 999'999'999'999'999'990L, + 999'999'999'999'999'990L, + 999'999'999'999'999'990L, + // Expected results for small values. + -999'999'999'999'999'990L, + -1'000'000'000'000'000'000L, + -1'000'000'000'000'000'000L, + }); + + auto& varcharTruncateTransform = partitionSpec->fields[2]; + testTransform( + varcharTruncateTransform, + { + StringView(""), + StringView("a"), + StringView("ab"), + StringView("abc"), + StringView("abcd"), + StringView("测"), // 1 code point, 3 bytes. + StringView("测试"), // 2 code points, 6 bytes. + StringView("测试abc"), // 5 code points. + StringView("a测b试c"), // 5 code points. + StringView("🚀"), // 1 code point, 4 bytes. + StringView("🚀🔥"), // 2 code points, 8 bytes. + StringView("abc🚀🔥"), // 5 code points. + StringView("é"), // 1 code point (e + combining acute accent). + StringView("éfac"), // 4 code points. + StringView("a\u0300"), // 'a' + combining grave accent = 1 code point. + }, + { + StringView(""), + StringView("a"), + StringView("ab"), + StringView("ab"), + StringView("ab"), + StringView("测"), + StringView("测试"), + StringView("测试"), + StringView("a测"), + StringView("🚀"), + StringView("🚀🔥"), + StringView("ab"), + StringView("é"), + StringView("éf"), + StringView("a\u0300"), + }); + + auto& varbinaryTransform = partitionSpec->fields[3]; + testTransform( + varbinaryTransform, + { + StringView("\x01\x02\x03", 3), + StringView("\x04\x05\x06\x07", 4), + StringView("\x08\x09", 2), + StringView("", 0), + StringView( + "\xFF\xFE\xFD\xFC\xFA\xFB\xFC\xF1\xF2\xF3\xF4\xF5\xF6\xF7", 14), + }, + { + StringView("\x01\x02\x03", 3), + StringView("\x04\x05\x06", 3), + StringView("\x08\x09", 2), + StringView("", 0), + StringView("\xFF\xFE\xFD", 3), + }, + VARBINARY()); +} + +TEST_F(IcebergTransformUnitTest, testBucketTransform) { + rowType_ = + ROW({"c_int", "c_bigint", "c_varchar", "c_varbinary", "c_date"}, + {INTEGER(), BIGINT(), VARCHAR(), VARBINARY(), DATE()}); + + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kBucket, 4}, + {1, TransformType::kBucket, 8}, + {2, TransformType::kBucket, 16}, + {3, TransformType::kBucket, 32}, + {4, TransformType::kBucket, 10}}, + rowType_); + + auto& intBucketTransform = partitionSpec->fields[0]; + EXPECT_EQ(intBucketTransform.transformType, TransformType::kBucket); + + testTransform( + intBucketTransform, + {8, + 34, + 0, + 1, + -1, + 42, + 100, + 1000, + std::numeric_limits::min(), + std::numeric_limits::max()}, + {3, 3, 0, 0, 0, 2, 0, 0, 0, 2}); + + auto& bigintBucketTransform = partitionSpec->fields[1]; + EXPECT_EQ(bigintBucketTransform.transformType, TransformType::kBucket); + + testTransform( + bigintBucketTransform, + {34L, + 0L, + -34L, + -1L, + 1L, + 42L, + 123'456'789L, + -123'456'789L, + std::numeric_limits::min(), + std::numeric_limits::max()}, + {3, 4, 5, 0, 4, 6, 1, 4, 5, 7}); + + auto& varcharBucketTransform = partitionSpec->fields[2]; + EXPECT_EQ(varcharBucketTransform.transformType, TransformType::kBucket); + + testTransform( + varcharBucketTransform, + {StringView("abcdefg"), + StringView("测试"), + StringView("测试ping试测"), + StringView(""), + StringView("🚀🔥"), + StringView("a\u0300\u0301"), // Combining characters. + StringView("To be or not to be, that is the question.")}, + {6, 8, 11, 0, 14, 11, 9}); + + auto& varbinaryBucketTransform = partitionSpec->fields[3]; + EXPECT_EQ(varbinaryBucketTransform.transformType, TransformType::kBucket); + + testTransform( + varbinaryBucketTransform, + {StringView("abc\0\0", 5), + StringView("\x01\x02\x03\x04", 4), + StringView("\xFF\xFE\xFD\xFC", 4), + StringView("\x00\x00\x00\x00", 4), + StringView("\xDE\xAD\xBE\xEF", 4), + StringView(std::string(100, 'x').c_str(), 100)}, + {11, 5, 15, 30, 10, 18}, + VARBINARY()); + + auto& dateBucketTransform = partitionSpec->fields[4]; + EXPECT_EQ(dateBucketTransform.transformType, TransformType::kBucket); + + testTransform( + dateBucketTransform, + { + 0, // 1970-01-01. + 365, // 1971-01-01. + 18'262, // 2020-01-01. + -365, // 1969-01-01. + -1, // 1969-12-31. + 20'181, // 2025-04-03. + -36889, // 1869-01-01. + 18'628 // 2021-01-01. + }, + {6, 1, 3, 6, 2, 5, 9, 0}); +} + +TEST_F(IcebergTransformUnitTest, testTemporalTransforms) { + rowType_ = ROW({"c_date"}, {DATE()}); + + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kYear, std::nullopt}, + {0, TransformType::kMonth, std::nullopt}, + {0, TransformType::kDay, std::nullopt}, + {0, TransformType::kHour, std::nullopt}, + {0, TransformType::kBucket, 8}, + {0, TransformType::kIdentity, std::nullopt}}, + rowType_); + + auto& yearTransform = partitionSpec->fields[0]; + EXPECT_EQ(yearTransform.transformType, TransformType::kYear); + // Create test dates (days since epoch). + testTransform( + yearTransform, + { + -36889, // 1869-01-01. + -18628, // 1919-01-01. + -365, // 1969-01-01. + -1, // 1969-12-31. + 0, // 1970-01-01 (epoch). + 31, // 1970-02-01. + 365, // 1971-01-01. + 18'262, // 2020-01-01. + 20'181 // 2025-04-03. + }, + { + -101, // 1869 - 1970 = -101. + -51, // 1919 - 1970 = -51. + -1, // 1969 - 1970 = -1. + -1, // 1969 - 1970 = -1. + 0, // 1970 - 1970 = 0. + 0, // 1970 - 1970 = 0. + 1, // 1971 - 1970 = 1. + 50, // 2020 - 1970 = 50. + 55 // 2025 - 1970 = 55. + }); + // Test month transform. + auto& monthTransform = partitionSpec->fields[1]; + EXPECT_EQ(monthTransform.transformType, TransformType::kMonth); + + testTransform( + monthTransform, + {-36525, -18263, -365, -1, 0, 31, 365, 18'262, 20'181}, + {-1201, -600, -12, -1, 0, 1, 12, 600, 663}); + // Test day transform. + auto& dayTransform = partitionSpec->fields[2]; + EXPECT_EQ(dayTransform.transformType, TransformType::kDay); + testTransform( + dayTransform, + {-36525, -18263, -365, -1, 0, 31, 365, 18'262, 20'181}, + {-36525, -18263, -365, -1, 0, 31, 365, 18'262, 20'181}); +} + +TEST_F(IcebergTransformUnitTest, testTransformOnTimestamp) { + rowType_ = ROW({"c_timestamp"}, {TIMESTAMP()}); + + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kYear, std::nullopt}, + {0, TransformType::kMonth, std::nullopt}, + {0, TransformType::kDay, std::nullopt}, + {0, TransformType::kHour, std::nullopt}, + {0, TransformType::kBucket, 8}, + {0, TransformType::kIdentity, std::nullopt}}, + rowType_); + + auto& yearTransform = partitionSpec->fields[0]; + EXPECT_EQ(yearTransform.transformType, TransformType::kYear); + testTransform( + yearTransform, + { + Timestamp(0, 0), + Timestamp(31536000, 0), // 1971-01-01 00:00:00. + Timestamp(1609459200, 0), // 2021-01-01 00:00:00. + Timestamp(1612224000, 0), // 2021-02-01 00:00:00. + }, + { + 0, // 1970 - 1970 = 0. + 1, // 1971 - 1970 = 1. + 51, // 2021 - 1970 = 51. + 51 // 2021 - 1970 = 51. + }); + + auto& monthTransform = partitionSpec->fields[1]; + EXPECT_EQ(monthTransform.transformType, TransformType::kMonth); + + testTransform( + monthTransform, + {Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0)}, + {0, 12, 612, 613}); + + auto& dayTransform = partitionSpec->fields[2]; + EXPECT_EQ(dayTransform.transformType, TransformType::kDay); + testTransform( + dayTransform, + {Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0)}, + {0, 365, 18628, 18660}); + + auto& hourTransform = partitionSpec->fields[3]; + EXPECT_EQ(hourTransform.transformType, TransformType::kHour); + testTransform( + hourTransform, + {Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0)}, + {0, 8760, 447072, 447840}); + + auto& bucketTransform = partitionSpec->fields[4]; + EXPECT_EQ(bucketTransform.transformType, TransformType::kBucket); + testTransform( + bucketTransform, + { + Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0), + Timestamp(-31536000, 0), + }, + {4, 4, 6, 5, 3}); + + auto& identityTransform = partitionSpec->fields[5]; + EXPECT_EQ(identityTransform.transformType, TransformType::kIdentity); + testTransform( + identityTransform, + {Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0)}, + {Timestamp(0, 0), + Timestamp(31536000, 0), + Timestamp(1609459200, 0), + Timestamp(1612224000, 0)}); +} + +TEST_F(IcebergTransformUnitTest, testTransformsWithNulls) { + rowType_ = ROW( + {"c_int", "c_bigint", "c_decimal", "c_varchar", "c_varbinary", "c_date"}, + {INTEGER(), BIGINT(), DECIMAL(18, 3), VARCHAR(), VARBINARY(), DATE()}); + + const auto partitionSpec = createPartitionSpec( + {{0, TransformType::kIdentity, std::nullopt}, + {2, TransformType::kTruncate, 100}, + {1, TransformType::kBucket, 16}, + {5, TransformType::kYear, std::nullopt}, + {5, TransformType::kMonth, std::nullopt}, + {5, TransformType::kDay, std::nullopt}}, + rowType_); + + auto& identityTransform = partitionSpec->fields[0]; + EXPECT_EQ(identityTransform.transformType, TransformType::kIdentity); + + auto intInput = + makeNullableFlatVector({5, std::nullopt, 15, std::nullopt, 25}); + std::vector children = {intInput}; + std::vector names = {identityTransform.name}; + auto rowVector = makeRowVector(names, children); + + std::vector> transforms = + parsePartitionTransformSpecs({identityTransform}, opPool_.get()); + auto transform = transforms[0]; + auto identityResult = transform->transform(rowVector, 0); + ASSERT_EQ(identityResult->size(), 5); + EXPECT_EQ(identityResult->as>()->valueAt(0), 5); + EXPECT_TRUE(identityResult->isNullAt(1)); + EXPECT_EQ(identityResult->as>()->valueAt(2), 15); + EXPECT_TRUE(identityResult->isNullAt(3)); + EXPECT_EQ(identityResult->as>()->valueAt(4), 25); + + auto& truncateTransform = partitionSpec->fields[1]; + EXPECT_EQ(truncateTransform.transformType, TransformType::kTruncate); + + auto decimalInput = makeNullableFlatVector( + {5'000, std::nullopt, 15'000, std::nullopt, 25'000}); + children = {decimalInput}; + names = {truncateTransform.name}; + rowVector = makeRowVector(names, children); + transforms = parsePartitionTransformSpecs({truncateTransform}, opPool_.get()); + transform = transforms[0]; + auto truncateResult = transform->transform(rowVector, 0); + ASSERT_EQ(truncateResult->size(), 5); + EXPECT_EQ(truncateResult->as>()->valueAt(0), 5000); + EXPECT_TRUE(truncateResult->isNullAt(1)); + EXPECT_EQ(truncateResult->as>()->valueAt(2), 15'000); + EXPECT_TRUE(truncateResult->isNullAt(3)); + EXPECT_EQ(truncateResult->as>()->valueAt(4), 25'000); + + auto& bucketTransform = partitionSpec->fields[2]; + EXPECT_EQ(bucketTransform.transformType, TransformType::kBucket); + + auto bigintInput = makeNullableFlatVector( + {50L, std::nullopt, 150L, std::nullopt, 250L}); + children = {bigintInput}; + names = {bucketTransform.name}; + rowVector = makeRowVector(names, children); + transforms = parsePartitionTransformSpecs({bucketTransform}, opPool_.get()); + transform = transforms[0]; + auto bucketResult = transform->transform(rowVector, 0); + ASSERT_EQ(bucketResult->size(), 5); + EXPECT_TRUE(bucketResult->isNullAt(1)); + EXPECT_TRUE(bucketResult->isNullAt(3)); + + auto& yearTransform = partitionSpec->fields[3]; + EXPECT_EQ(yearTransform.transformType, TransformType::kYear); + + auto dateInput = makeNullableFlatVector( + {0, std::nullopt, 365, std::nullopt, 20'175}); + children = {dateInput}; + names = {yearTransform.name}; + rowVector = makeRowVector(names, children); + transforms = parsePartitionTransformSpecs({yearTransform}, opPool_.get()); + transform = transforms[0]; + auto yearResult = transform->transform(rowVector, 0); + ASSERT_EQ(yearResult->size(), 5); + EXPECT_EQ(yearResult->as>()->valueAt(0), 0); + EXPECT_TRUE(yearResult->isNullAt(1)); + EXPECT_EQ(yearResult->as>()->valueAt(2), 1); + EXPECT_TRUE(yearResult->isNullAt(3)); + EXPECT_EQ(yearResult->as>()->valueAt(4), 55); + + auto& monthTransform = partitionSpec->fields[4]; + EXPECT_EQ(monthTransform.transformType, TransformType::kMonth); + children = {dateInput}; + names = {monthTransform.name}; + rowVector = makeRowVector(names, children); + transforms = parsePartitionTransformSpecs({monthTransform}, opPool_.get()); + transform = transforms[0]; + auto monthResult = transform->transform(rowVector, 0); + ASSERT_EQ(monthResult->size(), 5); + EXPECT_EQ(monthResult->as>()->valueAt(0), 0); + EXPECT_TRUE(monthResult->isNullAt(1)); + EXPECT_EQ(monthResult->as>()->valueAt(2), 12); + EXPECT_TRUE(monthResult->isNullAt(3)); + EXPECT_EQ(monthResult->as>()->valueAt(4), 662); + + auto& dayTransform = partitionSpec->fields[5]; + EXPECT_EQ(dayTransform.transformType, TransformType::kDay); + names = {dayTransform.name}; + rowVector = makeRowVector(names, children); + transforms = parsePartitionTransformSpecs({dayTransform}, opPool_.get()); + transform = transforms[0]; + auto dayResult = transform->transform(rowVector, 0); + ASSERT_EQ(dayResult->size(), 5); + EXPECT_EQ(dayResult->as>()->valueAt(0), 0); + EXPECT_TRUE(dayResult->isNullAt(1)); + EXPECT_EQ(dayResult->as>()->valueAt(2), 365); + EXPECT_TRUE(dayResult->isNullAt(3)); + EXPECT_EQ(dayResult->as>()->valueAt(4), 20'175); + + auto varcharInput = makeNullableFlatVector( + {StringView("abc"), + std::nullopt, + StringView("def"), + std::nullopt, + StringView("ghi")}); + + rowType_ = ROW({"c_varchar"}, {VARCHAR()}); + auto varcharIdentityTransform = + createPartitionSpec( + {{0, TransformType::kIdentity, std::nullopt}}, rowType_) + ->fields[0]; + children = {varcharInput}; + names = {varcharIdentityTransform.name}; + rowVector = makeRowVector(names, children); + + transforms = + parsePartitionTransformSpecs({varcharIdentityTransform}, opPool_.get()); + transform = transforms[0]; + auto varcharIdentityResult = transform->transform(rowVector, 0); + ASSERT_EQ(varcharIdentityResult->size(), 5); + EXPECT_EQ( + varcharIdentityResult->as>()->valueAt(0).str(), + "abc"); + EXPECT_TRUE(varcharIdentityResult->isNullAt(1)); + EXPECT_EQ( + varcharIdentityResult->as>()->valueAt(2).str(), + "def"); + EXPECT_TRUE(varcharIdentityResult->isNullAt(3)); + EXPECT_EQ( + varcharIdentityResult->as>()->valueAt(4).str(), + "ghi"); + + auto varbinaryInput = makeNullableFlatVector( + {StringView("\x01\x02\x03", 3), + std::nullopt, + StringView("\x04\x05\x06", 3), + std::nullopt, + StringView("\x07\x08\x09", 3)}, + VARBINARY()); + + rowType_ = ROW({"c_varbinary"}, {VARBINARY()}); + auto varbinaryIdentityTransform = + createPartitionSpec( + {{0, TransformType::kIdentity, std::nullopt}}, rowType_) + ->fields[0]; + children = {varbinaryInput}; + names = {varbinaryIdentityTransform.name}; + rowVector = makeRowVector(names, children); + transforms = + parsePartitionTransformSpecs({varbinaryIdentityTransform}, opPool_.get()); + transform = transforms[0]; + auto varbinaryIdentityResult = transform->transform(rowVector, 0); + ASSERT_EQ(varbinaryIdentityResult->size(), 5); + EXPECT_TRUE(varbinaryIdentityResult->isNullAt(1)); + EXPECT_TRUE(varbinaryIdentityResult->isNullAt(3)); +} + +} // namespace facebook::velox::connector::hive::iceberg::test diff --git a/velox/connectors/hive/iceberg/tests/Main.cpp b/velox/connectors/hive/iceberg/tests/Main.cpp new file mode 100644 index 000000000000..3c9dd6615055 --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/Main.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/common/process/ThreadDebugInfo.h" + +#include +#include + +// This main is needed for some tests on linux. +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + // Signal handler required for ThreadDebugInfoTest + facebook::velox::process::addDefaultFatalSignalHandler(); + folly::Init init(&argc, &argv, false); + return RUN_ALL_TESTS(); +} diff --git a/velox/connectors/hive/iceberg/tests/Murmur3Test.cpp b/velox/connectors/hive/iceberg/tests/Murmur3Test.cpp new file mode 100644 index 000000000000..c8444ecee5be --- /dev/null +++ b/velox/connectors/hive/iceberg/tests/Murmur3Test.cpp @@ -0,0 +1,181 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/connectors/hive/iceberg/Murmur3.h" +#include +#include "folly/Random.h" +#include "velox/type/TimestampConversion.h" + +namespace facebook::velox::connector::hive::iceberg { +class Murmur3HashTest : public ::testing::Test { + public: + void SetUp() override { + rng_.seed(1); + } + + void TearDown() override {} + + // Little-endian. + static std::vector toBytes(uint64_t value) { + std::vector bytes; + bytes.reserve(sizeof(uint64_t)); + for (int32_t i = 0; i < sizeof(uint64_t); ++i) { + bytes[i] = static_cast((value >> (8 * i)) & 0xFF); + } + return bytes; + } + + template + void + verifyHashBucket(T input, uint32_t bucketCount, uint32_t expectedBucket) { + const auto hash = Murmur3Hash32::hash(input); + uint32_t actualBucket = (hash & 0X7FFFFFFF) % bucketCount; + EXPECT_EQ(actualBucket, expectedBucket) + << "Input: " << input << ", Bucket Count: " << bucketCount + << ", Hash: " << hash << ", Expected Bucket: " << expectedBucket + << ", Actual Bucket: " << actualBucket; + } + + protected: + folly::Random::DefaultGenerator rng_; +}; + +TEST_F(Murmur3HashTest, testSpecValues) { + auto hash = Murmur3Hash32::hash(34); + EXPECT_EQ(hash, 2'017'239'379); + + const auto days = + util::fromDateString("2017-11-16", util::ParseMode::kIso8601); + EXPECT_EQ(days.value(), 17'486); + hash = Murmur3Hash32::hash(days.value()); + EXPECT_EQ(hash, -653'330'422); + + auto timestampResult = util::fromTimestampString( + "2017-11-16T22:31:08", util::TimestampParseMode::kIso8601); + hash = Murmur3Hash32::hash(timestampResult.value().toMicros()); + EXPECT_EQ(hash, -2'047'944'441); + + timestampResult = util::fromTimestampString( + "2017-11-16T22:31:08.000001", util::TimestampParseMode::kIso8601); + hash = Murmur3Hash32::hash(timestampResult.value().toMicros()); + EXPECT_EQ(hash, -1'207'196'810); + + timestampResult = util::fromTimestampString( + "2017-11-16T22:31:08.000001001", util::TimestampParseMode::kIso8601); + hash = Murmur3Hash32::hash(timestampResult.value().toMicros()); + EXPECT_EQ(hash, -1'207'196'810); + + const auto bytes = new char[4]{0x00, 0x01, 0x02, 0x03}; + hash = Murmur3Hash32::hash(bytes, 4); + EXPECT_EQ(hash, -188'683'207); + + hash = Murmur3Hash32::hash("iceberg"); + EXPECT_EQ(hash, 1'210'000'089); +} + +TEST_F(Murmur3HashTest, hashString) { + const std::vector> testCases = { + {"abcdefg", 5, 4}, + {"abc", 128, 122}, + {"abcde", 64, 54}, + {"测试", 12, 8}, + {"测试raul试测", 16, 1}, + {"", 16, 0}}; + + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + verifyHashBucket(input, bucketCount, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashInteger) { + const std::vector> testCases = { + {8, 10, 3}, {34, 100, 79}}; + + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + verifyHashBucket(input, bucketCount, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashTrue) { + const auto hash = Murmur3Hash32::hash(1); + EXPECT_EQ(hash, 1'392'991'556U); +} + +TEST_F(Murmur3HashTest, hashDate) { + const std::vector> testCases = { + {util::fromDateString("1970-01-09", util::ParseMode::kIso8601).value(), + 10, + 3}, + {util::fromDateString("1970-02-04", util::ParseMode::kIso8601).value(), + 100, + 79}}; + + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + verifyHashBucket(input, bucketCount, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashLong) { + const std::vector> testCases = { + {34L, 100, 79}, {0L, 100, 76}, {-34L, 100, 97}, {-1L, 2, 0}}; + + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + verifyHashBucket(input, bucketCount, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashDecimal) { + const std::vector> testCases = { + {1234L, 64, 56}, + {1230L, 18, 13}, + {12999L, 16, 2}, + {5L, 32, 21}, + {5L, 18, 3}}; + + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + const auto hash = Murmur3Hash32::hashDecimal(input); + auto actualBucket = (hash & 0X7FFFFFFF) % bucketCount; + EXPECT_EQ(actualBucket, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashBinary) { + const std::string s("abc\0\0", 5); + const std::vector> testCases = { + {StringView("abcdefg"), 12, 10}, + {StringView(s), 18, 13}, + {StringView("abc"), 48, 42}, + {StringView("测试_"), 16, 3}}; + for (const auto& [input, bucketCount, expectedBucket] : testCases) { + verifyHashBucket(input, bucketCount, expectedBucket); + } +} + +TEST_F(Murmur3HashTest, hashIntegerAndBytes) { + const auto number = folly::Random::rand32(rng_); + const auto hashOfInteger = Murmur3Hash32::hash(number); + const auto hashOfBytes = Murmur3Hash32::hash(toBytes(number).data(), 8); + EXPECT_EQ(hashOfInteger, hashOfBytes); +} + +TEST_F(Murmur3HashTest, hashLongAndBytes) { + const auto number = folly::Random::rand64(rng_); + const auto hashOfLong = Murmur3Hash32::hash(number); + const auto hashOfBytes = Murmur3Hash32::hash(toBytes(number).data(), 8); + EXPECT_EQ(hashOfLong, hashOfBytes); +} + +} // namespace facebook::velox::connector::hive::iceberg diff --git a/velox/docs/configs.rst b/velox/docs/configs.rst index 605ae3266bcf..62b98ffeaa7c 100644 --- a/velox/docs/configs.rst +++ b/velox/docs/configs.rst @@ -685,7 +685,11 @@ Each query can override the config by setting corresponding query session proper - bool - false - Whether to preserve flat maps in memory as FlatMapVectors instead of converting them to MapVectors. This is only applied during data reading inside the DWRF and Nimble readers, not during downstream processing like expression evaluation etc. - + * - enable-requested-type-check + - enable_requested_type_check + - bool + - true + - Whether to enable requested type check in the `ReaderBase::convertType`. True by default. ``ORC File Format Configuration`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/velox/dwio/common/CMakeLists.txt b/velox/dwio/common/CMakeLists.txt index 2611a16a9332..883a6ac01f07 100644 --- a/velox/dwio/common/CMakeLists.txt +++ b/velox/dwio/common/CMakeLists.txt @@ -42,6 +42,7 @@ velox_add_library( OnDemandUnitLoader.cpp InputStream.cpp IntDecoder.cpp + DataFileStatistics.cpp MetadataFilter.cpp Options.cpp OutputStream.cpp diff --git a/velox/dwio/common/DataFileStatistics.cpp b/velox/dwio/common/DataFileStatistics.cpp new file mode 100644 index 000000000000..6968f8ee5287 --- /dev/null +++ b/velox/dwio/common/DataFileStatistics.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/dwio/common/DataFileStatistics.h" + +namespace facebook::velox::dwio::common { + +folly::dynamic DataFileStatistics::toJson() const { + folly::dynamic json = folly::dynamic::object; + json["recordCount"] = numRecords; + + auto mapToJson = [](const auto& map) { + folly::dynamic result = folly::dynamic::object; + for (const auto& pair : map) { + result[folly::to(pair.first)] = pair.second; + } + return result; + }; + + json["columnSizes"] = mapToJson(columnsSizes); + json["valueCounts"] = mapToJson(valueCounts); + json["nullValueCounts"] = mapToJson(nullValueCounts); + json["nanValueCounts"] = mapToJson(nanValueCounts); + json["lowerBounds"] = mapToJson(lowerBounds); + json["upperBounds"] = mapToJson(upperBounds); + + return json; +} + +folly::dynamic DataFileStatistics::splitOffsetsAsJson() const { + folly::dynamic arr = folly::dynamic::array; + for (const auto& offset : splitOffsets) { + arr.push_back(offset); + } + return arr; +} + +} // namespace facebook::velox::dwio::common diff --git a/velox/dwio/common/DataFileStatistics.h b/velox/dwio/common/DataFileStatistics.h new file mode 100644 index 000000000000..d8eb1491d8e5 --- /dev/null +++ b/velox/dwio/common/DataFileStatistics.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace facebook::velox::dwio::common { + +// Iceberg data_file struct fields. +struct DataFileStatistics { + int64_t numRecords; + std::unordered_map columnsSizes; + std::unordered_map valueCounts; + std::unordered_map nullValueCounts; + std::unordered_map nanValueCounts; + std::unordered_map lowerBounds; + std::unordered_map upperBounds; + + // Split offsets for the data file. For example, all row + // group offsets in a Parquet file. Must be sorted ascending. + std::vector splitOffsets; + + DataFileStatistics() : numRecords(0) {} + + folly::dynamic toJson() const; + + folly::dynamic splitOffsetsAsJson() const; +}; + +} // namespace facebook::velox::dwio::common diff --git a/velox/dwio/common/DataFileStatsCollector.h b/velox/dwio/common/DataFileStatsCollector.h new file mode 100644 index 000000000000..0f400d43c5b8 --- /dev/null +++ b/velox/dwio/common/DataFileStatsCollector.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include "velox/dwio/common/DataFileStatistics.h" + +namespace facebook::velox::dwio::common { + +/// Base settings for collecting data file statistics. Can be extended +/// by specific table formats to add format-specific fields. +struct DataFileStatsSettings { + virtual ~DataFileStatsSettings() = default; +}; + +class FileStatsCollector { + public: + explicit FileStatsCollector( + std::shared_ptr< + std::vector>> + settings) + : statsSetting_(std::move(settings)) {} + + virtual ~FileStatsCollector() = default; + + virtual void collectStats( + const void* metadata, + const std::shared_ptr& fileStats) = 0; + + protected: + std::shared_ptr< + std::vector>> + statsSetting_; +}; + +} // namespace facebook::velox::dwio::common diff --git a/velox/dwio/common/Options.h b/velox/dwio/common/Options.h index ef70cb723794..0f785b8726aa 100644 --- a/velox/dwio/common/Options.h +++ b/velox/dwio/common/Options.h @@ -27,6 +27,7 @@ #include "velox/common/io/Options.h" #include "velox/common/memory/Memory.h" #include "velox/dwio/common/ColumnSelector.h" +#include "velox/dwio/common/DataFileStatsCollector.h" #include "velox/dwio/common/ErrorTolerance.h" #include "velox/dwio/common/FlatMapHelper.h" #include "velox/dwio/common/FlushPolicy.h" @@ -629,6 +630,10 @@ class ReaderOptions : public io::ReaderOptions { return randomSkip_; } + bool enableRequestedTypeCheck() const { + return enableRequestedTypeCheck_; + } + void setRandomSkip(std::shared_ptr randomSkip) { randomSkip_ = std::move(randomSkip); } @@ -665,6 +670,10 @@ class ReaderOptions : public io::ReaderOptions { allowEmptyFile_ = value; } + void setEnableRequestedTypeCheck(bool enableRequestedTypeCheck) { + enableRequestedTypeCheck_ = enableRequestedTypeCheck; + } + private: uint64_t tailLocation_; FileFormat fileFormat_; @@ -682,6 +691,7 @@ class ReaderOptions : public io::ReaderOptions { bool adjustTimestampToTimezone_{false}; bool selectiveNimbleReaderEnabled_{false}; bool allowEmptyFile_{false}; + bool enableRequestedTypeCheck_{true}; }; struct WriterOptions { @@ -706,6 +716,11 @@ struct WriterOptions { std::string sessionTimezoneName; bool adjustTimestampToTimezone{false}; + /// Data file statistics collector for format-specific statistics collection + /// during write operations. Each table format (e.g., Iceberg, Hudi) can + /// provide its own implementation to collect connector-specific metadata. + FileStatsCollector* fileStatsCollector{nullptr}; + // WriterOption implementations can implement this function to specify how to // process format-specific session and connector configs. virtual void processConfigs( diff --git a/velox/dwio/common/Reader.h b/velox/dwio/common/Reader.h index 9dddfaeaca08..93f823839331 100644 --- a/velox/dwio/common/Reader.h +++ b/velox/dwio/common/Reader.h @@ -210,6 +210,18 @@ class Reader { static TypePtr updateColumnNames( const TypePtr& fileType, const TypePtr& tableType); + + const std::unordered_set& requiredExtraFieldIds() const { + return requiredExtraFieldIds_; + } + + void setRequiredExtraFieldIds( + std::unordered_set& requiredExtraFieldIds) { + requiredExtraFieldIds_ = requiredExtraFieldIds; + } + + private: + std::unordered_set requiredExtraFieldIds_; }; } // namespace facebook::velox::dwio::common diff --git a/velox/dwio/common/ScanSpec.cpp b/velox/dwio/common/ScanSpec.cpp index 1e47a4c29fc8..1ede7c0c2394 100644 --- a/velox/dwio/common/ScanSpec.cpp +++ b/velox/dwio/common/ScanSpec.cpp @@ -36,25 +36,27 @@ std::string_view ScanSpec::columnTypeString(ScanSpec::ColumnType columnType) { } } -ScanSpec* ScanSpec::getOrCreateChild(const std::string& name) { +ScanSpec* ScanSpec::getOrCreateChild(const std::string& name, bool isTempNode) { if (auto it = this->childByFieldName_.find(name); it != this->childByFieldName_.end()) { return it->second; } - this->children_.push_back(std::make_unique(name)); + this->children_.push_back(std::make_unique(name, isTempNode)); auto* child = this->children_.back().get(); this->childByFieldName_[child->fieldName()] = child; return child; } -ScanSpec* ScanSpec::getOrCreateChild(const Subfield& subfield) { +ScanSpec* ScanSpec::getOrCreateChild( + const Subfield& subfield, + bool isTempNode) { auto* container = this; const auto& path = subfield.path(); for (size_t depth = 0; depth < path.size(); ++depth) { const auto element = path[depth].get(); VELOX_CHECK_EQ(element->kind(), kNestedField); auto* nestedField = static_cast(element); - container = container->getOrCreateChild(nestedField->name()); + container = container->getOrCreateChild(nestedField->name(), isTempNode); } return container; } @@ -70,18 +72,19 @@ bool ScanSpec::compareTimeToDropValue( } // Integer filters are before other filters if there is no // history data. - if (left->filter_ && right->filter_) { - if (left->filter_->kind() == right->filter_->kind()) { + if (!left->filters_.empty() && !right->filters_.empty()) { + if (left->filters_.back()->kind() == right->filters_.back()->kind()) { return left->fieldName_ < right->fieldName_; } - return left->filter_->kind() < right->filter_->kind(); + return left->filters_.back()->kind() < right->filters_.back()->kind(); } + // If hasFilter() is true but 'filter_' is nullptr, we have a filter // on complex type members. The simple type filter goes first. - if (left->filter_) { + if (!left->filters_.empty()) { return true; } - if (right->filter_) { + if (!right->filters_.empty()) { return false; } return left->fieldName_ < right->fieldName_; @@ -139,6 +142,20 @@ void ScanSpec::enableFilterInSubTree(bool value) { } } +void ScanSpec::deleteTempNodes() { + for (auto it = children_.begin(); it != children_.end();) { + if ((*it)->isTempNode()) { + it = children_.erase(it); + } else { + if ((*it)->hasTempFilter()) { + (*it)->popFilter(); + (*it)->setHasTempFilter(false); + } + ++it; + } + } +} + const std::vector& ScanSpec::stableChildren() { std::lock_guard l(mutex_); if (stableChildren_.empty()) { @@ -157,7 +174,7 @@ bool ScanSpec::hasFilter() const { if (hasFilter_.has_value()) { return hasFilter_.value(); } - if (!isConstant() && filter()) { + if (filter()) { hasFilter_ = true; return true; } @@ -172,7 +189,7 @@ bool ScanSpec::hasFilter() const { } bool ScanSpec::hasFilterApplicableToConstant() const { - if (filter_) { + if (!filters_.empty()) { return true; } for (auto& child : children_) { @@ -211,7 +228,7 @@ void ScanSpec::moveAdaptationFrom(ScanSpec& other) { // constant will have been evaluated at split start time. If // 'child' is constant there is no adaptation that can be // received. - child->filter_ = std::move(otherChild->filter_); + child->filters_ = std::move(otherChild->filters_); child->selectivity_ = otherChild->selectivity_; } } @@ -417,8 +434,8 @@ std::string ScanSpec::toString() const { std::stringstream out; if (!fieldName_.empty()) { out << fieldName_; - if (filter_) { - out << " filter " << filter_->toString(); + if (!filters_.empty()) { + out << " filter " << filters_.back()->toString(); if (filterDisabled_) { out << " disabled"; } @@ -443,6 +460,10 @@ std::string ScanSpec::toString() const { return out.str(); } +void ScanSpec::addFilter(const Filter& filter) { + pushFilter(filter.clone()); +} + ScanSpec* ScanSpec::addField(const std::string& name, column_index_t channel) { auto child = getOrCreateChild(name); child->setProjectOut(true); @@ -578,8 +599,8 @@ void ScanSpec::applyFilter( const BaseVector& vector, vector_size_t size, uint64_t* result) const { - if (filter_) { - filterRows(vector, *filter_, size, result); + if (!filters_.empty()) { + filterRows(vector, *filters_.back(), size, result); } if (!vector.type()->isRow()) { // Filter on MAP or ARRAY children are pruning, and won't affect correctness diff --git a/velox/dwio/common/ScanSpec.h b/velox/dwio/common/ScanSpec.h index fbcac3d3a591..155d03053917 100644 --- a/velox/dwio/common/ScanSpec.h +++ b/velox/dwio/common/ScanSpec.h @@ -55,19 +55,34 @@ class ScanSpec { static constexpr const char* kMapValuesFieldName = "values"; static constexpr const char* kArrayElementsFieldName = "elements"; - explicit ScanSpec(const std::string& name) : fieldName_(name) {} + explicit ScanSpec(const std::string& name, bool isTempNode = false) + : fieldName_(name), isTempNode_(isTempNode) {} // Filter to apply. If 'this' corresponds to a struct/list/map, this // can only be isNull or isNotNull, other filtering is given by // 'children'. + const common::Filter* filter() const { - return filterDisabled_ ? nullptr : filter_.get(); + return filterDisabled_ + ? nullptr + : (filters_.empty() ? nullptr : filters_.back().get()); } // Sets 'filter_'. May be used at initialization or when adding a // pushed down filter, e.g. top k cutoff. void setFilter(std::shared_ptr filter) { - filter_ = std::move(filter); + filters_.push_back(std::move(filter)); + } + + void pushFilter(std::unique_ptr newFilter) { + if (!filters_.empty()) { + newFilter = newFilter->mergeWith(filters_.back().get()); + } + filters_.push_back(std::move(newFilter)); + } + + void popFilter() { + filters_.pop_back(); } void setMaxArrayElementsCount(vector_size_t count) { @@ -199,13 +214,15 @@ class ScanSpec { /// Returns the ScanSpec corresponding to 'name'. Creates it if needed without /// any intermediate level. - ScanSpec* getOrCreateChild(const std::string& name); + ScanSpec* getOrCreateChild(const std::string& name, bool isTempNode = false); // Returns the ScanSpec corresponding to 'subfield'. Creates it if // needed, including any intermediate levels. This is used at // TableScan initialization to create the ScanSpec tree that // corresponds to the ColumnReader tree. - ScanSpec* getOrCreateChild(const Subfield& subfield); + ScanSpec* getOrCreateChild(const Subfield& subfield, bool isTempNode = false); + + void deleteTempNodes(); ScanSpec* childByName(const std::string& name) const { auto it = childByFieldName_.find(name); @@ -296,6 +313,8 @@ class ScanSpec { std::string toString() const; + void addFilter(const Filter& filter); + // Add a field to this ScanSpec, with content projected out. ScanSpec* addField(const std::string& name, column_index_t channel); @@ -390,6 +409,18 @@ class ScanSpec { return disableStatsBasedFilterReorder_; } + bool isTempNode() const { + return isTempNode_; + } + + void setHasTempFilter(bool hasTempFilter) { + hasTempFilter_ = hasTempFilter; + } + + bool hasTempFilter() const { + return hasTempFilter_; + } + private: void reorder(); @@ -430,9 +461,9 @@ class ScanSpec { // True if a string dictionary or flat map in this field should be // returned as flat. bool makeFlat_ = false; - std::shared_ptr filter_; bool filterDisabled_ = false; dwio::common::DeltaColumnUpdater* deltaUpdate_ = nullptr; + std::vector> filters_; // Filters that will be only used for row group filtering based on metadata. // The conjunctions among these filters are tracked in MetadataFilter, with @@ -470,6 +501,11 @@ class ScanSpec { // This node represents a flat map column that need to be read as struct, // i.e. in table schema it is a MAP, but in result vector it is ROW. bool isFlatMapAsStruct_ = false; + + // This node is temporary, will be used and deleted after intermediate + // processing stages, like Iceberg equality deletes. + bool isTempNode_ = false; + bool hasTempFilter_ = false; }; template diff --git a/velox/dwio/common/SelectiveFlatMapColumnReader.h b/velox/dwio/common/SelectiveFlatMapColumnReader.h index 8a939ffa7bbe..507331123298 100644 --- a/velox/dwio/common/SelectiveFlatMapColumnReader.h +++ b/velox/dwio/common/SelectiveFlatMapColumnReader.h @@ -24,11 +24,13 @@ namespace facebook::velox::dwio::common { class SelectiveFlatMapColumnReader : public SelectiveStructColumnReaderBase { protected: SelectiveFlatMapColumnReader( + const dwio::common::ColumnReaderOptions& columnReaderOptions, const TypePtr& requestedType, const std::shared_ptr& fileType, FormatParams& params, velox::common::ScanSpec& scanSpec) : SelectiveStructColumnReaderBase( + columnReaderOptions, requestedType, fileType, params, diff --git a/velox/dwio/common/SelectiveStructColumnReader.cpp b/velox/dwio/common/SelectiveStructColumnReader.cpp index 7c0e9b208f9b..e118efd73a59 100644 --- a/velox/dwio/common/SelectiveStructColumnReader.cpp +++ b/velox/dwio/common/SelectiveStructColumnReader.cpp @@ -426,7 +426,6 @@ void SelectiveStructColumnReaderBase::read( } const auto& childSpecs = scanSpec_->children(); - VELOX_CHECK(!childSpecs.empty()); for (size_t i = 0; i < childSpecs.size(); ++i) { const auto& childSpec = childSpecs[i]; VELOX_TRACE_HISTORY_PUSH("read %s", childSpec->fieldName().c_str()); @@ -526,15 +525,17 @@ bool SelectiveStructColumnReaderBase::isChildMissing( // row type that doesn't exist // in the output. fileType_->type()->kind() != - TypeKind::MAP && // If this is the case it means this is a flat map, - // so it can't have "missing" fields. - childSpec.channel() >= fileType_->size()); + TypeKind::MAP // If this is the case it means this is a flat map, + // so it can't have "missing" fields. + ) && + (columnReaderOptions_.useColumnNamesForColumnMapping_ + ? !asRowType(fileType_->type())->containsChild(childSpec.fieldName()) + : childSpec.channel() >= fileType_->size()); } void SelectiveStructColumnReaderBase::getValues( const RowSet& rows, VectorPtr* result) { - VELOX_CHECK(!scanSpec_->children().empty()); VELOX_CHECK_NOT_NULL( *result, "SelectiveStructColumnReaderBase expects a non-null result"); VELOX_CHECK( diff --git a/velox/dwio/common/SelectiveStructColumnReader.h b/velox/dwio/common/SelectiveStructColumnReader.h index e867caa80eeb..941f38e60de2 100644 --- a/velox/dwio/common/SelectiveStructColumnReader.h +++ b/velox/dwio/common/SelectiveStructColumnReader.h @@ -16,6 +16,7 @@ #pragma once +#include "velox/dwio/common/Options.h" #include "velox/dwio/common/SelectiveColumnReaderInternal.h" namespace facebook::velox::dwio::common { @@ -111,6 +112,7 @@ class SelectiveStructColumnReaderBase : public SelectiveColumnReader { static constexpr int32_t kConstantChildSpecSubscript = -1; SelectiveStructColumnReaderBase( + const dwio::common::ColumnReaderOptions& columnReaderOptions, const TypePtr& requestedType, const std::shared_ptr& fileType, FormatParams& params, @@ -118,6 +120,7 @@ class SelectiveStructColumnReaderBase : public SelectiveColumnReader { bool isRoot = false, bool generateLazyChildren = true) : SelectiveColumnReader(requestedType, fileType, params, scanSpec), + columnReaderOptions_(columnReaderOptions), debugString_( getExceptionContext().message(VeloxException::Type::kSystem)), isRoot_(isRoot), @@ -172,6 +175,8 @@ class SelectiveStructColumnReaderBase : public SelectiveColumnReader { } } + const dwio::common::ColumnReaderOptions& columnReaderOptions_; + // Context information obtained from ExceptionContext. Stored here // so that LazyVector readers under this can add this to their // ExceptionContext. Allows contextualizing reader errors to split diff --git a/velox/dwio/common/SortingWriter.cpp b/velox/dwio/common/SortingWriter.cpp index 00bab4ee5360..3471ef4f907b 100644 --- a/velox/dwio/common/SortingWriter.cpp +++ b/velox/dwio/common/SortingWriter.cpp @@ -91,6 +91,7 @@ void SortingWriter::close() { setState(State::kClosed); VELOX_CHECK_NULL(sortBuffer_); outputWriter_->close(); + dataFileStats_ = outputWriter_->dataFileStats(); } void SortingWriter::abort() { diff --git a/velox/dwio/common/TypeWithId.cpp b/velox/dwio/common/TypeWithId.cpp index 03328024ef67..7dda5055b5e7 100644 --- a/velox/dwio/common/TypeWithId.cpp +++ b/velox/dwio/common/TypeWithId.cpp @@ -74,15 +74,30 @@ int countNodes(const TypePtr& type) { std::unique_ptr TypeWithId::create( const RowTypePtr& type, - const velox::common::ScanSpec& spec) { + const velox::common::ScanSpec& spec, + const std::unordered_set& requiredExtraFieldIds) { uint32_t next = 1; std::vector> children(type->size()); + + auto extraFieldSelector = [&requiredExtraFieldIds](size_t id, size_t maxId) { + for (auto extraFieldId : requiredExtraFieldIds) { + if (extraFieldId >= id && extraFieldId < maxId) { + return true; + } + } + return false; + }; + for (int i = 0, size = type->size(); i < size; ++i) { + // Recursively create children if the corresponding field is needed and is + // not constant value, or is one of the extra fields required. auto* childSpec = spec.childByName(type->nameOf(i)); - if (childSpec && !childSpec->isConstant()) { + uint32_t nodeCount = countNodes(type->childAt(i)); + if ((childSpec && !childSpec->isConstant()) || + extraFieldSelector(next, next + nodeCount)) { children[i] = create(type->childAt(i), next, i); } else { - next += countNodes(type->childAt(i)); + next += nodeCount; } } return std::make_unique( @@ -98,6 +113,20 @@ const std::shared_ptr& TypeWithId::childAt( return children_.at(idx); } +const std::shared_ptr& TypeWithId::childByFieldId( + uint32_t fieldId) const { + std::vector childNames; + std::vector childTypes; + for (auto& child : children_) { + if (fieldId == child->id()) { + return child; + } else if (fieldId > child->id() && fieldId <= child->maxId()) { + return child->childByFieldId(fieldId); + } + } + VELOX_FAIL("FieldId {} not found", fieldId); +} + std::unique_ptr TypeWithId::create( const std::shared_ptr& type, uint32_t& next, diff --git a/velox/dwio/common/TypeWithId.h b/velox/dwio/common/TypeWithId.h index a147cfe5066f..8a9645edebfe 100644 --- a/velox/dwio/common/TypeWithId.h +++ b/velox/dwio/common/TypeWithId.h @@ -43,9 +43,14 @@ class TypeWithId : public velox::Tree> { /// Create TypeWithId node but leave all the unselected children as nullptr. /// The ids are set correctly even when some of the previous nodes are not /// selected. + /// @requiredExtraFieldIds is used to determine the ids of the fields should + /// be included. This is because some connectors may require extra fields to + /// be included in the TypeWithId and ScanSpec after the base ScanSpec is + /// created. static std::unique_ptr create( const RowTypePtr& type, - const velox::common::ScanSpec& spec); + const velox::common::ScanSpec& spec, + const std::unordered_set& requiredExtraFieldIds); uint32_t size() const override; @@ -71,6 +76,9 @@ class TypeWithId : public velox::Tree> { const std::shared_ptr& childAt(uint32_t idx) const override; + const std::shared_ptr& childByFieldId( + uint32_t fieldId) const; + const std::shared_ptr& childByName( const std::string& name) const { VELOX_CHECK_EQ(type_->kind(), velox::TypeKind::ROW); diff --git a/velox/dwio/common/Writer.h b/velox/dwio/common/Writer.h index 774aafe4c9a8..8420432e70c4 100644 --- a/velox/dwio/common/Writer.h +++ b/velox/dwio/common/Writer.h @@ -21,6 +21,8 @@ #include #include +#include "velox/dwio/common/DataFileStatistics.h" +#include "velox/dwio/common/DataFileStatsCollector.h" #include "velox/vector/ComplexVector.h" namespace facebook::velox::dwio::common { @@ -79,6 +81,11 @@ class Writer { /// Data can no longer be written. virtual void abort() = 0; + /// Return statistics based on each Iceberg data file + std::shared_ptr dataFileStats() const { + return dataFileStats_; + }; + protected: bool isRunning() const; bool isFinishing() const; @@ -92,6 +99,8 @@ class Writer { static void checkStateTransition(State oldState, State newState); State state_{State::kInit}; + std::shared_ptr dataFileStats_{nullptr}; + dwio::common::FileStatsCollector* statsCollector_; }; FOLLY_ALWAYS_INLINE std::ostream& operator<<( diff --git a/velox/dwio/dwrf/reader/DwrfReader.h b/velox/dwio/dwrf/reader/DwrfReader.h index dcb38dbb5a1d..784c4dcbee83 100644 --- a/velox/dwio/dwrf/reader/DwrfReader.h +++ b/velox/dwio/dwrf/reader/DwrfReader.h @@ -288,7 +288,7 @@ class DwrfReader : public dwio::common::Reader { const std::shared_ptr& typeWithId() const override { - return readerBase_->schemaWithId(); + return readerBase_->schemaWithId(requiredExtraFieldIds()); } const PostScript& getPostscript() const { diff --git a/velox/dwio/dwrf/reader/ReaderBase.cpp b/velox/dwio/dwrf/reader/ReaderBase.cpp index c52dcf871a7a..0e137c60a330 100644 --- a/velox/dwio/dwrf/reader/ReaderBase.cpp +++ b/velox/dwio/dwrf/reader/ReaderBase.cpp @@ -344,13 +344,19 @@ std::shared_ptr ReaderBase::convertType( return SMALLINT(); case TypeKind::INTEGER: return INTEGER(); - case TypeKind::BIGINT: + case TypeKind::BIGINT: { + TypePtr converted; if (type.format() == DwrfFormat::kOrc && type.getOrcPtr()->kind() == proto::orc::Type_Kind_DECIMAL) { - return DECIMAL( - type.getOrcPtr()->precision(), type.getOrcPtr()->scale()); + converted = + DECIMAL(type.getOrcPtr()->precision(), type.getOrcPtr()->scale()); + } else { + converted = BIGINT(); + common::testutil::TestValue::adjust( + "facebook::velox::dwrf::ReaderBase::convertType", &converted); } - return BIGINT(); + return converted; + } case TypeKind::HUGEINT: if (type.format() == DwrfFormat::kOrc && type.getOrcPtr()->kind() == proto::orc::Type_Kind_DECIMAL) { diff --git a/velox/dwio/dwrf/reader/ReaderBase.h b/velox/dwio/dwrf/reader/ReaderBase.h index 561d88e56841..ac9dde9afe01 100644 --- a/velox/dwio/dwrf/reader/ReaderBase.h +++ b/velox/dwio/dwrf/reader/ReaderBase.h @@ -134,11 +134,12 @@ class ReaderBase { schema_ = std::move(newSchema); } - const std::shared_ptr& schemaWithId() const { + const std::shared_ptr& schemaWithId( + const std::unordered_set& requiredExtraFieldIds = {}) const { if (!schemaWithId_) { if (options_.scanSpec()) { - schemaWithId_ = - dwio::common::TypeWithId::create(schema_, *options_.scanSpec()); + schemaWithId_ = dwio::common::TypeWithId::create( + schema_, *options_.scanSpec(), requiredExtraFieldIds); } else { schemaWithId_ = dwio::common::TypeWithId::create(schema_); } diff --git a/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.cpp b/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.cpp index ec570ae05b7f..dce82faca156 100644 --- a/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.cpp +++ b/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.cpp @@ -75,16 +75,17 @@ void SelectiveDecimalColumnReader::seekToRowGroup(int64_t index) { template template -void SelectiveDecimalColumnReader::readHelper(RowSet rows) { - vector_size_t numRows = rows.back() + 1; +void SelectiveDecimalColumnReader::readHelper( + const common::Filter* filter, + RowSet rows) { ExtractToReader extractValues(this); - common::AlwaysTrue filter; + common::AlwaysTrue alwaysTrue; DirectRleColumnVisitor< int64_t, common::AlwaysTrue, decltype(extractValues), kDense> - visitor(filter, this, rows, extractValues); + visitor(alwaysTrue, this, rows, extractValues); // decode scale stream if (version_ == velox::dwrf::RleVersion_1) { @@ -104,14 +105,161 @@ void SelectiveDecimalColumnReader::readHelper(RowSet rows) { // reset numValues_ before reading values numValues_ = 0; valueSize_ = sizeof(DataT); + vector_size_t numRows = rows.back() + 1; ensureValuesCapacity(numRows); // decode value stream facebook::velox::dwio::common:: ColumnVisitor - valueVisitor(filter, this, rows, extractValues); + valueVisitor(alwaysTrue, this, rows, extractValues); decodeWithVisitor>(valueDecoder_.get(), valueVisitor); readOffset_ += numRows; + + // Fill decimals before applying filter. + fillDecimals(); + + const auto rawNulls = nullsInReadRange_ + ? (kDense ? nullsInReadRange_->as() : rawResultNulls_) + : nullptr; + // Process filter. + process(filter, rows, rawNulls); +} + +template +void SelectiveDecimalColumnReader::processNulls( + bool isNull, + const RowSet& rows, + const uint64_t* rawNulls) { + if (!rawNulls) { + return; + } + returnReaderNulls_ = false; + anyNulls_ = !isNull; + allNull_ = isNull; + + auto rawDecimal = values_->asMutable(); + auto rawScale = scaleBuffer_->asMutable(); + + vector_size_t idx = 0; + if (isNull) { + for (vector_size_t i = 0; i < numValues_; i++) { + if (bits::isBitNull(rawNulls, i)) { + bits::setNull(rawResultNulls_, idx); + addOutputRow(rows[i]); + idx++; + } + } + } else { + for (vector_size_t i = 0; i < numValues_; i++) { + if (!bits::isBitNull(rawNulls, i)) { + bits::setNull(rawResultNulls_, idx, false); + rawDecimal[idx] = rawDecimal[i]; + rawScale[idx] = rawScale[i]; + addOutputRow(rows[i]); + idx++; + } + } + } +} + +template +void SelectiveDecimalColumnReader::processFilter( + const common::Filter* filter, + const RowSet& rows, + const uint64_t* rawNulls) { + VELOX_CHECK_NOT_NULL(filter, "Filter must not be null."); + returnReaderNulls_ = false; + anyNulls_ = false; + allNull_ = true; + + vector_size_t idx = 0; + auto rawDecimal = values_->asMutable(); + for (vector_size_t i = 0; i < numValues_; i++) { + if (rawNulls && bits::isBitNull(rawNulls, i)) { + if (filter->testNull()) { + bits::setNull(rawResultNulls_, idx); + addOutputRow(rows[i]); + anyNulls_ = true; + idx++; + } + } else { + bool tested; + if constexpr (std::is_same_v) { + tested = filter->testInt64(rawDecimal[i]); + } else { + tested = filter->testInt128(rawDecimal[i]); + } + + if (tested) { + if (rawNulls) { + bits::setNull(rawResultNulls_, idx, false); + } + rawDecimal[idx] = rawDecimal[i]; + addOutputRow(rows[i]); + allNull_ = false; + idx++; + } + } + } +} + +template +void SelectiveDecimalColumnReader::process( + const common::Filter* filter, + const RowSet& rows, + const uint64_t* rawNulls) { + // Treat the filter as kAlwaysTrue if any of the following conditions are met: + // 1) No filter found; + // 2) Filter is kIsNotNull but rawNulls == NULL (no elements is null). + auto filterKind = + !filter || (filter->kind() == common::FilterKind::kIsNotNull && !rawNulls) + ? common::FilterKind::kAlwaysTrue + : filter->kind(); + switch (filterKind) { + case common::FilterKind::kAlwaysTrue: + // Simply add all rows to output. + for (vector_size_t i = 0; i < numValues_; i++) { + addOutputRow(rows[i]); + } + break; + case common::FilterKind::kIsNull: + processNulls(true, rows, rawNulls); + break; + case common::FilterKind::kIsNotNull: + processNulls(false, rows, rawNulls); + break; + case common::FilterKind::kBigintRange: + case common::FilterKind::kBigintValuesUsingHashTable: + case common::FilterKind::kBigintValuesUsingBitmask: + case common::FilterKind::kNegatedBigintRange: + case common::FilterKind::kNegatedBigintValuesUsingHashTable: + case common::FilterKind::kNegatedBigintValuesUsingBitmask: + case common::FilterKind::kBigintMultiRange: { + if constexpr (std::is_same_v) { + processFilter(filter, rows, rawNulls); + } else { + const auto actualType = CppToType::create(); + VELOX_NYI( + "Expected type BIGINT, but found file type {}.", + actualType->toString()); + } + break; + } + case common::FilterKind::kHugeintValuesUsingHashTable: + case common::FilterKind::kHugeintRange: { + if constexpr (std::is_same_v) { + processFilter(filter, rows, rawNulls); + } else { + const auto actualType = CppToType::create(); + VELOX_NYI( + "Expected type HUGEINT, but found file type {}.", + actualType->toString()); + } + break; + } + default: + VELOX_NYI("Unsupported filter: {}.", static_cast(filterKind)); + } } template @@ -119,14 +267,20 @@ void SelectiveDecimalColumnReader::read( int64_t offset, const RowSet& rows, const uint64_t* incomingNulls) { - VELOX_CHECK(!scanSpec_->filter()); VELOX_CHECK(!scanSpec_->valueHook()); prepareRead(offset, rows, incomingNulls); + if (!resultNulls_ || !resultNulls_->unique() || + resultNulls_->capacity() * 8 < rows.size()) { + // Make sure a dedicated resultNulls_ is allocated with enough capacity as + // RleDecoder always assumes it is available. + resultNulls_ = AlignedBuffer::allocate(rows.size(), memoryPool_); + rawResultNulls_ = resultNulls_->asMutable(); + } bool isDense = rows.back() == rows.size() - 1; if (isDense) { - readHelper(rows); + readHelper(scanSpec_->filter(), rows); } else { - readHelper(rows); + readHelper(scanSpec_->filter(), rows); } } @@ -134,16 +288,18 @@ template void SelectiveDecimalColumnReader::getValues( const RowSet& rows, VectorPtr* result) { + rawValues_ = values_->asMutable(); + getIntValues(rows, requestedType_, result); +} + +template +void SelectiveDecimalColumnReader::fillDecimals() { auto nullsPtr = resultNulls() ? resultNulls()->template as() : nullptr; auto scales = scaleBuffer_->as(); auto values = values_->asMutable(); - DecimalUtil::fillDecimals( values, nullsPtr, values, scales, numValues_, scale_); - - rawValues_ = values_->asMutable(); - getIntValues(rows, requestedType_, result); } template class SelectiveDecimalColumnReader; diff --git a/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.h b/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.h index 67a82b051e36..338d8ac4756f 100644 --- a/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.h +++ b/velox/dwio/dwrf/reader/SelectiveDecimalColumnReader.h @@ -49,7 +49,24 @@ class SelectiveDecimalColumnReader : public SelectiveColumnReader { private: template - void readHelper(RowSet rows); + void readHelper(const common::Filter* filter, RowSet rows); + + // Process IsNull and IsNotNull filters. + void processNulls(bool isNull, const RowSet& rows, const uint64_t* rawNulls); + + // Process filters on decimal values. + void processFilter( + const common::Filter* filter, + const RowSet& rows, + const uint64_t* rawNulls); + + // Dispatch to the respective filter processing based on the filter type. + void process( + const common::Filter* filter, + const RowSet& rows, + const uint64_t* rawNulls); + + void fillDecimals(); std::unique_ptr> valueDecoder_; std::unique_ptr> scaleDecoder_; diff --git a/velox/dwio/dwrf/reader/SelectiveFlatMapColumnReader.cpp b/velox/dwio/dwrf/reader/SelectiveFlatMapColumnReader.cpp index 854f4001a857..b9c8a12d0232 100644 --- a/velox/dwio/dwrf/reader/SelectiveFlatMapColumnReader.cpp +++ b/velox/dwio/dwrf/reader/SelectiveFlatMapColumnReader.cpp @@ -203,6 +203,7 @@ class SelectiveFlatMapAsStructReader : public SelectiveStructColumnReaderBase { DwrfParams& params, common::ScanSpec& scanSpec) : SelectiveStructColumnReaderBase( + columnReaderOptions, requestedType, fileType, params, @@ -241,6 +242,7 @@ class SelectiveFlatMapAsMapReader : public SelectiveStructColumnReaderBase { DwrfParams& params, common::ScanSpec& scanSpec) : SelectiveStructColumnReaderBase( + columnReaderOptions, requestedType, fileType, params, @@ -280,6 +282,7 @@ class SelectiveFlatMapReader DwrfParams& params, common::ScanSpec& scanSpec) : dwio::common::SelectiveFlatMapColumnReader( + columnReaderOptions, requestedType, fileType, params, diff --git a/velox/dwio/dwrf/reader/SelectiveStructColumnReader.cpp b/velox/dwio/dwrf/reader/SelectiveStructColumnReader.cpp index b1f47289505e..aa55cab7f04e 100644 --- a/velox/dwio/dwrf/reader/SelectiveStructColumnReader.cpp +++ b/velox/dwio/dwrf/reader/SelectiveStructColumnReader.cpp @@ -31,6 +31,7 @@ SelectiveStructColumnReader::SelectiveStructColumnReader( common::ScanSpec& scanSpec, bool isRoot) : SelectiveStructColumnReaderBase( + columnReaderOptions, requestedType, fileType, params, diff --git a/velox/dwio/dwrf/reader/SelectiveStructColumnReader.h b/velox/dwio/dwrf/reader/SelectiveStructColumnReader.h index 6e323b67fe39..f8559edfca0f 100644 --- a/velox/dwio/dwrf/reader/SelectiveStructColumnReader.h +++ b/velox/dwio/dwrf/reader/SelectiveStructColumnReader.h @@ -25,6 +25,7 @@ class SelectiveStructColumnReaderBase : public dwio::common::SelectiveStructColumnReaderBase { public: SelectiveStructColumnReaderBase( + const dwio::common::ColumnReaderOptions& columnReaderOptions, const TypePtr& requestedType, const std::shared_ptr& fileType, DwrfParams& params, @@ -32,6 +33,7 @@ class SelectiveStructColumnReaderBase bool isRoot = false, bool generateLazyChildren = true) : dwio::common::SelectiveStructColumnReaderBase( + columnReaderOptions, requestedType, fileType, params, diff --git a/velox/dwio/dwrf/test/E2EFilterTest.cpp b/velox/dwio/dwrf/test/E2EFilterTest.cpp index 43b67e91e550..6a56da80e891 100644 --- a/velox/dwio/dwrf/test/E2EFilterTest.cpp +++ b/velox/dwio/dwrf/test/E2EFilterTest.cpp @@ -15,6 +15,7 @@ */ #include "velox/common/base/Portability.h" +#include "velox/common/base/tests/GTestUtils.h" #include "velox/common/testutil/TestValue.h" #include "velox/dwio/common/tests/utils/E2EFilterTestBase.h" #include "velox/dwio/dwrf/reader/DwrfReader.h" @@ -64,11 +65,11 @@ class E2EFilterTest : public E2EFilterTestBase { const TypePtr& type, const std::vector& batches, bool forRowGroupSkip = false) override { - auto options = createWriterOptions(type); + setWriterOptions(type); int32_t flushCounter = 0; // If we test row group skip, we have all the data in one stripe. For // scan, we start a stripe every 'flushEveryNBatches_' batches. - options.flushPolicyFactory = [&]() { + options_.flushPolicyFactory = [&]() { return std::make_unique([&]() { return forRowGroupSkip ? false : (++flushCounter % flushEveryNBatches_ == 0); @@ -80,8 +81,8 @@ class E2EFilterTest : public E2EFilterTestBase { dwio::common::FileSink::Options{.pool = leafPool_.get()}); ASSERT_TRUE(sink->isBuffered()); auto* sinkPtr = sink.get(); - options.memoryPool = rootPool_.get(); - writer_ = std::make_unique(std::move(sink), options); + options_.memoryPool = rootPool_.get(); + writer_ = std::make_unique(std::move(sink), options_); for (auto& batch : batches) { writer_->write(batch); } @@ -105,9 +106,10 @@ class E2EFilterTest : public E2EFilterTestBase { } std::unordered_set flatMapColumns_; + dwrf::WriterOptions options_; private: - dwrf::WriterOptions createWriterOptions(const TypePtr& type) { + void setWriterOptions(const TypePtr& type) { auto config = std::make_shared(); config->set(dwrf::Config::COMPRESSION, CompressionKind_NONE); config->set(dwrf::Config::USE_VINTS, useVInts_); @@ -148,10 +150,8 @@ class E2EFilterTest : public E2EFilterTestBase { config->set>>( dwrf::Config::MAP_FLAT_COLS_STRUCT_KEYS, mapFlatColsStructKeys); } - dwrf::WriterOptions options; - options.config = config; - options.schema = writerSchema; - return options; + options_.config = config; + options_.schema = writerSchema; } std::unique_ptr writer_; @@ -227,6 +227,74 @@ TEST_F(E2EFilterTest, byteRle) { 20); } +DEBUG_ONLY_TEST_F(E2EFilterTest, shortDecimal) { + testutil::TestValue::enable(); + options_.format = DwrfFormat::kOrc; + const std::unordered_map types = { + {"shortdecimal_val:decimal(8, 5)", DECIMAL(8, 5)}, + {"shortdecimal_val:decimal(10, 5)", DECIMAL(10, 5)}, + {"shortdecimal_val:decimal(17, 5)", DECIMAL(17, 5)}}; + + for (const auto& pair : types) { + SCOPED_TESTVALUE_SET( + "facebook::velox::dwrf::ReaderBase::convertType", + std::function( + [&](TypePtr* type) { *type = pair.second; })); + testWithTypes( + pair.first, + [&]() { + makeIntDistribution( + "shortdecimal_val", + 10, // min + 100, // max + 22, // repeats + 19, // rareFrequency + -999, // rareMin + 30000, // rareMax + true); + }, + false, + {"shortdecimal_val"}, + 20); + } + options_.format = DwrfFormat::kDwrf; +} + +DEBUG_ONLY_TEST_F(E2EFilterTest, longDecimal) { + testutil::TestValue::enable(); + options_.format = DwrfFormat::kOrc; + const std::unordered_map types = { + {"longdecimal_val:decimal(30, 10)", DECIMAL(30, 10)}, + {"longdecimal_val:decimal(37, 15)", DECIMAL(37, 15)}}; + + SCOPED_TESTVALUE_SET( + "facebook::velox::dwrf::ProtoUtils::writeType", + std::function([&](bool* kindSet) { *kindSet = true; })); + for (const auto& pair : types) { + SCOPED_TESTVALUE_SET( + "facebook::velox::dwrf::ReaderBase::convertType", + std::function( + [&](TypePtr* type) { *type = pair.second; })); + testWithTypes( + pair.first, + [&]() { + makeIntDistribution( + "longdecimal_val", + 10, // min + 100, // max + 22, // repeats + 19, // rareFrequency + -999, // rareMin + 30000, // rareMax + true); + }, + false, + {"longdecimal_val"}, + 20); + } + options_.format = DwrfFormat::kDwrf; +} + TEST_F(E2EFilterTest, floatAndDouble) { testWithTypes( "float_val:float," diff --git a/velox/dwio/dwrf/utils/ProtoUtils.cpp b/velox/dwio/dwrf/utils/ProtoUtils.cpp index 405d2e79ddfb..e907c4126d3e 100644 --- a/velox/dwio/dwrf/utils/ProtoUtils.cpp +++ b/velox/dwio/dwrf/utils/ProtoUtils.cpp @@ -57,9 +57,22 @@ void ProtoUtils::writeType( if (parent) { parent->add_subtypes(footer.types_size() - 1); } - auto kind = - VELOX_STATIC_FIELD_DYNAMIC_DISPATCH(SchemaType, kind, type.kind()); - self->set_kind(kind); + bool kindSet = false; + if (type.kind() == TypeKind::HUGEINT) { + // Hugeint is not supported by DWRF, and this branch is only for ORC + // testing before the ORC footer write is implemented. + auto kind = SchemaType::kind; + self->set_kind(kind); + common::testutil::TestValue::adjust( + "facebook::velox::dwrf::ProtoUtils::writeType", &kindSet); + } else { + auto kind = + VELOX_STATIC_FIELD_DYNAMIC_DISPATCH(SchemaType, kind, type.kind()); + self->set_kind(kind); + kindSet = true; + } + VELOX_CHECK(kindSet, "Unknown type {}.", type.toString()); + switch (type.kind()) { case TypeKind::ROW: { auto& row = type.asRow(); diff --git a/velox/dwio/dwrf/writer/ColumnWriter.cpp b/velox/dwio/dwrf/writer/ColumnWriter.cpp index 2a4cf2077961..a1084f4d6104 100644 --- a/velox/dwio/dwrf/writer/ColumnWriter.cpp +++ b/velox/dwio/dwrf/writer/ColumnWriter.cpp @@ -2183,7 +2183,12 @@ std::unique_ptr BaseColumnWriter::create( context, type, sequence, onRecordPosition); ret->children_.reserve(type.size()); for (int32_t i = 0; i < type.size(); ++i) { - ret->children_.push_back(create(context, *type.childAt(i), sequence)); + ret->children_.push_back(create( + context, + *type.childAt(i), + sequence, + /*onRecordPosition=*/nullptr, + format)); } return ret; } @@ -2199,15 +2204,30 @@ std::unique_ptr BaseColumnWriter::create( } auto ret = std::make_unique( context, type, sequence, onRecordPosition); - ret->children_.push_back(create(context, *type.childAt(0), sequence)); - ret->children_.push_back(create(context, *type.childAt(1), sequence)); + ret->children_.push_back(create( + context, + *type.childAt(0), + sequence, + /*onRecordPosition=*/nullptr, + format)); + ret->children_.push_back(create( + context, + *type.childAt(1), + sequence, + /*onRecordPosition=*/nullptr, + format)); return ret; } case TypeKind::ARRAY: { VELOX_CHECK_EQ(type.size(), 1, "Array should have exactly one child"); auto ret = std::make_unique( context, type, sequence, onRecordPosition); - ret->children_.push_back(create(context, *type.childAt(0), sequence)); + ret->children_.push_back(create( + context, + *type.childAt(0), + sequence, + /*onRecordPosition=*/nullptr, + format)); return ret; } default: diff --git a/velox/dwio/dwrf/writer/Writer.cpp b/velox/dwio/dwrf/writer/Writer.cpp index d6011c38f8de..b5af93a2cc1b 100644 --- a/velox/dwio/dwrf/writer/Writer.cpp +++ b/velox/dwio/dwrf/writer/Writer.cpp @@ -200,7 +200,12 @@ Writer::Writer( } if (options.columnWriterFactory == nullptr) { - writer_ = BaseColumnWriter::create(writerBase_->getContext(), *schema_); + writer_ = BaseColumnWriter::create( + writerBase_->getContext(), + *schema_, + /*sequence=*/0, + /*onRecordPosition=*/nullptr, + options.format); } else { writer_ = options.columnWriterFactory(writerBase_->getContext(), *schema_); } diff --git a/velox/dwio/parquet/common/CMakeLists.txt b/velox/dwio/parquet/common/CMakeLists.txt index 4e3edf6687ce..88dd68e703e9 100644 --- a/velox/dwio/parquet/common/CMakeLists.txt +++ b/velox/dwio/parquet/common/CMakeLists.txt @@ -17,7 +17,8 @@ velox_add_library( BloomFilter.cpp XxHasher.cpp LevelComparison.cpp - LevelConversion.cpp) + LevelConversion.cpp + UnicodeUtil.cpp) velox_link_libraries( velox_dwio_parquet_common diff --git a/velox/dwio/parquet/common/UnicodeUtil.cpp b/velox/dwio/parquet/common/UnicodeUtil.cpp new file mode 100644 index 000000000000..780fa8163183 --- /dev/null +++ b/velox/dwio/parquet/common/UnicodeUtil.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/dwio/parquet/common/UnicodeUtil.h" + +#include "velox/external/utf8proc/utf8proc.h" +#include "velox/functions/lib/string/StringImpl.h" + +namespace facebook::velox::parquet { + +std::string_view UnicodeUtil::truncateStringMin( + const char* input, + int32_t inputLength, + int32_t numCodePoints) { + auto length = functions::stringImpl::cappedByteLength( + StringView(input, inputLength), numCodePoints); + return std::string_view(input, length); +} + +std::string UnicodeUtil::truncateStringMax( + const char* input, + int32_t inputLength, + int32_t numCodePoints) { + auto truncatedLength = functions::stringImpl::cappedByteLength( + StringView(input, inputLength), numCodePoints); + + if (truncatedLength == inputLength) { + return std::string(input, inputLength); + } + + // Try to increment the last code point. + for (auto i = numCodePoints - 1; i >= 0; --i) { + const char* current = input; + int32_t currentCodePoint = 0; + + // Find the i-th code point position. + while (current < input + truncatedLength && currentCodePoint < i) { + int32_t charLength; + utf8proc_codepoint(current, input + truncatedLength, charLength); + current += charLength; + currentCodePoint++; + } + + if (current >= input + truncatedLength) + continue; + + int32_t charLength; + auto codePoint = + utf8proc_codepoint(current, input + truncatedLength, charLength); + auto nextCodePoint = codePoint + 1; + + // Check if the incremented code point is valid. + if (nextCodePoint != 0 && utf8proc_codepoint_valid(nextCodePoint)) { + std::string result; + result.reserve(truncatedLength + 4); + result.assign(input, current - input); + char buffer[4]; + auto bytesWritten = utf8proc_encode_char( + nextCodePoint, reinterpret_cast(buffer)); + result.append(buffer, bytesWritten); + return result; + } + } + + return std::string(input, truncatedLength); +} + +} // namespace facebook::velox::parquet diff --git a/velox/dwio/parquet/common/UnicodeUtil.h b/velox/dwio/parquet/common/UnicodeUtil.h new file mode 100644 index 000000000000..4d0952b9f9f0 --- /dev/null +++ b/velox/dwio/parquet/common/UnicodeUtil.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +namespace facebook::velox::parquet { + +class UnicodeUtil { + public: + static std::string_view truncateStringMin( + const char* input, + int32_t inputLength, + int32_t numCodePoints); + + static std::string truncateStringMax( + const char* input, + int32_t inputLength, + int32_t numCodePoints); + + private: + UnicodeUtil() = delete; +}; + +} // namespace facebook::velox::parquet diff --git a/velox/dwio/parquet/reader/ParquetColumnReader.cpp b/velox/dwio/parquet/reader/ParquetColumnReader.cpp index 0b69f446280d..f3519ecc2782 100644 --- a/velox/dwio/parquet/reader/ParquetColumnReader.cpp +++ b/velox/dwio/parquet/reader/ParquetColumnReader.cpp @@ -38,7 +38,8 @@ std::unique_ptr ParquetColumnReader::build( const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec) { + common::ScanSpec& scanSpec, + memory::MemoryPool& pool) { auto colName = scanSpec.fieldName(); switch (fileType->type()->kind()) { @@ -59,7 +60,7 @@ std::unique_ptr ParquetColumnReader::build( case TypeKind::ROW: return std::make_unique( - columnReaderOptions, requestedType, fileType, params, scanSpec); + columnReaderOptions, requestedType, fileType, params, scanSpec, pool); case TypeKind::VARBINARY: case TypeKind::VARCHAR: @@ -67,11 +68,11 @@ std::unique_ptr ParquetColumnReader::build( case TypeKind::ARRAY: return std::make_unique( - columnReaderOptions, requestedType, fileType, params, scanSpec); + columnReaderOptions, requestedType, fileType, params, scanSpec, pool); case TypeKind::MAP: return std::make_unique( - columnReaderOptions, requestedType, fileType, params, scanSpec); + columnReaderOptions, requestedType, fileType, params, scanSpec, pool); case TypeKind::BOOLEAN: return std::make_unique( diff --git a/velox/dwio/parquet/reader/ParquetColumnReader.h b/velox/dwio/parquet/reader/ParquetColumnReader.h index dd3a4a450b9e..4d9725d08391 100644 --- a/velox/dwio/parquet/reader/ParquetColumnReader.h +++ b/velox/dwio/parquet/reader/ParquetColumnReader.h @@ -47,6 +47,7 @@ class ParquetColumnReader { const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec); + common::ScanSpec& scanSpec, + memory::MemoryPool& pool); }; } // namespace facebook::velox::parquet diff --git a/velox/dwio/parquet/reader/ParquetReader.cpp b/velox/dwio/parquet/reader/ParquetReader.cpp index 955abc91b8ad..6ae383163370 100644 --- a/velox/dwio/parquet/reader/ParquetReader.cpp +++ b/velox/dwio/parquet/reader/ParquetReader.cpp @@ -722,6 +722,7 @@ TypePtr ReaderBase::convertType( static std::string_view kTypeMappingErrorFmtStr = "Converted type {} is not allowed for requested type {}"; + const bool enableRequestedTypeCheck = options_.enableRequestedTypeCheck(); if (schemaElement.__isset.converted_type) { switch (schemaElement.converted_type) { case thrift::ConvertedType::INT_8: @@ -731,14 +732,16 @@ TypePtr ReaderBase::convertType( thrift::Type::INT32, "{} converted type can only be set for value of thrift::Type::INT32", schemaElement.converted_type); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::TINYINT || - requestedType->kind() == TypeKind::SMALLINT || - requestedType->kind() == TypeKind::INTEGER || - requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "TINYINT", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::TINYINT || + requestedType->kind() == TypeKind::SMALLINT || + requestedType->kind() == TypeKind::INTEGER || + requestedType->kind() == TypeKind::BIGINT, + kTypeMappingErrorFmtStr, + "TINYINT", + requestedType->toString()); + } return TINYINT(); case thrift::ConvertedType::INT_16: @@ -748,13 +751,15 @@ TypePtr ReaderBase::convertType( thrift::Type::INT32, "{} converted type can only be set for value of thrift::Type::INT32", schemaElement.converted_type); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::SMALLINT || - requestedType->kind() == TypeKind::INTEGER || - requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "SMALLINT", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::SMALLINT || + requestedType->kind() == TypeKind::INTEGER || + requestedType->kind() == TypeKind::BIGINT, + kTypeMappingErrorFmtStr, + "SMALLINT", + requestedType->toString()); + } return SMALLINT(); case thrift::ConvertedType::INT_32: @@ -764,12 +769,14 @@ TypePtr ReaderBase::convertType( thrift::Type::INT32, "{} converted type can only be set for value of thrift::Type::INT32", schemaElement.converted_type); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::INTEGER || - requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "INTEGER", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::INTEGER || + requestedType->kind() == TypeKind::BIGINT, + kTypeMappingErrorFmtStr, + "INTEGER", + requestedType->toString()); + } return INTEGER(); case thrift::ConvertedType::INT_64: @@ -779,11 +786,13 @@ TypePtr ReaderBase::convertType( thrift::Type::INT64, "{} converted type can only be set for value of thrift::Type::INT32", schemaElement.converted_type); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "BIGINT", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::BIGINT, + kTypeMappingErrorFmtStr, + "BIGINT", + requestedType->toString()); + } return BIGINT(); case thrift::ConvertedType::DATE: @@ -791,11 +800,13 @@ TypePtr ReaderBase::convertType( schemaElement.type, thrift::Type::INT32, "DATE converted type can only be set for value of thrift::Type::INT32"); - VELOX_CHECK( - !requestedType || requestedType->isDate(), - kTypeMappingErrorFmtStr, - "DATE", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->isDate(), + kTypeMappingErrorFmtStr, + "DATE", + requestedType->toString()); + } return DATE(); case thrift::ConvertedType::TIMESTAMP_MICROS: @@ -804,11 +815,13 @@ TypePtr ReaderBase::convertType( schemaElement.type, thrift::Type::INT64, "TIMESTAMP_MICROS or TIMESTAMP_MILLIS converted type can only be set for value of thrift::Type::INT64"); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, - kTypeMappingErrorFmtStr, - "TIMESTAMP", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, + kTypeMappingErrorFmtStr, + "TIMESTAMP", + requestedType->toString()); + } return TIMESTAMP(); case thrift::ConvertedType::DECIMAL: { @@ -817,20 +830,20 @@ TypePtr ReaderBase::convertType( "DECIMAL requires a length and scale specifier!"); const auto schemaElementPrecision = schemaElement.precision; const auto schemaElementScale = schemaElement.scale; - // A long decimal requested type cannot read a value of a short decimal. - // As a result, the mapping from short to long decimal is currently - // restricted. + // A long decimal requested type cannot read a value of a short + // decimal. As a result, the mapping from short to long decimal is + // currently restricted. auto type = DECIMAL(schemaElementPrecision, schemaElementScale); - if (requestedType) { + if (enableRequestedTypeCheck && requestedType) { VELOX_CHECK( requestedType->isDecimal(), kTypeMappingErrorFmtStr, "DECIMAL", requestedType->toString()); - // Reading short decimals with a long decimal requested type is not - // yet possible. To allow for correct interpretation of the values, - // the scale of the file type and requested type must match while - // precision may be larger. + // Reading short decimals with a long decimal requested type is + // not yet possible. To allow for correct interpretation of the + // values, the scale of the file type and requested type must + // match while precision may be larger. if (requestedType->isShortDecimal()) { const auto& shortDecimalType = requestedType->asShortDecimal(); VELOX_CHECK( @@ -858,11 +871,13 @@ TypePtr ReaderBase::convertType( switch (schemaElement.type) { case thrift::Type::BYTE_ARRAY: case thrift::Type::FIXED_LEN_BYTE_ARRAY: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::VARCHAR, - kTypeMappingErrorFmtStr, - "VARCHAR", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::VARCHAR, + kTypeMappingErrorFmtStr, + "VARCHAR", + requestedType->toString()); + } return VARCHAR(); default: VELOX_FAIL( @@ -873,11 +888,13 @@ TypePtr ReaderBase::convertType( schemaElement.type, thrift::Type::BYTE_ARRAY, "ENUM converted type can only be set for value of thrift::Type::BYTE_ARRAY"); - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::VARCHAR, - kTypeMappingErrorFmtStr, - "VARCHAR", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::VARCHAR, + kTypeMappingErrorFmtStr, + "VARCHAR", + requestedType->toString()); + } return VARCHAR(); } case thrift::ConvertedType::MAP: @@ -896,69 +913,85 @@ TypePtr ReaderBase::convertType( } else { switch (schemaElement.type) { case thrift::Type::type::BOOLEAN: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::BOOLEAN, - kTypeMappingErrorFmtStr, - "BOOLEAN", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::BOOLEAN, + kTypeMappingErrorFmtStr, + "BOOLEAN", + requestedType->toString()); + } return BOOLEAN(); case thrift::Type::type::INT32: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::INTEGER || - requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "INTEGER", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::INTEGER || + requestedType->kind() == TypeKind::BIGINT, + kTypeMappingErrorFmtStr, + "INTEGER", + requestedType->toString()); + } return INTEGER(); case thrift::Type::type::INT64: // For Int64 Timestamp in nano precision if (schemaElement.__isset.logicalType && schemaElement.logicalType.__isset.TIMESTAMP) { + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, + kTypeMappingErrorFmtStr, + "TIMESTAMP", + requestedType->toString()); + } + return TIMESTAMP(); + } + if (enableRequestedTypeCheck) { VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, + !requestedType || requestedType->kind() == TypeKind::BIGINT, kTypeMappingErrorFmtStr, - "TIMESTAMP", + "BIGINT", requestedType->toString()); - return TIMESTAMP(); } - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::BIGINT, - kTypeMappingErrorFmtStr, - "BIGINT", - requestedType->toString()); return BIGINT(); case thrift::Type::type::INT96: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, - kTypeMappingErrorFmtStr, - "TIMESTAMP", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::TIMESTAMP, + kTypeMappingErrorFmtStr, + "TIMESTAMP", + requestedType->toString()); + } return TIMESTAMP(); // INT96 only maps to a timestamp case thrift::Type::type::FLOAT: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::REAL || - requestedType->kind() == TypeKind::DOUBLE, - kTypeMappingErrorFmtStr, - "REAL", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::REAL || + requestedType->kind() == TypeKind::DOUBLE, + kTypeMappingErrorFmtStr, + "REAL", + requestedType->toString()); + } return REAL(); case thrift::Type::type::DOUBLE: - VELOX_CHECK( - !requestedType || requestedType->kind() == TypeKind::DOUBLE, - kTypeMappingErrorFmtStr, - "DOUBLE", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->kind() == TypeKind::DOUBLE, + kTypeMappingErrorFmtStr, + "DOUBLE", + requestedType->toString()); + } return DOUBLE(); case thrift::Type::type::BYTE_ARRAY: case thrift::Type::type::FIXED_LEN_BYTE_ARRAY: if (requestedType && requestedType->isVarchar()) { return VARCHAR(); } else { - VELOX_CHECK( - !requestedType || requestedType->isVarbinary(), - kTypeMappingErrorFmtStr, - "VARBINARY", - requestedType->toString()); + if (enableRequestedTypeCheck) { + VELOX_CHECK( + !requestedType || requestedType->isVarbinary(), + kTypeMappingErrorFmtStr, + "VARBINARY", + requestedType->toString()); + } return VARBINARY(); } @@ -1066,12 +1099,15 @@ class ParquetRowReader::Impl { options_.timestampPrecision()); requestedType_ = options_.requestedType() ? options_.requestedType() : readerBase_->schema(); + columnReaderOptions_ = + dwio::common::makeColumnReaderOptions(readerBase_->options()); columnReader_ = ParquetColumnReader::build( columnReaderOptions_, requestedType_, readerBase_->schemaWithId(), // Id is schema id params, - *options_.scanSpec()); + *options_.scanSpec(), + pool_); columnReader_->setIsTopLevel(); filterRowGroups(); @@ -1081,9 +1117,6 @@ class ParquetRowReader::Impl { // table scan. advanceToNextRowGroup(); } - - columnReaderOptions_ = - dwio::common::makeColumnReaderOptions(readerBase_->options()); } void filterRowGroups() { diff --git a/velox/dwio/parquet/reader/RepeatedColumnReader.cpp b/velox/dwio/parquet/reader/RepeatedColumnReader.cpp index 8cd75156747a..7c2ed6326eda 100644 --- a/velox/dwio/parquet/reader/RepeatedColumnReader.cpp +++ b/velox/dwio/parquet/reader/RepeatedColumnReader.cpp @@ -33,6 +33,9 @@ PageReader* readLeafRepDefs( return nullptr; } auto pageReader = reader->formatData().as().reader(); + if (pageReader == nullptr) { + return nullptr; + } pageReader->decodeRepDefs(numTop); return pageReader; } @@ -114,7 +117,8 @@ MapColumnReader::MapColumnReader( const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec) + common::ScanSpec& scanSpec, + memory::MemoryPool& pool) : dwio::common::SelectiveMapColumnReader( requestedType, fileType, @@ -128,13 +132,15 @@ MapColumnReader::MapColumnReader( keyChildType, fileType_->childAt(0), params, - *scanSpec.children()[0]); + *scanSpec.children()[0], + pool); elementReader_ = ParquetColumnReader::build( columnReaderOptions, elementChildType, fileType_->childAt(1), params, - *scanSpec.children()[1]); + *scanSpec.children()[1], + pool); reinterpret_cast(fileType.get()) ->makeLevelInfo(levelInfo_); children_ = {keyReader_.get(), elementReader_.get()}; @@ -232,7 +238,8 @@ ListColumnReader::ListColumnReader( const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec) + common::ScanSpec& scanSpec, + memory::MemoryPool& pool) : dwio::common::SelectiveListColumnReader( requestedType, fileType, @@ -244,7 +251,8 @@ ListColumnReader::ListColumnReader( childType, fileType_->childAt(0), params, - *scanSpec.children()[0]); + *scanSpec.children()[0], + pool); reinterpret_cast(fileType.get()) ->makeLevelInfo(levelInfo_); children_ = {child_.get()}; diff --git a/velox/dwio/parquet/reader/RepeatedColumnReader.h b/velox/dwio/parquet/reader/RepeatedColumnReader.h index 823e874b2f06..066cee7f7cd0 100644 --- a/velox/dwio/parquet/reader/RepeatedColumnReader.h +++ b/velox/dwio/parquet/reader/RepeatedColumnReader.h @@ -61,7 +61,8 @@ class MapColumnReader : public dwio::common::SelectiveMapColumnReader { const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec); + common::ScanSpec& scanSpec, + memory::MemoryPool& pool); void prepareRead( vector_size_t offset, @@ -118,7 +119,8 @@ class ListColumnReader : public dwio::common::SelectiveListColumnReader { const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec); + common::ScanSpec& scanSpec, + memory::MemoryPool& pool); void prepareRead( vector_size_t offset, diff --git a/velox/dwio/parquet/reader/StructColumnReader.cpp b/velox/dwio/parquet/reader/StructColumnReader.cpp index 694f334c51a2..6fb84c0cdd69 100644 --- a/velox/dwio/parquet/reader/StructColumnReader.cpp +++ b/velox/dwio/parquet/reader/StructColumnReader.cpp @@ -31,18 +31,33 @@ StructColumnReader::StructColumnReader( const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec) - : SelectiveStructColumnReader(requestedType, fileType, params, scanSpec) { + common::ScanSpec& scanSpec, + memory::MemoryPool& pool) + : SelectiveStructColumnReader( + columnReaderOptions, + requestedType, + fileType, + params, + scanSpec, + /*isRoot=*/false) { auto& childSpecs = scanSpec_->stableChildren(); + const bool useColumnNames = + columnReaderOptions.useColumnNamesForColumnMapping_; + std::vector missingFields; for (auto i = 0; i < childSpecs.size(); ++i) { auto childSpec = childSpecs[i]; - if (childSpec->isConstant() || isChildMissing(*childSpec)) { + if (childSpec->isConstant() && + (!useColumnNames && isChildMissing(*childSpec))) { childSpec->setSubscript(kConstantChildSpecSubscript); continue; } if (!childSpecs[i]->readFromFile()) { continue; } + if (useColumnNames && isChildMissing(*childSpec)) { + missingFields.emplace_back(i); + continue; + } auto childFileType = fileType_->childByName(childSpec->fieldName()); auto childRequestedType = requestedType_->asRow().findChild(childSpec->fieldName()); @@ -51,10 +66,30 @@ StructColumnReader::StructColumnReader( childRequestedType, childFileType, params, - *childSpec)); + *childSpec, + pool)); childSpecs[i]->setSubscript(children_.size() - 1); } + + // 'missingFields' is not empty only when using column names for column + // mapping. + if (missingFields.size() > 0) { + // Set the struct as null if all the subfields in the requested type are + // missing and the number of subfields is more than one. + if (childSpecs.size() > 1 && missingFields.size() == childSpecs.size()) { + scanSpec_->setConstantValue( + BaseVector::createNullConstant(requestedType_, 1, &pool)); + } else { + // Set null constant for the missing subfield of requested type. + auto rowTypePtr = asRowType(requestedType_); + for (int channel : missingFields) { + childSpecs[channel]->setConstantValue(BaseVector::createNullConstant( + rowTypePtr->findChild(childSpecs[channel]->fieldName()), 1, &pool)); + } + } + } + auto type = reinterpret_cast(fileType_.get()); if (type->parent()) { levelMode_ = reinterpret_cast(fileType_.get()) @@ -64,7 +99,10 @@ StructColumnReader::StructColumnReader( // this and the child. auto child = childForRepDefs_; for (;;) { - assert(child); + if (child == nullptr) { + levelMode_ = LevelMode::kNulls; + break; + } if (child->fileType().type()->kind() == TypeKind::ARRAY || child->fileType().type()->kind() == TypeKind::MAP) { levelMode_ = LevelMode::kStructOverLists; @@ -101,7 +139,6 @@ StructColumnReader::findBestLeaf() { best = child; } } - assert(best); return best; } diff --git a/velox/dwio/parquet/reader/StructColumnReader.h b/velox/dwio/parquet/reader/StructColumnReader.h index 37acbdc2e5ea..015dcbc1babc 100644 --- a/velox/dwio/parquet/reader/StructColumnReader.h +++ b/velox/dwio/parquet/reader/StructColumnReader.h @@ -37,7 +37,8 @@ class StructColumnReader : public dwio::common::SelectiveStructColumnReader { const TypePtr& requestedType, const std::shared_ptr& fileType, ParquetParams& params, - common::ScanSpec& scanSpec); + common::ScanSpec& scanSpec, + memory::MemoryPool& pool); void read(int64_t offset, const RowSet& rows, const uint64_t* incomingNulls) override; diff --git a/velox/dwio/parquet/tests/common/CMakeLists.txt b/velox/dwio/parquet/tests/common/CMakeLists.txt index e767f4cc1671..9e9e82498353 100644 --- a/velox/dwio/parquet/tests/common/CMakeLists.txt +++ b/velox/dwio/parquet/tests/common/CMakeLists.txt @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -add_executable(velox_dwio_parquet_common_test LevelConversionTest.cpp) +add_executable(velox_dwio_parquet_common_test LevelConversionTest.cpp + UnicodeUtilTest.cpp) add_test(velox_dwio_parquet_common_test velox_dwio_parquet_common_test) target_link_libraries( diff --git a/velox/dwio/parquet/tests/common/UnicodeUtilTest.cpp b/velox/dwio/parquet/tests/common/UnicodeUtilTest.cpp new file mode 100644 index 000000000000..9bc529ad2576 --- /dev/null +++ b/velox/dwio/parquet/tests/common/UnicodeUtilTest.cpp @@ -0,0 +1,152 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "velox/dwio/parquet/common/UnicodeUtil.h" +#include + +using namespace facebook::velox::parquet; + +class UnicodeUtilTest : public testing::Test {}; + +TEST_F(UnicodeUtilTest, truncateStringMin) { + // ASCII string. + std::string ascii = "Hello, world!"; + EXPECT_EQ( + UnicodeUtil::truncateStringMin(ascii.c_str(), ascii.length(), 0), ""); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(ascii.c_str(), ascii.length(), 1), "H"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(ascii.c_str(), ascii.length(), 5), + "Hello"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(ascii.c_str(), ascii.length(), 13), ascii); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(ascii.c_str(), ascii.length(), 20), ascii); + + // String with multi-bytes characters. + std::string unicode = "Hello, 世界!"; + EXPECT_EQ( + UnicodeUtil::truncateStringMin(unicode.c_str(), unicode.length(), 7), + "Hello, "); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(unicode.c_str(), unicode.length(), 8), + "Hello, 世"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(unicode.c_str(), unicode.length(), 9), + "Hello, 世界"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(unicode.c_str(), unicode.length(), 10), + unicode); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(unicode.c_str(), unicode.length(), 20), + unicode); + + // String with emoji (surrogate pairs). + std::string emoji = "Hello 🌍!"; + EXPECT_EQ( + UnicodeUtil::truncateStringMin(emoji.c_str(), emoji.length(), 6), + "Hello "); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(emoji.c_str(), emoji.length(), 7), + "Hello 🌍"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(emoji.c_str(), emoji.length(), 8), emoji); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(emoji.c_str(), emoji.length(), 10), emoji); + + std::string empty = ""; + EXPECT_EQ( + UnicodeUtil::truncateStringMin(empty.c_str(), empty.length(), 0), ""); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(empty.c_str(), empty.length(), 5), ""); + + std::string mixed = "café世界🌍"; + EXPECT_EQ( + UnicodeUtil::truncateStringMin(mixed.c_str(), mixed.length(), 3), "caf"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(mixed.c_str(), mixed.length(), 4), "café"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(mixed.c_str(), mixed.length(), 5), + "café世"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(mixed.c_str(), mixed.length(), 6), + "café世界"); + EXPECT_EQ( + UnicodeUtil::truncateStringMin(mixed.c_str(), mixed.length(), 7), mixed); +} + +TEST_F(UnicodeUtilTest, truncateStringMax) { + std::string ascii = "Hello, world!"; + auto result = + UnicodeUtil::truncateStringMax(ascii.c_str(), ascii.length(), 0); + EXPECT_EQ(result, ""); + result = UnicodeUtil::truncateStringMax(ascii.c_str(), ascii.length(), 5); + EXPECT_EQ(result, "Hellp"); // 'o' -> 'p'. + result = UnicodeUtil::truncateStringMax( + ascii.c_str(), ascii.length(), ascii.length()); + EXPECT_EQ(result, ascii); + + ascii = "Customer#000001500"; + result = UnicodeUtil::truncateStringMax(ascii.c_str(), ascii.length(), 16); + EXPECT_EQ(result, "Customer#0000016"); //. '5' -> '6'. + + std::string unicode = "Hello, 世界!"; + result = UnicodeUtil::truncateStringMax(unicode.c_str(), unicode.length(), 8); + EXPECT_EQ(result, "Hello, 丗"); + + // No truncation needed. + std::string shortString = "Hi"; + result = UnicodeUtil::truncateStringMax( + shortString.c_str(), shortString.length(), 2); + EXPECT_EQ(result, shortString); + result = UnicodeUtil::truncateStringMax( + shortString.c_str(), shortString.length(), 20); + EXPECT_EQ(result, shortString); + + // Last character is already at maximum value. + std::string maxChar = + "Hello\U0010FFFF"; // U0010FFFF is maximum Unicode code point. + result = UnicodeUtil::truncateStringMax(maxChar.c_str(), maxChar.length(), 6); + EXPECT_EQ(result, maxChar); + + std::string empty = ""; + result = UnicodeUtil::truncateStringMax(empty.c_str(), empty.length(), 0); + EXPECT_EQ(result, ""); + result = UnicodeUtil::truncateStringMax(empty.c_str(), empty.length(), 5); + EXPECT_EQ(result, ""); + + std::string single = "a"; + result = UnicodeUtil::truncateStringMax(single.c_str(), single.length(), 1); + EXPECT_EQ(result, "a"); + + std::string zChar = "zz"; + result = UnicodeUtil::truncateStringMax(zChar.c_str(), zChar.length(), 1); + EXPECT_EQ(result, "{"); // 'z' -> '{'. + + // Emoji increment test + std::string emojiTest = "🌍!!"; + result = + UnicodeUtil::truncateStringMax(emojiTest.c_str(), emojiTest.length(), 1); + EXPECT_EQ(result, "\U0001F30E"); // U1F30D (🌍) -> U1F30E. + + std::string multiByteTest = "café+"; + result = UnicodeUtil::truncateStringMax( + multiByteTest.c_str(), multiByteTest.length(), 3); + EXPECT_EQ(result, "cag"); // 'f' -> 'g'. + result = UnicodeUtil::truncateStringMax( + multiByteTest.c_str(), multiByteTest.length(), 4); + EXPECT_EQ(result, "cafê"); // 'é' -> 'ê'. +} diff --git a/velox/dwio/parquet/tests/reader/ParquetTableScanTest.cpp b/velox/dwio/parquet/tests/reader/ParquetTableScanTest.cpp index 96c4a3a40513..95393458daec 100644 --- a/velox/dwio/parquet/tests/reader/ParquetTableScanTest.cpp +++ b/velox/dwio/parquet/tests/reader/ParquetTableScanTest.cpp @@ -297,6 +297,11 @@ class ParquetTableScanTest : public HiveConnectorTestBase { "SELECT t from tmp where t != TIMESTAMP '2000-09-12 22:36:29'"); } + const std::vector>& splits() + const { + return splits_; + } + private: RowTypePtr getRowType(std::vector&& outputColumnNames) const { std::vector types; @@ -541,6 +546,23 @@ TEST_F(ParquetTableScanTest, array) { assertSelectWithFilter( {"repeatedInt"}, {}, "", "SELECT UNNEST(array[array[1,2,3]])"); + + // Requested type does not match the element type. + auto rowType = ROW({"repeatedInt"}, {ARRAY(INTEGER())}); + parse::ParseOptions options; + + auto plan = PlanBuilder(pool_.get()) + .setParseOptions(options) + .tableScan(rowType, {}, "", rowType, {}) + .planNode(); + + AssertQueryBuilder(plan, duckDbQueryRunner_) + .connectorSessionProperty( + kHiveConnectorId, + HiveConfig::kEnableRequestedTypeCheckSession, + "false") + .splits({makeSplit(getExampleFilePath("old_repeated_int.parquet"))}) + .assertResults("SELECT UNNEST(array[array[1,2,3]])"); } // Optional array with required elements. @@ -1177,6 +1199,133 @@ TEST_F(ParquetTableScanTest, schemaMatch) { assertEqualVectors(rows->childAt(2), nullVector); } +TEST_F(ParquetTableScanTest, structMatchByName) { + const auto assertSelectUseColumnNames = + [this]( + const RowTypePtr& outputType, + const std::string& sql, + const std::string& remainingFilter = "") { + const auto plan = + PlanBuilder().tableScan(outputType, {}, remainingFilter).planNode(); + AssertQueryBuilder(plan, duckDbQueryRunner_) + .connectorSessionProperty( + kHiveConnectorId, + HiveConfig::kParquetUseColumnNamesSession, + "true") + .splits(splits()) + .assertResults(sql); + }; + + std::vector values = {2}; + const auto id = makeFlatVector(values); + const auto name = makeRowVector( + {"first", "last"}, + { + makeFlatVector({"Janet"}), + makeFlatVector({"Jones"}), + }); + const auto address = makeFlatVector({"567 Maple Drive"}); + auto vector = makeRowVector({"id", "name", "address"}, {id, name, address}); + + WriterOptions options; + auto file = TempFilePath::create(); + writeToParquetFile(file->getPath(), {vector}, options); + + loadData(file->getPath(), asRowType(vector->type()), vector); + assertSelect({"id", "name", "address"}, "SELECT id, name, address from tmp"); + + // Add one non-existing subfield 'middle' to the 'name' field and rename filed + // 'address'. + auto rowType = + ROW({"id", "name", "email"}, + {BIGINT(), + ROW({"first", "middle", "last"}, {VARCHAR(), VARCHAR(), VARCHAR()}), + VARCHAR()}); + loadData(file->getPath(), rowType, vector); + assertSelectUseColumnNames( + rowType, "SELECT 2, ('Janet', null, 'Jones'), null"); + + // Filter pushdown on the non-existing field. + assertSelectUseColumnNames( + rowType, "SELECT * from tmp where false", "not(is_null(name.middle))"); + + // Rename subfields of the 'name' field. + rowType = + ROW({"id", "name", "address"}, + {BIGINT(), ROW({"a", "b"}, {VARCHAR(), VARCHAR()}), VARCHAR()}); + loadData(file->getPath(), rowType, vector); + assertSelectUseColumnNames(rowType, "SELECT 2, null, '567 Maple Drive'"); + + // Filter pushdown on the NULL subfield. + assertSelectUseColumnNames( + rowType, "SELECT * from tmp where false", "not(is_null(name))"); + + // Deletion of one subfield from the 'name' field. + rowType = + ROW({"id", "name", "address"}, + {BIGINT(), ROW({"full"}, {VARCHAR()}), VARCHAR()}); + loadData(file->getPath(), rowType, vector); + assertSelectUseColumnNames(rowType, "SELECT 2, row(null), '567 Maple Drive'"); + + // Filter pushdown on the non-existing subfield. + assertSelectUseColumnNames( + rowType, "SELECT * from tmp where false", "not(is_null(name.full))"); + + // No subfield in the 'name' field. + rowType = ROW({"id", "name", "address"}, {BIGINT(), ROW({}, {}), VARCHAR()}); + const auto op = PlanBuilder() + .startTableScan() + .outputType(rowType) + .dataColumns(rowType) + .endTableScan() + .planNode(); + const auto split = makeSplit(file->getPath()); + const auto result = AssertQueryBuilder(op) + .connectorSessionProperty( + kHiveConnectorId, + HiveConfig::kParquetUseColumnNamesSession, + "true") + .split(split) + .copyResults(pool()); + const auto rows = result->as(); + const auto expected = makeRowVector(ROW({}, {}), 1); + assertEqualVectors(expected, rows->childAt(1)); + + // Case sensitivity when matching by name. + vector = makeRowVector( + {"id", "name", "address"}, + {id, + makeRowVector( + {"FIRST", "LAST"}, + { + makeFlatVector({"Janet"}), + makeFlatVector({"Jones"}), + }), + address}); + file = TempFilePath::create(); + writeToParquetFile(file->getPath(), {vector}, options); + + rowType = + ROW({"id", "name", "address"}, + {BIGINT(), + ROW({"first", "middle", "last"}, {VARCHAR(), VARCHAR(), VARCHAR()}), + VARCHAR()}); + loadData(file->getPath(), rowType, vector); + assertSelectUseColumnNames(rowType, "SELECT 2, null, '567 Maple Drive'"); + + // Case insensitivity when matching by name and reading as lower case. + auto plan = PlanBuilder().tableScan(rowType, {}, "", rowType).planNode(); + AssertQueryBuilder(plan, duckDbQueryRunner_) + .connectorSessionProperty( + kHiveConnectorId, HiveConfig::kParquetUseColumnNamesSession, "true") + .connectorSessionProperty( + kHiveConnectorId, + HiveConfig::kFileColumnNamesReadAsLowerCaseSession, + "true") + .splits(splits()) + .assertResults("SELECT 2, ('Janet', null, 'Jones'), '567 Maple Drive'"); +} + TEST_F(ParquetTableScanTest, deltaByteArray) { auto a = makeFlatVector({"axis", "axle", "babble", "babyhood"}); auto expected = makeRowVector({"a"}, {a}); diff --git a/velox/dwio/parquet/thrift/ParquetThriftTypes.cpp b/velox/dwio/parquet/thrift/ParquetThriftTypes.cpp index 674c99300f76..757472a58ee8 100644 --- a/velox/dwio/parquet/thrift/ParquetThriftTypes.cpp +++ b/velox/dwio/parquet/thrift/ParquetThriftTypes.cpp @@ -677,6 +677,11 @@ void Statistics::__set_distinct_count(const int64_t val) { __isset.distinct_count = true; } +void Statistics::__set_nan_count(const int64_t val) { + this->nan_count = val; + __isset.nan_count = true; +} + void Statistics::__set_max_value(const std::string& val) { this->max_value = val; __isset.max_value = true; @@ -820,6 +825,7 @@ void swap(Statistics& a, Statistics& b) { swap(a.min, b.min); swap(a.null_count, b.null_count); swap(a.distinct_count, b.distinct_count); + swap(a.nan_count, b.nan_count); swap(a.max_value, b.max_value); swap(a.min_value, b.min_value); swap(a.__isset, b.__isset); @@ -830,6 +836,7 @@ Statistics::Statistics(const Statistics& other0) { min = other0.min; null_count = other0.null_count; distinct_count = other0.distinct_count; + nan_count = other0.nan_count; max_value = other0.max_value; min_value = other0.min_value; __isset = other0.__isset; @@ -839,6 +846,7 @@ Statistics& Statistics::operator=(const Statistics& other1) { min = other1.min; null_count = other1.null_count; distinct_count = other1.distinct_count; + nan_count = other1.nan_count; max_value = other1.max_value; min_value = other1.min_value; __isset = other1.__isset; diff --git a/velox/dwio/parquet/thrift/ParquetThriftTypes.h b/velox/dwio/parquet/thrift/ParquetThriftTypes.h index 0fd63e83cc0e..bdaee2252136 100644 --- a/velox/dwio/parquet/thrift/ParquetThriftTypes.h +++ b/velox/dwio/parquet/thrift/ParquetThriftTypes.h @@ -466,12 +466,14 @@ typedef struct _Statistics__isset { min(false), null_count(false), distinct_count(false), + nan_count(false), max_value(false), min_value(false) {} bool max : 1; bool min : 1; bool null_count : 1; bool distinct_count : 1; + bool nan_count : 1; bool max_value : 1; bool min_value : 1; } _Statistics__isset; @@ -489,6 +491,7 @@ class Statistics : public virtual apache::thrift::TBase { min(), null_count(0), distinct_count(0), + nan_count(0), max_value(), min_value() {} @@ -516,6 +519,11 @@ class Statistics : public virtual apache::thrift::TBase { * count of distinct values occurring */ int64_t distinct_count; + + /** + * count of NaN values occurring + */ + int64_t nan_count; /** * Min and max values for the column, determined by its ColumnOrder. * @@ -535,6 +543,8 @@ class Statistics : public virtual apache::thrift::TBase { void __set_distinct_count(const int64_t val); + void __set_nan_count(const int64_t val); + void __set_max_value(const std::string& val); void __set_min_value(const std::string& val); @@ -556,6 +566,10 @@ class Statistics : public virtual apache::thrift::TBase { return false; else if (__isset.distinct_count && !(distinct_count == rhs.distinct_count)) return false; + if (__isset.nan_count != rhs.__isset.nan_count) + return false; + else if (__isset.nan_count && !(nan_count == rhs.nan_count)) + return false; if (__isset.max_value != rhs.__isset.max_value) return false; else if (__isset.max_value && !(max_value == rhs.max_value)) diff --git a/velox/dwio/parquet/writer/Writer.cpp b/velox/dwio/parquet/writer/Writer.cpp index 2444fa247cc5..6e635bf7b8a2 100644 --- a/velox/dwio/parquet/writer/Writer.cpp +++ b/velox/dwio/parquet/writer/Writer.cpp @@ -22,6 +22,7 @@ #include "velox/common/config/Config.h" #include "velox/common/testutil/TestValue.h" #include "velox/core/QueryConfig.h" +#include "velox/dwio/parquet/writer/arrow/ArrowSchema.h" #include "velox/dwio/parquet/writer/arrow/Properties.h" #include "velox/dwio/parquet/writer/arrow/Writer.h" #include "velox/exec/MemoryReclaimer.h" @@ -193,47 +194,70 @@ void validateSchemaRecursive(const RowTypePtr& schema) { } } -std::shared_ptr<::arrow::Field> updateFieldNameRecursive( +std::shared_ptr<::arrow::Field> updateFieldRecursive( const std::shared_ptr<::arrow::Field>& field, const Type& type, - const std::string& name = "") { + const std::string& name = "", + const ParquetFieldId* fieldId = nullptr) { + std::shared_ptr<::arrow::Field> newField{nullptr}; if (type.isRow()) { auto& rowType = type.asRow(); - auto newField = field->WithName(name); + newField = field->WithName(name); auto structType = std::dynamic_pointer_cast<::arrow::StructType>(newField->type()); auto childrenSize = rowType.size(); std::vector> newFields; - newFields.reserve(childrenSize); - for (auto i = 0; i < childrenSize; i++) { - newFields.push_back(updateFieldNameRecursive( - structType->fields()[i], *rowType.childAt(i), rowType.nameOf(i))); + newFields.reserve(rowType.size()); + for (auto i = 0; i < rowType.size(); ++i) { + const auto* childSetting = fieldId ? &fieldId->children.at(i) : nullptr; + newFields.push_back(updateFieldRecursive( + structType->fields()[i], + *rowType.childAt(i), + rowType.nameOf(i), + childSetting)); } - return newField->WithType(::arrow::struct_(newFields)); + newField = newField->WithType(::arrow::struct_(newFields)); } else if (type.isArray()) { - auto newField = field->WithName(name); + newField = field->WithName(name); auto listType = std::dynamic_pointer_cast<::arrow::BaseListType>(newField->type()); auto elementType = type.asArray().elementType(); auto elementField = listType->value_field(); - return newField->WithType( - ::arrow::list(updateFieldNameRecursive(elementField, *elementType))); + const auto* childSetting = fieldId ? &fieldId->children.at(0) : nullptr; + + auto updatedElementField = + updateFieldRecursive(elementField, *elementType, name, childSetting); + newField = newField->WithType(::arrow::list(updatedElementField)); } else if (type.isMap()) { auto mapType = type.asMap(); - auto newField = field->WithName(name); + newField = field->WithName(name); auto arrowMapType = std::dynamic_pointer_cast<::arrow::MapType>(newField->type()); - auto newKeyField = - updateFieldNameRecursive(arrowMapType->key_field(), *mapType.keyType()); - auto newValueField = updateFieldNameRecursive( - arrowMapType->item_field(), *mapType.valueType()); - return newField->WithType( - ::arrow::map(newKeyField->type(), newValueField->type())); + const auto* keySetting = fieldId ? &fieldId->children.at(0) : nullptr; + const auto* valueSetting = fieldId ? &fieldId->children.at(1) : nullptr; + auto newKeyField = updateFieldRecursive( + arrowMapType->key_field(), + *mapType.keyType(), + mapType.nameOf(0), + keySetting); + auto newValueField = updateFieldRecursive( + arrowMapType->item_field(), + *mapType.valueType(), + mapType.nameOf(1), + valueSetting); + newField = newField->WithType( + std::make_shared<::arrow::MapType>(newKeyField, newValueField)); } else if (name != "") { - return field->WithName(name); + newField = field->WithName(name); } else { - return field; + newField = field; + } + + if (fieldId) { + newField = + newField->WithMetadata(arrow::arrow::FieldIdMetadata(fieldId->fieldId)); } + return newField; } std::optional getTimestampUnit( @@ -351,6 +375,9 @@ Writer::Writer( setMemoryReclaimers(); writeInt96AsTimestamp_ = options.writeInt96AsTimestamp; arrowMemoryPool_ = options.arrowMemoryPool; + dataFileStats_ = std::make_shared(); + parquetFieldIds_ = options.parquetFieldIds; + statsCollector_ = options.fileStatsCollector; } Writer::Writer( @@ -446,8 +473,11 @@ void Writer::write(const VectorPtr& data) { std::vector> newFields; auto childSize = schema_->size(); for (auto i = 0; i < childSize; i++) { - newFields.push_back(updateFieldNameRecursive( - arrowSchema->fields()[i], *schema_->childAt(i), schema_->nameOf(i))); + newFields.push_back(updateFieldRecursive( + arrowSchema->fields()[i], + *schema_->childAt(i), + schema_->nameOf(i), + parquetFieldIds_ ? &parquetFieldIds_->at(i) : nullptr)); } PARQUET_ASSIGN_OR_THROW( @@ -491,6 +521,11 @@ void Writer::close() { if (arrowContext_->writer) { PARQUET_THROW_NOT_OK(arrowContext_->writer->Close()); + if (statsCollector_) { + auto fileMetadata = arrowContext_->writer->metadata(); + statsCollector_->collectStats( + static_cast(&fileMetadata), dataFileStats_); + } arrowContext_->writer.reset(); } PARQUET_THROW_NOT_OK(stream_->Close()); diff --git a/velox/dwio/parquet/writer/Writer.h b/velox/dwio/parquet/writer/Writer.h index 7deca7ee670b..5df2238478f1 100644 --- a/velox/dwio/parquet/writer/Writer.h +++ b/velox/dwio/parquet/writer/Writer.h @@ -88,6 +88,13 @@ class LambdaFlushPolicy : public DefaultFlushPolicy { std::function lambda_; }; +// Settings for specifying Parquet field IDs during write operations. +// Used to explicitly control field ID assignment in the Parquet schema. +struct ParquetFieldId { + int32_t fieldId; + std::vector children; +}; + struct WriterOptions : public dwio::common::WriterOptions { // Growth ratio passed to ArrowDataBufferSink. The default value is a // heuristic borrowed from @@ -116,6 +123,8 @@ struct WriterOptions : public dwio::common::WriterOptions { std::shared_ptr arrowMemoryPool; + std::shared_ptr> parquetFieldIds; + // Parsing session and hive configs. // This isn't a typo; session and hive connector config names are different @@ -214,6 +223,8 @@ class Writer : public dwio::common::Writer { std::shared_ptr arrowContext_; + std::shared_ptr> parquetFieldIds_; + std::unique_ptr flushPolicy_; const RowTypePtr schema_; diff --git a/velox/dwio/parquet/writer/arrow/ArrowSchema.cpp b/velox/dwio/parquet/writer/arrow/ArrowSchema.cpp index de5a2382198a..2bbef5c3b946 100644 --- a/velox/dwio/parquet/writer/arrow/ArrowSchema.cpp +++ b/velox/dwio/parquet/writer/arrow/ArrowSchema.cpp @@ -64,6 +64,8 @@ using ParquetType = Type; namespace { +static constexpr char FIELD_ID_KEY[] = "PARQUET:field_id"; + /// Increments levels according to the cardinality of node. void IncrementLevels(LevelInfo& current_levels, const schema::Node& node) { if (node.is_repeated()) { @@ -344,16 +346,6 @@ static Status GetTimestampMetadata( return Status::OK(); } -static constexpr char FIELD_ID_KEY[] = "PARQUET:field_id"; - -std::shared_ptr<::arrow::KeyValueMetadata> FieldIdMetadata(int field_id) { - if (field_id >= 0) { - return ::arrow::key_value_metadata({FIELD_ID_KEY}, {ToChars(field_id)}); - } else { - return nullptr; - } -} - int FieldIdFromMetadata( const std::shared_ptr& metadata) { if (!metadata) { @@ -1222,6 +1214,14 @@ Result ApplyOriginalMetadata( } // namespace +std::shared_ptr<::arrow::KeyValueMetadata> FieldIdMetadata(int32_t field_id) { + if (field_id >= 0) { + return ::arrow::key_value_metadata({FIELD_ID_KEY}, {ToChars(field_id)}); + } else { + return nullptr; + } +} + Status FieldToNode( const std::shared_ptr& field, const WriterProperties& properties, diff --git a/velox/dwio/parquet/writer/arrow/ArrowSchema.h b/velox/dwio/parquet/writer/arrow/ArrowSchema.h index 8302bc1cdb19..d60d2090f48b 100644 --- a/velox/dwio/parquet/writer/arrow/ArrowSchema.h +++ b/velox/dwio/parquet/writer/arrow/ArrowSchema.h @@ -195,5 +195,7 @@ struct PARQUET_EXPORT SchemaManifest { } }; +std::shared_ptr<::arrow::KeyValueMetadata> FieldIdMetadata(int32_t field_id); + } // namespace arrow } // namespace facebook::velox::parquet::arrow diff --git a/velox/dwio/parquet/writer/arrow/Metadata.cpp b/velox/dwio/parquet/writer/arrow/Metadata.cpp index ef978a01a747..418b1c8e60a7 100644 --- a/velox/dwio/parquet/writer/arrow/Metadata.cpp +++ b/velox/dwio/parquet/writer/arrow/Metadata.cpp @@ -101,10 +101,12 @@ static std::shared_ptr MakeTypedColumnStats( metadata.num_values - metadata.statistics.null_count, metadata.statistics.null_count, metadata.statistics.distinct_count, + metadata.statistics.nan_count, metadata.statistics.__isset.max_value || metadata.statistics.__isset.min_value, metadata.statistics.__isset.null_count, - metadata.statistics.__isset.distinct_count); + metadata.statistics.__isset.distinct_count, + metadata.statistics.__isset.nan_count); } // Default behavior return MakeStatistics( @@ -114,9 +116,11 @@ static std::shared_ptr MakeTypedColumnStats( metadata.num_values - metadata.statistics.null_count, metadata.statistics.null_count, metadata.statistics.distinct_count, + metadata.statistics.nan_count, metadata.statistics.__isset.max || metadata.statistics.__isset.min, metadata.statistics.__isset.null_count, - metadata.statistics.__isset.distinct_count); + metadata.statistics.__isset.distinct_count, + metadata.statistics.__isset.nan_count); } std::shared_ptr MakeColumnStats( @@ -389,6 +393,10 @@ class ColumnChunkMetaData::ColumnChunkMetaDataImpl { return std::nullopt; } + inline int32_t field_id() const { + return descr_->field_id(); + } + private: mutable std::shared_ptr possible_stats_; std::vector encodings_; @@ -535,6 +543,10 @@ int64_t ColumnChunkMetaData::total_compressed_size() const { return impl_->total_compressed_size(); } +int32_t ColumnChunkMetaData::field_id() const { + return impl_->field_id(); +} + std::unique_ptr ColumnChunkMetaData::crypto_metadata() const { return impl_->crypto_metadata(); diff --git a/velox/dwio/parquet/writer/arrow/Metadata.h b/velox/dwio/parquet/writer/arrow/Metadata.h index c69ee5a03d41..7cb7670a0387 100644 --- a/velox/dwio/parquet/writer/arrow/Metadata.h +++ b/velox/dwio/parquet/writer/arrow/Metadata.h @@ -187,6 +187,7 @@ class PARQUET_EXPORT ColumnChunkMetaData { int64_t index_page_offset() const; int64_t total_compressed_size() const; int64_t total_uncompressed_size() const; + int32_t field_id() const; std::unique_ptr crypto_metadata() const; std::optional GetColumnIndexLocation() const; std::optional GetOffsetIndexLocation() const; diff --git a/velox/dwio/parquet/writer/arrow/Schema.h b/velox/dwio/parquet/writer/arrow/Schema.h index e71493d751de..4130b3ad01b7 100644 --- a/velox/dwio/parquet/writer/arrow/Schema.h +++ b/velox/dwio/parquet/writer/arrow/Schema.h @@ -505,6 +505,10 @@ class PARQUET_EXPORT ColumnDescriptor { return node_; } + int32_t field_id() const { + return node_->field_id(); + } + std::string ToString() const; int type_length() const; diff --git a/velox/dwio/parquet/writer/arrow/Statistics.cpp b/velox/dwio/parquet/writer/arrow/Statistics.cpp index 757ac36b62e8..ba0e1eb12bad 100644 --- a/velox/dwio/parquet/writer/arrow/Statistics.cpp +++ b/velox/dwio/parquet/writer/arrow/Statistics.cpp @@ -35,10 +35,13 @@ #include "arrow/visit_data_inline.h" #include "velox/common/base/Exceptions.h" +#include "velox/dwio/parquet/common/UnicodeUtil.h" #include "velox/dwio/parquet/writer/arrow/Encoding.h" #include "velox/dwio/parquet/writer/arrow/Exception.h" #include "velox/dwio/parquet/writer/arrow/Platform.h" #include "velox/dwio/parquet/writer/arrow/Schema.h" +#include "velox/type/DecimalUtil.h" +#include "velox/type/HugeInt.h" using arrow::default_memory_pool; using arrow::MemoryPool; @@ -547,6 +550,17 @@ TypedComparatorImpl::GetMinMax( return GetMinMaxBinaryHelper(*this, values); } +template +std::string encodeDecimalToBigEndian(T value) { + uint8_t buffer[sizeof(T)]; + if constexpr (std::is_same_v) { + *reinterpret_cast(buffer) = ::arrow::bit_util::ToBigEndian(value); + } else if constexpr (std::is_same_v) { + *reinterpret_cast(buffer) = DecimalUtil::bigEndian(value); + } + return std::string(reinterpret_cast(buffer), sizeof(T)); +} + template class TypedStatisticsImpl : public TypedStatistics { public: @@ -590,9 +604,11 @@ class TypedStatisticsImpl : public TypedStatistics { int64_t num_values, int64_t null_count, int64_t distinct_count, + int64_t nan_count, bool has_min_max, bool has_null_count, bool has_distinct_count, + bool has_nan_count, MemoryPool* pool) : TypedStatisticsImpl(descr, pool) { TypedStatisticsImpl::IncrementNumValues(num_values); @@ -607,6 +623,12 @@ class TypedStatisticsImpl : public TypedStatistics { has_distinct_count_ = false; } + if (has_nan_count) { + IncrementNaNValues(nan_count); + } else { + has_nan_count_ = false; + } + if (!encoded_min.empty()) { PlainDecode(encoded_min, &min_); } @@ -619,6 +641,11 @@ class TypedStatisticsImpl : public TypedStatistics { bool HasDistinctCount() const override { return has_distinct_count_; }; + + bool HasNaNCount() const override { + return has_nan_count_; + }; + bool HasMinMax() const override { return has_min_max_; } @@ -635,6 +662,12 @@ class TypedStatisticsImpl : public TypedStatistics { num_values_ += n; } + void IncrementNaNValues(int64_t n) override { + if (n > 0) { + statistics_.nan_count += n; + has_nan_count_ = true; + } + } bool Equals(const Statistics& raw_other) const override { if (physical_type() != raw_other.physical_type()) return false; @@ -673,6 +706,10 @@ class TypedStatisticsImpl : public TypedStatistics { } else { this->has_null_count_ = false; } + if (other.HasNaNCount()) { + this->statistics_.nan_count += other.nan_count(); + this->has_nan_count_ = true; + } if (has_distinct_count_ && other.HasDistinctCount() && (distinct_count() == 0 || other.distinct_count() == 0)) { // We can merge distinct counts if either side is zero. @@ -742,6 +779,55 @@ class TypedStatisticsImpl : public TypedStatistics { return s; } + std::string MinValue() const override { + if constexpr (std::is_same_v) { + if (descr_->logical_type()->is_decimal()) { + return encodeDecimalToBigEndian(min_); + } + } + if constexpr (std::is_same_v) { + return encodeDecimalToBigEndian(min_); + } + if constexpr (std::is_same_v) { + // TODO: 16 is default value. See DEFAULT_WRITE_METRICS_MODE_DEFAULT in + // org.apache.iceberg.TableProperties. Need to support this table + // property. + const auto truncatedMin = UnicodeUtil::truncateStringMin( + reinterpret_cast(min_.ptr), min_.len, 16); + std::string s; + this->PlainEncode( + ByteArray( + truncatedMin.size(), + reinterpret_cast(truncatedMin.data())), + &s); + return s; + } + return EncodeMin(); + } + + std::string MaxValue() const override { + if constexpr (std::is_same_v) { + if (descr_->logical_type()->is_decimal()) { + return encodeDecimalToBigEndian(max_); + } + } + if constexpr (std::is_same_v) { + return encodeDecimalToBigEndian(max_); + } + if constexpr (std::is_same_v) { + const auto truncatedMax = UnicodeUtil::truncateStringMax( + reinterpret_cast(max_.ptr), max_.len, 16); + std::string s; + this->PlainEncode( + ByteArray( + truncatedMax.size(), + reinterpret_cast(truncatedMax.data())), + &s); + return s; + } + return EncodeMax(); + } + EncodedStatistics Encode() override { EncodedStatistics s; if (HasMinMax()) { @@ -756,6 +842,9 @@ class TypedStatisticsImpl : public TypedStatistics { if (HasDistinctCount()) { s.set_distinct_count(this->distinct_count()); } + if (has_nan_count_) { + s.set_nan_count(this->nan_count()); + } return s; } @@ -769,11 +858,26 @@ class TypedStatisticsImpl : public TypedStatistics { return num_values_; } + int64_t nan_count() const override { + return statistics_.nan_count; + } + + bool CompareMax(const Statistics& other) const override { + auto typedStats = dynamic_cast*>(&other); + return comparator_->Compare(max_, typedStats->max_) ? false : true; + } + + bool CompareMin(const Statistics& other) const override { + auto typedStats = dynamic_cast*>(&other); + return comparator_->Compare(min_, typedStats->min_) ? true : false; + } + private: const ColumnDescriptor* descr_; bool has_min_max_ = false; bool has_null_count_ = false; bool has_distinct_count_ = false; + bool has_nan_count_ = false; T min_; T max_; ::arrow::MemoryPool* pool_; @@ -803,6 +907,7 @@ class TypedStatisticsImpl : public TypedStatistics { void ResetCounts() { this->statistics_.null_count = 0; this->statistics_.distinct_count = 0; + this->statistics_.nan_count = 0; this->num_values_ = 0; } @@ -815,6 +920,7 @@ class TypedStatisticsImpl : public TypedStatistics { this->has_distinct_count_ = false; // Null count calculation is cheap and enabled by default. this->has_null_count_ = true; + this->has_nan_count_ = false; } void SetMinMaxPair(std::pair min_max) { @@ -841,6 +947,46 @@ class TypedStatisticsImpl : public TypedStatistics { max_buffer_.get()); } } + + int64_t CountNaN(const T* values, int64_t length) { + if constexpr (!std::is_floating_point_v) { + return 0; + } else { + int64_t count = 0; + for (auto i = 0; i < length; i++) { + const auto val = SafeLoad(values + i); + if (std::isnan(val)) { + count++; + } + } + return count; + } + } + + int64_t CountNaNSpaced( + const T* values, + int64_t length, + const uint8_t* valid_bits, + int64_t valid_bits_offset) { + if constexpr (!std::is_floating_point_v) { + return 0; + } else { + int64_t count = 0; + ::arrow::internal::VisitSetBitRunsVoid( + valid_bits, + valid_bits_offset, + length, + [&](int64_t position, int64_t run_length) { + for (auto i = 0; i < run_length; i++) { + const auto val = SafeLoad(values + i + position); + if (std::isnan(val)) { + count++; + } + } + }); + return count; + } + } }; template <> @@ -896,6 +1042,7 @@ void TypedStatisticsImpl::Update( if (num_values == 0) return; SetMinMaxPair(comparator_->GetMinMax(values, num_values)); + IncrementNaNValues(CountNaN(values, num_values)); } template @@ -916,6 +1063,8 @@ void TypedStatisticsImpl::UpdateSpaced( return; SetMinMaxPair(comparator_->GetMinMaxSpaced( values, num_spaced_values, valid_bits, valid_bits_offset)); + IncrementNaNValues( + CountNaNSpaced(values, num_spaced_values, valid_bits, valid_bits_offset)); } template @@ -1080,9 +1229,11 @@ std::shared_ptr Statistics::Make( num_values, encoded_stats->null_count, encoded_stats->distinct_count, + encoded_stats->nan_count, encoded_stats->has_min && encoded_stats->has_max, encoded_stats->has_null_count, encoded_stats->has_distinct_count, + encoded_stats->has_nan_count, pool); } @@ -1093,9 +1244,11 @@ std::shared_ptr Statistics::Make( int64_t num_values, int64_t null_count, int64_t distinct_count, + int64_t nan_count, bool has_min_max, bool has_null_count, bool has_distinct_count, + bool has_nan_count, ::arrow::MemoryPool* pool) { #define MAKE_STATS(CAP_TYPE, KLASS) \ case Type::CAP_TYPE: \ @@ -1106,9 +1259,11 @@ std::shared_ptr Statistics::Make( num_values, \ null_count, \ distinct_count, \ + nan_count, \ has_min_max, \ has_null_count, \ has_distinct_count, \ + has_nan_count, \ pool) switch (descr->physical_type()) { diff --git a/velox/dwio/parquet/writer/arrow/Statistics.h b/velox/dwio/parquet/writer/arrow/Statistics.h index 6abf66b0b20a..0b7b738c253a 100644 --- a/velox/dwio/parquet/writer/arrow/Statistics.h +++ b/velox/dwio/parquet/writer/arrow/Statistics.h @@ -141,11 +141,13 @@ class PARQUET_EXPORT EncodedStatistics { int64_t null_count = 0; int64_t distinct_count = 0; + int64_t nan_count = 0; bool has_min = false; bool has_max = false; bool has_null_count = false; bool has_distinct_count = false; + bool has_nan_count = false; // When all values in the statistics are null, it is set to true. // Otherwise, at least one value is not null, or we are not sure at all. @@ -170,7 +172,8 @@ class PARQUET_EXPORT EncodedStatistics { } bool is_set() const { - return has_min || has_max || has_null_count || has_distinct_count; + return has_min || has_max || has_null_count || has_distinct_count || + has_nan_count; } bool is_signed() const { @@ -204,6 +207,12 @@ class PARQUET_EXPORT EncodedStatistics { has_distinct_count = true; return *this; } + + EncodedStatistics& set_nan_count(int64_t value) { + nan_count = value; + has_nan_count = true; + return *this; + } }; /// \brief Base type for computing column statistics while writing a file @@ -227,10 +236,12 @@ class PARQUET_EXPORT Statistics { /// \param[in] num_values total number of values /// \param[in] null_count number of null values /// \param[in] distinct_count number of distinct values + /// \param[in] nan_count number of nan values /// \param[in] has_min_max whether the min/max statistics are set /// \param[in] has_null_count whether the null_count statistics are set /// \param[in] has_distinct_count whether the distinct_count statistics are - /// set \param[in] pool a memory pool to use for any memory allocations, + /// set \param[in] has_nan_count whether the nan_count statistics are set + /// \param[in] pool a memory pool to use for any memory allocations, /// optional static std::shared_ptr Make( const ColumnDescriptor* descr, @@ -239,9 +250,11 @@ class PARQUET_EXPORT Statistics { int64_t num_values, int64_t null_count, int64_t distinct_count, + int64_t nan_count, bool has_min_max, bool has_null_count, bool has_distinct_count, + bool has_nan_count, ::arrow::MemoryPool* pool = ::arrow::default_memory_pool()); // Helper function to convert EncodedStatistics to Statistics. @@ -268,6 +281,12 @@ class PARQUET_EXPORT Statistics { /// \brief The number of non-null values in the column virtual int64_t num_values() const = 0; + /// \brief Return true if the count of nan values is set + virtual bool HasNaNCount() const = 0; + + /// \brief The number of NaN values, may not be set + virtual int64_t nan_count() const = 0; + /// \brief Return true if the min and max statistics are set. Obtain /// with TypedStatistics::min and max virtual bool HasMinMax() const = 0; @@ -281,6 +300,12 @@ class PARQUET_EXPORT Statistics { /// \brief Plain-encoded maximum value virtual std::string EncodeMax() const = 0; + /// \brief Compatible minimum value with iceberg + virtual std::string MinValue() const = 0; + + /// \brief Compatible maximum value with iceberg + virtual std::string MaxValue() const = 0; + /// \brief The finalized encoded form of the statistics for transport virtual EncodedStatistics Encode() = 0; @@ -293,6 +318,24 @@ class PARQUET_EXPORT Statistics { /// \brief Check two Statistics for equality virtual bool Equals(const Statistics& other) const = 0; + /// \brief Return true if this object is greater than other + virtual bool CompareMax(const Statistics& other) const = 0; + + /// \brief Return true if this object is less than other + virtual bool CompareMin(const Statistics& other) const = 0; + + static std::shared_ptr CompareAndGetMax( + const std::shared_ptr& stats1, + const std::shared_ptr& stats2) { + return stats1->CompareMax(*stats2) ? stats1 : stats2; + } + + static std::shared_ptr CompareAndGetMin( + const std::shared_ptr& stats1, + const std::shared_ptr& stats2) { + return stats1->CompareMin(*stats2) ? stats1 : stats2; + } + protected: static std::shared_ptr Make( Type::type physical_type, @@ -369,6 +412,9 @@ class TypedStatistics : public Statistics { /// \brief Increments the number of values directly /// The same note on IncrementNullCount applies here virtual void IncrementNumValues(int64_t n) = 0; + + /// \brief Increments the NaN count directly + virtual void IncrementNaNValues(int64_t n) = 0; }; using BoolStatistics = TypedStatistics; @@ -414,9 +460,11 @@ std::shared_ptr> MakeStatistics( int64_t num_values, int64_t null_count, int64_t distinct_count, + int64_t nan_count, bool has_min_max, bool has_null_count, bool has_distinct_count, + bool has_nan_count, ::arrow::MemoryPool* pool = ::arrow::default_memory_pool()) { return std::static_pointer_cast>(Statistics::Make( descr, @@ -425,9 +473,11 @@ std::shared_ptr> MakeStatistics( num_values, null_count, distinct_count, + nan_count, has_min_max, has_null_count, has_distinct_count, + has_nan_count, pool)); } diff --git a/velox/dwio/parquet/writer/arrow/ThriftInternal.h b/velox/dwio/parquet/writer/arrow/ThriftInternal.h index a6383474d23f..dd0f900e3046 100644 --- a/velox/dwio/parquet/writer/arrow/ThriftInternal.h +++ b/velox/dwio/parquet/writer/arrow/ThriftInternal.h @@ -384,7 +384,9 @@ static inline facebook::velox::parquet::thrift::Statistics ToThrift( if (stats.has_distinct_count) { statistics.__set_distinct_count(stats.distinct_count); } - + if (stats.has_nan_count) { + statistics.__set_nan_count(stats.nan_count); + } return statistics; } diff --git a/velox/dwio/parquet/writer/arrow/tests/StatisticsTest.cpp b/velox/dwio/parquet/writer/arrow/tests/StatisticsTest.cpp index 85603e04b15f..9622c90fed60 100644 --- a/velox/dwio/parquet/writer/arrow/tests/StatisticsTest.cpp +++ b/velox/dwio/parquet/writer/arrow/tests/StatisticsTest.cpp @@ -344,9 +344,11 @@ class TestStatistics : public PrimitiveTypedTest { this->values_.size(), 0, 0, + 0, + true, true, true, - true); + false); auto statistics3 = MakeStatistics(this->schema_.Column(0)); std::vector valid_bits( @@ -610,9 +612,11 @@ void TestStatistics::TestMinMaxEncode() { this->values_.size(), 0, 0, + 0, + true, true, true, - true); + false); ASSERT_EQ(encoded_min, statistics2->EncodeMin()); ASSERT_EQ(encoded_max, statistics2->EncodeMax()); @@ -1495,6 +1499,7 @@ void CheckNaNs() { auto some_nan_stats = MakeStatistics(&descr); // Ingesting only nans should not yield valid min max AssertUnsetMinMax(some_nan_stats, all_nans); + EXPECT_EQ(some_nan_stats->nan_count(), all_nans.size()); // Ingesting a mix of NaNs and non-NaNs should not yield valid min max. AssertMinMaxAre(some_nan_stats, some_nans, min, max); // Ingesting only nans after a valid min/max, should have not effect @@ -1512,6 +1517,7 @@ void CheckNaNs() { 1.5f, max, -3.0f, -1.0f, nan, 2.0f, min, nan}; auto other_stats = MakeStatistics(&descr); AssertMinMaxAre(other_stats, other_nans, min, max); + EXPECT_EQ(other_stats->nan_count(), 2); } TEST(TestStatistic, NaNFloatValues) { diff --git a/velox/exec/AggregateCompanionAdapter.cpp b/velox/exec/AggregateCompanionAdapter.cpp index ea3c64c5c9ea..f77400e952d0 100644 --- a/velox/exec/AggregateCompanionAdapter.cpp +++ b/velox/exec/AggregateCompanionAdapter.cpp @@ -267,10 +267,13 @@ bool CompanionFunctionsRegistrar::registerPartialFunction( const core::QueryConfig& config) -> std::unique_ptr { if (auto func = getAggregateFunctionEntry(name)) { + core::AggregationNode::Step usedStep{ + core::AggregationNode::Step::kPartial}; if (!exec::isRawInput(step)) { - step = core::AggregationNode::Step::kIntermediate; + usedStep = core::AggregationNode::Step::kIntermediate; } - auto fn = func->factory(step, argTypes, resultType, config); + auto fn = + func->factory(usedStep, argTypes, resultType, config); VELOX_CHECK_NOT_NULL(fn); return std::make_unique< AggregateCompanionAdapter::PartialFunction>( @@ -409,26 +412,51 @@ bool CompanionFunctionsRegistrar::registerMergeExtractFunction( const std::vector& signatures, const AggregateFunctionMetadata& metadata, bool overwrite) { + bool registered = false; if (CompanionSignatures::hasSameIntermediateTypesAcrossSignatures( signatures)) { - return registerMergeExtractFunctionWithSuffix( - name, signatures, metadata, overwrite); + registered |= + registerMergeExtractFunctionWithSuffix(name, signatures, metadata, overwrite); } auto mergeExtractSignatures = CompanionSignatures::mergeExtractFunctionSignatures(signatures); if (mergeExtractSignatures.empty()) { - return false; + return registered; } auto mergeExtractFunctionName = CompanionSignatures::mergeExtractFunctionName(name); - return registerMergeExtractFunctionInternal( - name, - mergeExtractFunctionName, - std::move(mergeExtractSignatures), - metadata, - overwrite); + registered |= + exec::registerAggregateFunction( + mergeExtractFunctionName, + std::move(mergeExtractSignatures), + [name, mergeExtractFunctionName]( + core::AggregationNode::Step /*step*/, + const std::vector& argTypes, + const TypePtr& resultType, + const core::QueryConfig& config) -> std::unique_ptr { + if (auto func = getAggregateFunctionEntry(name)) { + auto fn = func->factory( + core::AggregationNode::Step::kFinal, + argTypes, + resultType, + config); + VELOX_CHECK_NOT_NULL(fn); + return std::make_unique< + AggregateCompanionAdapter::MergeExtractFunction>( + std::move(fn), resultType); + } + VELOX_FAIL( + "Original aggregation function {} not found: {}", + name, + mergeExtractFunctionName); + }, + metadata, + /*registerCompanionFunctions*/ false, + overwrite) + .mainFunction; + return registered; } VectorFunctionFactory getVectorFunctionFactory( diff --git a/velox/exec/MergeJoin.cpp b/velox/exec/MergeJoin.cpp index d7a5a37eec55..aecae7c77412 100644 --- a/velox/exec/MergeJoin.cpp +++ b/velox/exec/MergeJoin.cpp @@ -113,9 +113,10 @@ void MergeJoin::initialize() { isSemiFilterJoin(joinType_)) { joinTracker_ = JoinTracker(outputBatchSize_, pool()); } - } else if (joinNode_->isAntiJoin()) { + } else if (joinNode_->isAntiJoin() || joinNode_->isFullJoin()) { // Anti join needs to track the left side rows that have no match on the - // right. + // right. Full outer join needs to track the right side rows that have no + // match on the left. joinTracker_ = JoinTracker(outputBatchSize_, pool()); } @@ -392,7 +393,8 @@ bool MergeJoin::tryAddOutputRow( const RowVectorPtr& leftBatch, vector_size_t leftRow, const RowVectorPtr& rightBatch, - vector_size_t rightRow) { + vector_size_t rightRow, + bool isRightJoinForFullOuter) { if (outputSize_ == outputBatchSize_) { return false; } @@ -426,12 +428,15 @@ bool MergeJoin::tryAddOutputRow( filterRightInputProjections_); if (joinTracker_) { - if (isRightJoin(joinType_) || isRightSemiFilterJoin(joinType_)) { + if (isRightJoin(joinType_) || isRightSemiFilterJoin(joinType_) || + (isFullJoin(joinType_) && isRightJoinForFullOuter)) { // Record right-side row with a match on the left-side. - joinTracker_->addMatch(rightBatch, rightRow, outputSize_); + joinTracker_->addMatch( + rightBatch, rightRow, outputSize_, isRightJoinForFullOuter); } else { // Record left-side row with a match on the right-side. - joinTracker_->addMatch(leftBatch, leftRow, outputSize_); + joinTracker_->addMatch( + leftBatch, leftRow, outputSize_, isRightJoinForFullOuter); } } } @@ -441,7 +446,8 @@ bool MergeJoin::tryAddOutputRow( if (isAntiJoin(joinType_)) { VELOX_CHECK(joinTracker_.has_value()); // Record left-side row with a match on the right-side. - joinTracker_->addMatch(leftBatch, leftRow, outputSize_); + joinTracker_->addMatch( + leftBatch, leftRow, outputSize_, isRightJoinForFullOuter); } ++outputSize_; @@ -460,14 +466,15 @@ bool MergeJoin::prepareOutput( return true; } - if (isRightJoin(joinType_) && right != currentRight_) { - return true; - } - // If there is a new right, we need to flatten the dictionary. if (!isRightFlattened_ && right && currentRight_ != right) { flattenRightProjections(); } + + if (right != currentRight_) { + return true; + } + return false; } @@ -490,11 +497,15 @@ bool MergeJoin::prepareOutput( } } else { for (const auto& projection : leftProjections_) { + auto column = left->childAt(projection.inputChannel); + // Flatten the left column if the column already is DictionaryVector. + if (column->wrappedVector()->encoding() == + VectorEncoding::Simple::DICTIONARY) { + BaseVector::flattenVector(column); + } + column->clearContainingLazyAndWrapped(); localColumns[projection.outputChannel] = BaseVector::wrapInDictionary( - {}, - leftOutputIndices_, - outputBatchSize_, - left->childAt(projection.inputChannel)); + {}, leftOutputIndices_, outputBatchSize_, column); } } currentLeft_ = left; @@ -510,11 +521,10 @@ bool MergeJoin::prepareOutput( isRightFlattened_ = true; } else { for (const auto& projection : rightProjections_) { + auto column = right->childAt(projection.inputChannel); + column->clearContainingLazyAndWrapped(); localColumns[projection.outputChannel] = BaseVector::wrapInDictionary( - {}, - rightOutputIndices_, - outputBatchSize_, - right->childAt(projection.inputChannel)); + {}, rightOutputIndices_, outputBatchSize_, column); } isRightFlattened_ = false; } @@ -579,6 +589,39 @@ bool MergeJoin::prepareOutput( bool MergeJoin::addToOutput() { if (isRightJoin(joinType_) || isRightSemiFilterJoin(joinType_)) { return addToOutputForRightJoin(); + } else if (isFullJoin(joinType_) && filter_) { + if (!leftForRightJoinMatch_) { + leftForRightJoinMatch_ = leftMatch_; + rightForRightJoinMatch_ = rightMatch_; + } + + if (leftMatch_ && rightMatch_ && !leftJoinForFullFinished_) { + auto left = addToOutputForLeftJoin(); + if (!leftMatch_) { + leftJoinForFullFinished_ = true; + } + if (left) { + if (!leftMatch_) { + leftMatch_ = leftForRightJoinMatch_; + rightMatch_ = rightForRightJoinMatch_; + } + + return true; + } + } + + if (!leftMatch_ && !rightJoinForFullFinished_) { + leftMatch_ = leftForRightJoinMatch_; + rightMatch_ = rightForRightJoinMatch_; + rightJoinForFullFinished_ = true; + } + + auto right = addToOutputForRightJoin(); + + leftForRightJoinMatch_ = leftMatch_; + rightForRightJoinMatch_ = rightMatch_; + + return right; } else { return addToOutputForLeftJoin(); } @@ -727,7 +770,9 @@ bool MergeJoin::addToOutputForRightJoin() { } for (auto j = leftStartRow; j < leftEndRow; ++j) { - if (!tryAddOutputRow(leftBatch, j, rightBatch, i)) { + const auto isRightJoinForFullOuter = isFullJoin(joinType_); + if (!tryAddOutputRow( + leftBatch, j, rightBatch, i, isRightJoinForFullOuter)) { // If we run out of space in the current output_, we will need to // produce a buffer and continue processing left later. In this // case, we cannot leave left as a lazy vector, since we cannot have @@ -1141,7 +1186,7 @@ RowVectorPtr MergeJoin::doGetOutput() { isFullJoin(joinType_)) { // If output_ is currently wrapping a different buffer, return it // first. - if (prepareOutput(input_, nullptr)) { + if (prepareOutput(input_, rightInput_)) { output_->resize(outputSize_); return std::move(output_); } @@ -1166,7 +1211,7 @@ RowVectorPtr MergeJoin::doGetOutput() { if (isRightJoin(joinType_) || isFullJoin(joinType_)) { // If output_ is currently wrapping a different buffer, return it // first. - if (prepareOutput(nullptr, rightInput_)) { + if (prepareOutput(input_, rightInput_)) { output_->resize(outputSize_); return std::move(output_); } @@ -1218,6 +1263,8 @@ RowVectorPtr MergeJoin::doGetOutput() { endRightRow < rightInput_->size(), std::nullopt}; + leftJoinForFullFinished_ = false; + rightJoinForFullFinished_ = false; if (!leftMatch_->complete || !rightMatch_->complete) { if (!leftMatch_->complete) { // Need to continue looking for the end of match. @@ -1262,8 +1309,6 @@ RowVectorPtr MergeJoin::doGetOutput() { RowVectorPtr MergeJoin::applyFilter(const RowVectorPtr& output) { const auto numRows = output->size(); - RowVectorPtr fullOuterOutput = nullptr; - BufferPtr indices = allocateIndices(numRows, pool()); auto* rawIndices = indices->asMutable(); vector_size_t numPassed = 0; @@ -1280,84 +1325,41 @@ RowVectorPtr MergeJoin::applyFilter(const RowVectorPtr& output) { // If all matches for a given left-side row fail the filter, add a row to // the output with nulls for the right-side columns. - const auto onMiss = [&](auto row) { + const auto onMiss = [&](auto row, bool isRightJoinForFullOuter) { if (isSemiFilterJoin(joinType_)) { return; } rawIndices[numPassed++] = row; - if (isFullJoin(joinType_)) { - // For filtered rows, it is necessary to insert additional data - // to ensure the result set is complete. Specifically, we - // need to generate two records: one record containing the - // columns from the left table along with nulls for the - // right table, and another record containing the columns - // from the right table along with nulls for the left table. - // For instance, the current output is filtered based on the condition - // t > 1. - - // 1, 1 - // 2, 2 - // 3, 3 - - // In this scenario, we need to additionally insert a record 1, 1. - // Subsequently, we will set the values of the columns on the left to - // null and the values of the columns on the right to null as well. By - // doing so, we will obtain the final result set. - - // 1, null - // null, 1 - // 2, 2 - // 3, 3 - fullOuterOutput = BaseVector::create( - output->type(), output->size() + 1, pool()); - - for (auto i = 0; i < row + 1; ++i) { - for (auto j = 0; j < output->type()->size(); ++j) { - fullOuterOutput->childAt(j)->copy( - output->childAt(j).get(), i, i, 1); + if (!isRightJoin(joinType_)) { + if (isFullJoin(joinType_) && isRightJoinForFullOuter) { + for (auto& projection : leftProjections_) { + auto target = output->childAt(projection.outputChannel); + target->setNull(row, true); } - } - - for (auto j = 0; j < output->type()->size(); ++j) { - fullOuterOutput->childAt(j)->copy( - output->childAt(j).get(), row + 1, row, 1); - } - - for (auto i = row + 1; i < output->size(); ++i) { - for (auto j = 0; j < output->type()->size(); ++j) { - fullOuterOutput->childAt(j)->copy( - output->childAt(j).get(), i + 1, i, 1); + } else { + for (auto& projection : rightProjections_) { + auto target = output->childAt(projection.outputChannel); + target->setNull(row, true); } } - - for (auto& projection : leftProjections_) { - auto& target = fullOuterOutput->childAt(projection.outputChannel); - target->setNull(row, true); - } - - for (auto& projection : rightProjections_) { - auto& target = fullOuterOutput->childAt(projection.outputChannel); - target->setNull(row + 1, true); - } - } else if (!isRightJoin(joinType_)) { - for (auto& projection : rightProjections_) { - auto& target = output->childAt(projection.outputChannel); - target->setNull(row, true); - } } else { for (auto& projection : leftProjections_) { - auto& target = output->childAt(projection.outputChannel); + auto target = output->childAt(projection.outputChannel); target->setNull(row, true); } } }; auto onMatch = [&](auto row, bool firstMatch) { - const bool isNonSemiAntiJoin = - !isSemiFilterJoin(joinType_) && !isAntiJoin(joinType_); + const bool isFullLeftJoin = + isFullJoin(joinType_) && !joinTracker_->isRightJoinForFullOuter(row); + + const bool isNonSemiAntiFullJoin = !isSemiFilterJoin(joinType_) && + !isAntiJoin(joinType_) && !isFullJoin(joinType_); - if ((isSemiFilterJoin(joinType_) && firstMatch) || isNonSemiAntiJoin) { + if ((isSemiFilterJoin(joinType_) && firstMatch) || + isNonSemiAntiFullJoin || isFullLeftJoin) { rawIndices[numPassed++] = row; } }; @@ -1418,17 +1420,10 @@ RowVectorPtr MergeJoin::applyFilter(const RowVectorPtr& output) { if (numPassed == numRows) { // All rows passed. - if (fullOuterOutput) { - return fullOuterOutput; - } return output; } // Some, but not all rows passed. - if (fullOuterOutput) { - return wrap(numPassed, indices, fullOuterOutput); - } - return wrap(numPassed, indices, output); } diff --git a/velox/exec/MergeJoin.h b/velox/exec/MergeJoin.h index 5a44f92f8aff..0c1cb274a5db 100644 --- a/velox/exec/MergeJoin.h +++ b/velox/exec/MergeJoin.h @@ -246,7 +246,8 @@ class MergeJoin : public Operator { const RowVectorPtr& leftBatch, vector_size_t leftRow, const RowVectorPtr& rightBatch, - vector_size_t rightRow); + vector_size_t rightRow, + bool isRightJoinForFullOuter = false); // If the right side projected columns in the current output vector happen to // span more than one vector from the right side, they cannot be simply @@ -340,6 +341,11 @@ class MergeJoin : public Operator { : matchingRows_{numRows, false} { leftRowNumbers_ = AlignedBuffer::allocate(numRows, pool); rawLeftRowNumbers_ = leftRowNumbers_->asMutable(); + + rightJoinRows_ = AlignedBuffer::allocate(numRows, pool); + rawRightJoinRows_ = rightJoinRows_->asMutable(); + + auto tmp = AlignedBuffer::allocate(numRows, pool); } // Records a row of output that corresponds to a match between a left-side @@ -350,7 +356,8 @@ class MergeJoin : public Operator { void addMatch( const VectorPtr& vector, vector_size_t row, - vector_size_t outputIndex) { + vector_size_t outputIndex, + bool rightJoinForFullOuter = false) { matchingRows_.setValid(outputIndex, true); if (lastVector_ != vector || lastIndex_ != row) { @@ -361,6 +368,7 @@ class MergeJoin : public Operator { } rawLeftRowNumbers_[outputIndex] = lastLeftRowNumber_; + rawRightJoinRows_[outputIndex] = rightJoinForFullOuter; } // Returns a subset of "match" rows in [0, numRows) range that were @@ -403,7 +411,7 @@ class MergeJoin : public Operator { const auto rowNumber = rawLeftRowNumbers_[outputIndex]; if (currentLeftRowNumber_ != rowNumber) { if (currentRow_ != -1 && !currentRowPassed_) { - onMiss(currentRow_); + onMiss(currentRow_, rawRightJoinRows_[currentRow_]); } currentRow_ = outputIndex; currentLeftRowNumber_ = rowNumber; @@ -430,8 +438,8 @@ class MergeJoin : public Operator { // filter failed for all matches of that row. template void noMoreFilterResults(TOnMiss onMiss) { - if (!currentRowPassed_) { - onMiss(currentRow_); + if (!currentRowPassed_ && currentRow_ >= 0) { + onMiss(currentRow_, rawRightJoinRows_[currentRow_]); } currentRow_ = -1; @@ -440,6 +448,10 @@ class MergeJoin : public Operator { void reset(); + bool isRightJoinForFullOuter(vector_size_t row) { + return rawRightJoinRows_[row]; + } + private: // A subset of output rows where left side matched right side on the join // keys. Used in filter evaluation. @@ -459,6 +471,9 @@ class MergeJoin : public Operator { BufferPtr leftRowNumbers_; vector_size_t* rawLeftRowNumbers_; + BufferPtr rightJoinRows_; + bool* rawRightJoinRows_; + // Synthetic number assigned to the last added "match" row or zero if no row // has been added yet. vector_size_t lastLeftRowNumber_{0}; @@ -570,5 +585,10 @@ class MergeJoin : public Operator { bool leftHasDrained_{false}; bool rightHasDrained_{false}; + + bool leftJoinForFullFinished_{false}; + bool rightJoinForFullFinished_{false}; + std::optional leftForRightJoinMatch_; + std::optional rightForRightJoinMatch_; }; } // namespace facebook::velox::exec diff --git a/velox/exec/tests/MergeJoinTest.cpp b/velox/exec/tests/MergeJoinTest.cpp index 4019cc9b80b8..8c60aacba64a 100644 --- a/velox/exec/tests/MergeJoinTest.cpp +++ b/velox/exec/tests/MergeJoinTest.cpp @@ -1410,6 +1410,45 @@ TEST_F(MergeJoinTest, fullOuterJoin) { "SELECT * FROM t FULL OUTER JOIN u ON t.t0 = u.u0 AND t.t0 > 2"); } +TEST_F(MergeJoinTest, fullOuterJoinWithDuplicateMatch) { + // Each row on the left side has at most one match on the right side. + auto left = makeRowVector( + {"a", "b"}, + { + makeNullableFlatVector({1, 2, 2, 2, 3, 5, 6, std::nullopt}), + makeNullableFlatVector( + {2.0, 100.0, 1.0, 1.0, 3.0, 1.0, 6.0, std::nullopt}), + }); + + auto right = makeRowVector( + {"c", "d"}, + { + makeNullableFlatVector( + {0, 2, 2, 2, 2, 3, 4, 5, 7, std::nullopt}), + makeNullableFlatVector( + {0.0, 3.0, -1.0, -1.0, 3.0, 2.0, 1.0, 3.0, 7.0, std::nullopt}), + }); + + createDuckDbTable("t", {left}); + createDuckDbTable("u", {right}); + + auto planNodeIdGenerator = std::make_shared(); + + auto rightPlan = + PlanBuilder(planNodeIdGenerator) + .values({left}) + .mergeJoin( + {"a"}, + {"c"}, + PlanBuilder(planNodeIdGenerator).values({right}).planNode(), + "b < d", + {"a", "b", "c", "d"}, + core::JoinType::kFull) + .planNode(); + AssertQueryBuilder(rightPlan, duckDbQueryRunner_) + .assertResults("SELECT * from t FULL OUTER JOIN u ON a = c AND b < d"); +} + TEST_F(MergeJoinTest, fullOuterJoinNoFilter) { auto left = makeRowVector( {"t0", "t1", "t2", "t3"}, diff --git a/velox/exec/tests/TableScanTest.cpp b/velox/exec/tests/TableScanTest.cpp index 74515aa350dd..aa1bb5dcc69a 100644 --- a/velox/exec/tests/TableScanTest.cpp +++ b/velox/exec/tests/TableScanTest.cpp @@ -1190,6 +1190,92 @@ TEST_F(TableScanTest, missingColumnsInRepeatedColumns) { .assertResults(expected); } +TEST_F(TableScanTest, structMatchByName) { + const auto assertSelectUseColumnNames = + [this]( + const RowTypePtr& outputType, + const std::string& sql, + const std::string& filePath, + const std::string& remainingFilter = "") { + const auto plan = + PlanBuilder().tableScan(outputType, {}, remainingFilter).planNode(); + AssertQueryBuilder(plan, duckDbQueryRunner_) + .connectorSessionProperty( + kHiveConnectorId, + connector::hive::HiveConfig::kOrcUseColumnNamesSession, + "true") + .split(makeHiveConnectorSplit(filePath)) + .assertResults(sql); + }; + + std::vector values = {2}; + const auto id = makeFlatVector(values); + const auto name = makeRowVector( + {"first", "last"}, + { + makeFlatVector({"Janet"}), + makeFlatVector({"Jones"}), + }); + const auto address = makeFlatVector({"567 Maple Drive"}); + auto vector = makeRowVector({"id", "name", "address"}, {id, name, address}); + + auto file = TempFilePath::create(); + writeToFile(file->getPath(), {vector}); + + // Add one non-existing subfield 'middle' to the 'name' field and rename filed + // 'address'. + auto rowType = + ROW({"id", "name", "email"}, + {BIGINT(), + ROW({"first", "middle", "last"}, {VARCHAR(), VARCHAR(), VARCHAR()}), + VARCHAR()}); + assertSelectUseColumnNames( + rowType, "SELECT 2, ('Janet', null, 'Jones'), null", file->getPath()); + + // Filter pushdown on the non-existing field. + createDuckDbTable({vector}); + assertSelectUseColumnNames( + rowType, + "SELECT * from tmp where false", + file->getPath(), + "not(is_null(name.middle))"); + + // Deletion of one subfield from the 'name' field. + rowType = + ROW({"id", "name", "address"}, + {BIGINT(), ROW({"full"}, {VARCHAR()}), VARCHAR()}); + assertSelectUseColumnNames( + rowType, "SELECT 2, row(null), '567 Maple Drive'", file->getPath()); + + // Filter pushdown on the non-existing subfield. + assertSelectUseColumnNames( + rowType, + "SELECT * from tmp where false", + file->getPath(), + "not(is_null(name.full))"); + + // No subfield in the 'name' field. + rowType = ROW({"id", "name", "address"}, {BIGINT(), ROW({}, {}), VARCHAR()}); + const auto op = PlanBuilder() + .startTableScan() + .outputType(rowType) + .dataColumns(rowType) + .endTableScan() + .planNode(); + const auto split = makeHiveConnectorSplit(file->getPath()); + const auto result = + AssertQueryBuilder(op) + .connectorSessionProperty( + kHiveConnectorId, + connector::hive::HiveConfig::kOrcUseColumnNamesSession, + "true") + .split(split) + .copyResults(pool()); + const auto rows = result->as(); + const auto expected = makeRowVector(ROW({}, {}), 1); + facebook::velox::test::assertEqualVectors(expected, rows->childAt(1)); +} + // Tests queries that use Lazy vectors with multiple layers of wrapping. TEST_F(TableScanTest, constDictLazy) { vector_size_t size = 1'000; diff --git a/velox/functions/lib/aggregates/AverageAggregateBase.cpp b/velox/functions/lib/aggregates/AverageAggregateBase.cpp index 047d48cbf270..52e04dceccff 100644 --- a/velox/functions/lib/aggregates/AverageAggregateBase.cpp +++ b/velox/functions/lib/aggregates/AverageAggregateBase.cpp @@ -21,7 +21,8 @@ namespace facebook::velox::functions::aggregate { void checkAvgIntermediateType(const TypePtr& type) { VELOX_USER_CHECK( type->isRow() || type->isVarbinary(), - "Input type for final average must be row type or varbinary type."); + "Input type for final average must be row type or varbinary type, find {}", + type->toString()); if (type->kind() == TypeKind::VARBINARY) { return; } diff --git a/velox/functions/lib/aggregates/DecimalAggregate.h b/velox/functions/lib/aggregates/DecimalAggregate.h index 8e78a545d70c..58b534cda608 100644 --- a/velox/functions/lib/aggregates/DecimalAggregate.h +++ b/velox/functions/lib/aggregates/DecimalAggregate.h @@ -78,7 +78,7 @@ class DecimalAggregate : public exec::Aggregate { } int32_t accumulatorAlignmentSize() const override { - return static_cast(sizeof(int128_t)); + return alignof(LongDecimalWithOverflowState); } void addRawInput( @@ -275,7 +275,9 @@ class DecimalAggregate : public exec::Aggregate { } virtual TResultType computeFinalValue( - LongDecimalWithOverflowState* accumulator) = 0; + LongDecimalWithOverflowState* accumulator) { + return 0; + }; void extractValues(char** groups, int32_t numGroups, VectorPtr* result) override { @@ -327,11 +329,11 @@ class DecimalAggregate : public exec::Aggregate { } } - private: inline LongDecimalWithOverflowState* decimalAccumulator(char* group) { return exec::Aggregate::value(group); } + private: DecodedVector decodedRaw_; DecodedVector decodedPartial_; }; diff --git a/velox/functions/prestosql/InPredicate.cpp b/velox/functions/prestosql/InPredicate.cpp index 5ae0c6334373..6496f7572a1f 100644 --- a/velox/functions/prestosql/InPredicate.cpp +++ b/velox/functions/prestosql/InPredicate.cpp @@ -139,40 +139,6 @@ class VectorSetInPredicate : public exec::VectorFunction { const VectorPtr originalValues_; }; -// Read 'size' values from 'valuesVector' starting at 'offset', de-duplicate -// remove nulls and sort. Return a list of unique non-null values sorted in -// ascending order and a boolean indicating whether there were any null values. -template -std::pair, bool> toValues( - const VectorPtr& valuesVector, - vector_size_t offset, - vector_size_t size) { - auto simpleValues = valuesVector->as>(); - - bool nullAllowed = false; - std::vector values; - values.reserve(size); - - for (auto i = offset; i < offset + size; i++) { - if (simpleValues->isNullAt(i)) { - nullAllowed = true; - } else { - if constexpr (std::is_same_v) { - values.emplace_back(simpleValues->valueAt(i).toMillis()); - } else { - values.emplace_back(simpleValues->valueAt(i)); - } - } - } - - // In-place sort, remove duplicates, and later std::move to save memory - std::sort(values.begin(), values.end()); - auto last = std::unique(values.begin(), values.end()); - values.resize(std::distance(values.begin(), last)); - - return {std::move(values), nullAllowed}; -} - // Creates a filter for constant values. A null filter means either // no values or only null values. The boolean is true if the list is // non-empty and consists of nulls only. @@ -181,7 +147,8 @@ std::pair, bool> createBigintValuesFilter( const VectorPtr& valuesVector, vector_size_t offset, vector_size_t size) { - auto valuesPair = toValues(valuesVector, offset, size); + auto valuesPair = + common::deDuplicateValues(valuesVector, offset, size); const auto& values = valuesPair.first; bool nullAllowed = valuesPair.second; @@ -210,7 +177,7 @@ createFloatingPointValuesFilter( const VectorPtr& valuesVector, vector_size_t offset, vector_size_t size) { - auto valuesPair = toValues(valuesVector, offset, size); + auto valuesPair = common::deDuplicateValues(valuesVector, offset, size); auto& values = valuesPair.first; bool nullAllowed = valuesPair.second; @@ -252,7 +219,8 @@ std::pair, bool> createHugeintValuesFilter( const VectorPtr& valuesVector, vector_size_t offset, vector_size_t size) { - auto valuesPair = toValues(valuesVector, offset, size); + auto valuesPair = + common::deDuplicateValues(valuesVector, offset, size); const auto& values = valuesPair.first; bool nullAllowed = valuesPair.second; @@ -278,8 +246,8 @@ std::pair, bool> createBytesValuesFilter( const VectorPtr& valuesVector, vector_size_t offset, vector_size_t size) { - auto valuesPair = - toValues(valuesVector, offset, size); + auto valuesPair = common::deDuplicateValues( + valuesVector, offset, size); const auto& values = valuesPair.first; bool nullAllowed = valuesPair.second; diff --git a/velox/functions/sparksql/aggregates/AverageAggregate.cpp b/velox/functions/sparksql/aggregates/AverageAggregate.cpp index 623657df3a79..1c81de6e6b59 100644 --- a/velox/functions/sparksql/aggregates/AverageAggregate.cpp +++ b/velox/functions/sparksql/aggregates/AverageAggregate.cpp @@ -16,6 +16,7 @@ #include "velox/functions/sparksql/aggregates/AverageAggregate.h" #include "velox/functions/lib/aggregates/AverageAggregateBase.h" +#include "velox/functions/sparksql/DecimalUtil.h" using namespace facebook::velox::functions::aggregate; @@ -74,6 +75,308 @@ class AverageAggregate } }; +template +class DecimalAverageAggregate : public DecimalAggregate { + public: + explicit DecimalAverageAggregate(TypePtr resultType, TypePtr sumType) + : DecimalAggregate(resultType), sumType_(sumType) {} + + void addIntermediateResults( + char** groups, + const SelectivityVector& rows, + const std::vector& args, + bool /* mayPushdown */) override { + decodedPartial_.decode(*args[0], rows); + auto baseRowVector = dynamic_cast(decodedPartial_.base()); + auto sumVector = baseRowVector->childAt(0)->as>(); + auto countVector = baseRowVector->childAt(1)->as>(); + VELOX_USER_CHECK_NOT_NULL(sumVector); + + if (decodedPartial_.isConstantMapping()) { + if (!decodedPartial_.isNullAt(0)) { + auto decodedIndex = decodedPartial_.index(0); + auto count = countVector->valueAt(decodedIndex); + if (sumVector->isNullAt(decodedIndex) && + !countVector->isNullAt(decodedIndex) && count > 0) { + // Find overflow, set all groups to null. + rows.applyToSelected( + [&](vector_size_t i) { this->setNull(groups[i]); }); + } else { + auto sum = sumVector->valueAt(decodedIndex); + rows.applyToSelected([&](vector_size_t i) { + this->clearNull(groups[i]); + auto accumulator = this->decimalAccumulator(groups[i]); + mergeSumCount(accumulator, sum, count); + }); + } + } + } else if (decodedPartial_.mayHaveNulls()) { + rows.applyToSelected([&](vector_size_t i) { + if (decodedPartial_.isNullAt(i)) { + return; + } + auto decodedIndex = decodedPartial_.index(i); + auto count = countVector->valueAt(decodedIndex); + if (sumVector->isNullAt(decodedIndex) && + !countVector->isNullAt(decodedIndex) && count > 0) { + this->setNull(groups[i]); + } else { + this->clearNull(groups[i]); + auto sum = sumVector->valueAt(decodedIndex); + auto accumulator = this->decimalAccumulator(groups[i]); + mergeSumCount(accumulator, sum, count); + } + }); + } else { + rows.applyToSelected([&](vector_size_t i) { + auto decodedIndex = decodedPartial_.index(i); + auto count = countVector->valueAt(decodedIndex); + if (sumVector->isNullAt(decodedIndex) && + !countVector->isNullAt(decodedIndex) && count > 0) { + this->setNull(groups[i]); + } else { + this->clearNull(groups[i]); + auto sum = sumVector->valueAt(decodedIndex); + auto accumulator = this->decimalAccumulator(groups[i]); + mergeSumCount(accumulator, sum, count); + } + }); + } + } + + void addSingleGroupIntermediateResults( + char* group, + const SelectivityVector& rows, + const std::vector& args, + bool /* mayPushdown */) override { + decodedPartial_.decode(*args[0], rows); + auto baseRowVector = dynamic_cast(decodedPartial_.base()); + auto sumVector = baseRowVector->childAt(0)->as>(); + auto countVector = baseRowVector->childAt(1)->as>(); + + if (decodedPartial_.isConstantMapping()) { + if (!decodedPartial_.isNullAt(0)) { + auto decodedIndex = decodedPartial_.index(0); + if (isPartialSumOverflow(sumVector, countVector, decodedIndex)) { + // Find overflow, just set group to null and return. + this->setNull(group); + return; + } else { + if (rows.hasSelections()) { + this->clearNull(group); + } + auto sum = sumVector->valueAt(decodedIndex); + auto count = countVector->valueAt(decodedIndex); + rows.applyToSelected( + [&](vector_size_t i) { mergeAccumulators(group, sum, count); }); + } + } + } else if (decodedPartial_.mayHaveNulls()) { + rows.applyToSelected([&](vector_size_t i) { + if (!decodedPartial_.isNullAt(i)) { + this->clearNull(group); + auto decodedIndex = decodedPartial_.index(i); + if (isPartialSumOverflow(sumVector, countVector, decodedIndex)) { + // Find overflow, just set group to null. + this->setNull(group); + } else { + auto sum = sumVector->valueAt(decodedIndex); + auto count = countVector->valueAt(decodedIndex); + mergeAccumulators(group, sum, count); + } + } + }); + } else { + if (rows.hasSelections()) { + this->clearNull(group); + } + rows.applyToSelected([&](vector_size_t i) { + auto decodedIndex = decodedPartial_.index(i); + if (isPartialSumOverflow(sumVector, countVector, decodedIndex)) { + // Find overflow, just set group to null. + this->setNull(group); + } else { + auto sum = sumVector->valueAt(decodedIndex); + auto count = countVector->valueAt(decodedIndex); + mergeAccumulators(group, sum, count); + } + }); + } + } + + void extractAccumulators(char** groups, int32_t numGroups, VectorPtr* result) + override { + auto rowVector = (*result)->as(); + auto sumVector = rowVector->childAt(0)->asFlatVector(); + auto countVector = rowVector->childAt(1)->asFlatVector(); + VELOX_USER_CHECK_NOT_NULL(sumVector); + + rowVector->resize(numGroups); + sumVector->resize(numGroups); + countVector->resize(numGroups); + rowVector->clearAllNulls(); + + int64_t* rawCounts = countVector->mutableRawValues(); + int128_t* rawSums = sumVector->mutableRawValues(); + for (auto i = 0; i < numGroups; ++i) { + char* group = groups[i]; + auto* accumulator = this->decimalAccumulator(group); + std::optional adjustedSum = DecimalUtil::adjustSumForOverflow( + accumulator->sum, accumulator->overflow); + if (adjustedSum.has_value()) { + rawCounts[i] = accumulator->count; + rawSums[i] = adjustedSum.value(); + } else { + // Find overflow. + sumVector->setNull(i, true); + rawCounts[i] = accumulator->count; + } + } + } + + void extractValues(char** groups, int32_t numGroups, VectorPtr* result) + override { + auto vector = (*result)->as>(); + VELOX_CHECK(vector); + vector->resize(numGroups); + uint64_t* rawNulls = this->getRawNulls(vector); + + TResultType* rawValues = vector->mutableRawValues(); + for (int32_t i = 0; i < numGroups; ++i) { + char* group = groups[i]; + auto accumulator = this->decimalAccumulator(group); + if (accumulator->count == 0) { + // In Spark, if all inputs are null, count will be 0, + // and the result of final avg will be null. + vector->setNull(i, true); + } else { + this->clearNull(rawNulls, i); + std::optional avg = computeAvg(accumulator); + if (avg.has_value()) { + rawValues[i] = avg.value(); + } else { + // Find overflow. + vector->setNull(i, true); + } + } + } + } + + std::optional computeAvg( + LongDecimalWithOverflowState* accumulator) { + std::optional validSum = DecimalUtil::adjustSumForOverflow( + accumulator->sum, accumulator->overflow); + if (!validSum.has_value()) { + return std::nullopt; + } + + auto [resultPrecision, resultScale] = + getDecimalPrecisionScale(*this->resultType().get()); + // Spark use DECIMAL(20,0) to represent long value. + const uint8_t countPrecision = 20, countScale = 0; + auto [sumPrecision, sumScale] = + getDecimalPrecisionScale(*this->sumType_.get()); + auto [avgPrecision, avgScale] = computeResultPrecisionScale( + sumPrecision, sumScale, countPrecision, countScale); + avgScale = std::max(avgScale, resultScale); + auto sumRescale = computeRescaleFactor(sumScale, countScale, avgScale); + auto countDecimal = accumulator->count; + int128_t avg = 0; + + bool overflow = false; + functions::sparksql::DecimalUtil:: + divideWithRoundUp( + avg, validSum.value(), countDecimal, sumRescale, overflow); + if (overflow) { + return std::nullopt; + } + TResultType rescaledValue; + const auto status = DecimalUtil::rescaleWithRoundUp( + avg, + avgPrecision, + avgScale, + resultPrecision, + resultScale, + rescaledValue); + return status.ok() ? std::optional(rescaledValue) + : std::nullopt; + } + + private: + template + inline void mergeSumCount( + LongDecimalWithOverflowState* accumulator, + UnscaledType sum, + int64_t count) { + accumulator->count += count; + accumulator->overflow += + DecimalUtil::addWithOverflow(accumulator->sum, sum, accumulator->sum); + } + + template + void mergeAccumulators( + char* group, + const UnscaledType& otherSum, + const int64_t& otherCount) { + if constexpr (tableHasNulls) { + exec::Aggregate::clearNull(group); + } + auto accumulator = this->decimalAccumulator(group); + mergeSumCount(accumulator, otherSum, otherCount); + } + + inline static bool isPartialSumOverflow( + SimpleVector* sumVector, + SimpleVector* countVector, + int32_t index) { + return sumVector->isNullAt(index) && !countVector->isNullAt(index) && + countVector->valueAt(index) > 0; + } + + inline static uint8_t + computeRescaleFactor(uint8_t fromScale, uint8_t toScale, uint8_t rScale) { + return rScale - fromScale + toScale; + } + + inline static std::pair computeResultPrecisionScale( + const uint8_t aPrecision, + const uint8_t aScale, + const uint8_t bPrecision, + const uint8_t bScale) { + uint8_t intDig = aPrecision - aScale + bScale; + uint8_t scale = std::max(6, aScale + bPrecision + 1); + uint8_t precision = intDig + scale; + return functions::sparksql::DecimalUtil::adjustPrecisionScale( + precision, scale); + } + + inline static std::pair adjustPrecisionScale( + const uint8_t precision, + const uint8_t scale) { + VELOX_CHECK(precision >= scale); + if (precision <= 38) { + return {precision, scale}; + } else { + uint8_t intDigits = precision - scale; + uint8_t minScaleValue = std::min(scale, (uint8_t)6); + uint8_t adjustedScale = + std::max((uint8_t)(38 - intDigits), minScaleValue); + return {38, adjustedScale}; + } + } + + DecodedVector decodedRaw_; + DecodedVector decodedPartial_; + TypePtr sumType_; +}; + +TypePtr getDecimalSumType( + const uint8_t rawInputPrecision, + const uint8_t rawInputScale) { + // This computational logic is derived from the definition of Spark SQL. + return DECIMAL(std::min(38, rawInputPrecision + 10), rawInputScale); +} + } // namespace /// Count is BIGINT() while sum and the final aggregates type depends on @@ -99,13 +402,25 @@ exec::AggregateRegistrationResult registerAverage( .build()); } - signatures.push_back(exec::AggregateFunctionSignatureBuilder() - .integerVariable("a_precision") - .integerVariable("a_scale") - .argumentType("DECIMAL(a_precision, a_scale)") - .intermediateType("varbinary") - .returnType("DECIMAL(a_precision, a_scale)") - .build()); + signatures.push_back( + exec::AggregateFunctionSignatureBuilder() + .integerVariable("a_precision") + .integerVariable("a_scale") + .integerVariable("r_precision", "min(38, a_precision + 4)") + .integerVariable("r_scale", "min(38, a_scale + 4)") + .argumentType("DECIMAL(a_precision, a_scale)") + .intermediateType("ROW(DECIMAL(38, a_scale), BIGINT)") + .returnType("DECIMAL(r_precision, r_scale)") + .build()); + + signatures.push_back( + exec::AggregateFunctionSignatureBuilder() + .integerVariable("a_precision") + .integerVariable("a_scale") + .argumentType("DECIMAL(a_precision, a_scale)") + .intermediateType("ROW(DECIMAL(a_precision, a_scale), BIGINT)") + .returnType("DECIMAL(a_precision, a_scale)") + .build()); return exec::registerAggregateFunction( name, @@ -118,7 +433,7 @@ exec::AggregateRegistrationResult registerAverage( -> std::unique_ptr { VELOX_CHECK_LE( argTypes.size(), 1, "{} takes at most one argument", name); - auto inputType = argTypes[0]; + const auto& inputType = argTypes[0]; if (exec::isRawInput(step)) { switch (inputType->kind()) { case TypeKind::SMALLINT: @@ -129,16 +444,39 @@ exec::AggregateRegistrationResult registerAverage( AverageAggregate>(resultType); case TypeKind::BIGINT: { if (inputType->isShortDecimal()) { - return std::make_unique>( - resultType); + auto inputPrecision = inputType->asShortDecimal().precision(); + auto inputScale = inputType->asShortDecimal().scale(); + auto sumType = + DECIMAL(std::min(38, inputPrecision + 10), inputScale); + if (exec::isPartialOutput(step)) { + return std::make_unique< + DecimalAverageAggregate>( + resultType, sumType); + } else { + if (resultType->isShortDecimal()) { + return std::make_unique< + DecimalAverageAggregate>( + resultType, sumType); + } else if (resultType->isLongDecimal()) { + return std::make_unique< + DecimalAverageAggregate>( + resultType, sumType); + } else { + VELOX_FAIL("Result type must be decimal"); + } + } } return std::make_unique< AverageAggregate>(resultType); } case TypeKind::HUGEINT: { if (inputType->isLongDecimal()) { - return std::make_unique>( - resultType); + auto inputPrecision = inputType->asLongDecimal().precision(); + auto inputScale = inputType->asLongDecimal().scale(); + auto sumType = getDecimalSumType(inputPrecision, inputScale); + return std::make_unique< + DecimalAverageAggregate>( + resultType, sumType); } VELOX_NYI(); } @@ -162,26 +500,37 @@ exec::AggregateRegistrationResult registerAverage( resultType); case TypeKind::DOUBLE: case TypeKind::ROW: + if (inputType->childAt(0)->isLongDecimal()) { + return std::make_unique< + DecimalAverageAggregate>( + resultType, inputType->childAt(0)); + } return std::make_unique< AverageAggregate>(resultType); case TypeKind::BIGINT: - return std::make_unique>( - resultType); + VELOX_USER_CHECK(resultType->isShortDecimal()); + return std::make_unique< + DecimalAverageAggregate>( + resultType, inputType->childAt(0)); case TypeKind::HUGEINT: - return std::make_unique>( - resultType); + VELOX_USER_CHECK(resultType->isLongDecimal()); + return std::make_unique< + DecimalAverageAggregate>( + resultType, inputType->childAt(0)); case TypeKind::VARBINARY: if (inputType->isLongDecimal()) { - return std::make_unique>( - resultType); + return std::make_unique< + DecimalAverageAggregate>( + resultType, inputType->childAt(0)); } else if ( inputType->isShortDecimal() || inputType->kind() == TypeKind::VARBINARY) { // If the input and out type are VARBINARY, then the // LongDecimalWithOverflowState is used and the template type // does not matter. - return std::make_unique>( - resultType); + return std::make_unique< + DecimalAverageAggregate>( + resultType, inputType->childAt(0)); } [[fallthrough]]; default: diff --git a/velox/functions/sparksql/aggregates/tests/AverageAggregationTest.cpp b/velox/functions/sparksql/aggregates/tests/AverageAggregationTest.cpp index 93057ef155a5..6dac5631ab92 100644 --- a/velox/functions/sparksql/aggregates/tests/AverageAggregationTest.cpp +++ b/velox/functions/sparksql/aggregates/tests/AverageAggregationTest.cpp @@ -110,5 +110,152 @@ TEST_F(AverageAggregationTest, avgAllNulls) { assertQuery(plan, expected); } +TEST_F(AverageAggregationTest, avgDecimal) { + int64_t kRescale = DecimalUtil::kPowersOfTen[4]; + // Short decimal aggregation + auto shortDecimal = makeNullableFlatVector( + {1'000, 2'000, 3'000, 4'000, 5'000, std::nullopt}, DECIMAL(10, 1)); + testAggregations( + {makeRowVector({shortDecimal})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({makeNullableFlatVector( + {3'000 * kRescale}, DECIMAL(14, 5))})}); + + // Long decimal aggregation + testAggregations( + {makeRowVector({makeNullableFlatVector( + {HugeInt::build(10, 100), + HugeInt::build(10, 200), + HugeInt::build(10, 300), + HugeInt::build(10, 400), + HugeInt::build(10, 500), + std::nullopt}, + DECIMAL(23, 4))})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({makeFlatVector( + std::vector{HugeInt::build(10, 300) * kRescale}, + DECIMAL(27, 8))})}); + + // The total sum overflows the max int128_t limit. + std::vector rawVector; + for (int i = 0; i < 10; ++i) { + rawVector.push_back(DecimalUtil::kLongDecimalMax); + } + testAggregations( + {makeRowVector({makeFlatVector(rawVector, DECIMAL(38, 0))})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({makeNullableFlatVector( + std::vector>{std::nullopt}, + DECIMAL(38, 4))})}); + + // The total sum underflows the min int128_t limit. + rawVector.clear(); + auto underFlowTestResult = makeNullableFlatVector( + std::vector>{std::nullopt}, DECIMAL(38, 4)); + for (int i = 0; i < 10; ++i) { + rawVector.push_back(DecimalUtil::kLongDecimalMin); + } + testAggregations( + {makeRowVector({makeFlatVector(rawVector, DECIMAL(38, 0))})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({underFlowTestResult})}); + + // Test constant vector. + testAggregations( + {makeRowVector({makeConstant(100, 10, DECIMAL(10, 2))})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({makeFlatVector( + std::vector{100 * kRescale}, DECIMAL(14, 6))})}); + + auto newSize = shortDecimal->size() * 2; + auto indices = makeIndices(newSize, [&](int row) { return row / 2; }); + auto dictVector = + VectorTestBase::wrapInDictionary(indices, newSize, shortDecimal); + + testAggregations( + {makeRowVector({dictVector})}, + {}, + {"spark_avg(c0)"}, + {}, + {makeRowVector({makeFlatVector( + std::vector{3'000 * kRescale}, DECIMAL(14, 5))})}); + + // Decimal average aggregation with multiple groups. + auto inputRows = { + makeRowVector( + {makeNullableFlatVector({1, 1}), + makeFlatVector({37220, 53450}, DECIMAL(15, 2))}), + makeRowVector( + {makeNullableFlatVector({2, 2}), + makeFlatVector({10410, 9250}, DECIMAL(15, 2))}), + makeRowVector( + {makeNullableFlatVector({3, 3}), + makeFlatVector({-12783, 0}, DECIMAL(15, 2))}), + makeRowVector( + {makeNullableFlatVector({1, 2}), + makeFlatVector({23178, 41093}, DECIMAL(15, 2))}), + makeRowVector( + {makeNullableFlatVector({2, 3}), + makeFlatVector({-10023, 5290}, DECIMAL(15, 2))}), + }; + + auto expectedResult = { + makeRowVector( + {makeNullableFlatVector({1}), + makeFlatVector(std::vector{379493333}, DECIMAL(19, 6))}), + makeRowVector( + {makeNullableFlatVector({2}), + makeFlatVector(std::vector{126825000}, DECIMAL(19, 6))}), + makeRowVector( + {makeNullableFlatVector({3}), + makeFlatVector(std::vector{-24976667}, DECIMAL(19, 6))})}; + + testAggregations(inputRows, {"c0"}, {"spark_avg(c1)"}, expectedResult); + + auto valueA = HugeInt::parse("11999999998800000000"); + auto valueB = HugeInt::parse("12000000000000000000"); + auto longDecimalInputRows = {makeRowVector( + {makeNullableFlatVector({1, 1, 1, 1, 1, 1, 1}), + makeFlatVector( + {valueA, valueA, valueA, valueB, valueB, valueB, valueB}, + DECIMAL(38, 18))})}; + + auto longDecimalExpectedResult = {makeRowVector( + {makeNullableFlatVector({1}), + makeFlatVector( + std::vector{HugeInt::parse("119999999994857142857143")}, + DECIMAL(38, 22))})}; + + testAggregations( + longDecimalInputRows, + {"c0"}, + {"spark_avg(c1)"}, + longDecimalExpectedResult); +} + +TEST_F(AverageAggregationTest, avgDecimalWithMultipleRowVectors) { + int64_t kRescale = DecimalUtil::kPowersOfTen[4]; + auto inputRows = { + makeRowVector({makeFlatVector({100, 200}, DECIMAL(15, 2))}), + makeRowVector({makeFlatVector({300, 400}, DECIMAL(15, 2))}), + makeRowVector({makeFlatVector({500, 600}, DECIMAL(15, 2))}), + }; + + auto expectedResult = {makeRowVector( + {makeFlatVector(std::vector{350 * kRescale}, DECIMAL(19, 6))})}; + + testAggregations(inputRows, {}, {"spark_avg(c0)"}, expectedResult); +} + } // namespace } // namespace facebook::velox::functions::aggregate::sparksql::test diff --git a/velox/type/Filter.cpp b/velox/type/Filter.cpp index dfa059a94b1f..477a29c690aa 100644 --- a/velox/type/Filter.cpp +++ b/velox/type/Filter.cpp @@ -1213,7 +1213,7 @@ std::unique_ptr createBigintValuesFilter( std::unique_ptr createBigintValues( const std::vector& values, bool nullAllowed) { - return createBigintValuesFilter(values, nullAllowed, false); + return common::createBigintValuesFilter(values, nullAllowed, false); } std::unique_ptr createHugeintValues( @@ -1229,7 +1229,7 @@ std::unique_ptr createHugeintValues( std::unique_ptr createNegatedBigintValues( const std::vector& values, bool nullAllowed) { - return createBigintValuesFilter(values, nullAllowed, true); + return common::createBigintValuesFilter(values, nullAllowed, true); } BigintMultiRange::BigintMultiRange( diff --git a/velox/type/Filter.h b/velox/type/Filter.h index 28cfd58b9230..317fa0a533c0 100644 --- a/velox/type/Filter.h +++ b/velox/type/Filter.h @@ -31,6 +31,7 @@ #include "velox/type/StringView.h" #include "velox/type/Subfield.h" #include "velox/type/Type.h" +#include "velox/vector/BaseVector.h" namespace facebook::velox::common { @@ -2259,6 +2260,40 @@ static inline bool applyFilter(TFilter& filter, StringView value) { return filter.testBytes(value.data(), value.size()); } +// Read 'size' values from 'valuesVector' starting at 'offset', de-duplicate +// remove nulls and sort. Return a list of unique non-null values sorted in +// ascending order and a boolean indicating whether there were any null values. +template +std::pair, bool> deDuplicateValues( + const VectorPtr& valuesVector, + vector_size_t offset, + vector_size_t size) { + auto simpleValues = valuesVector->as>(); + + bool hasNull = false; + std::vector values; + values.reserve(size); + + for (auto i = offset; i < offset + size; i++) { + if (simpleValues->isNullAt(i)) { + hasNull = true; + } else { + if constexpr (std::is_same_v) { + values.emplace_back(simpleValues->valueAt(i).toMillis()); + } else { + values.emplace_back(simpleValues->valueAt(i)); + } + } + } + + // In-place sort, remove duplicates, and later std::move to save memory. + std::sort(values.begin(), values.end()); + auto last = std::unique(values.begin(), values.end()); + values.resize(std::distance(values.begin(), last)); + + return {std::move(values), hasNull}; +} + // Creates a hash or bitmap based IN filter depending on value distribution. std::unique_ptr createBigintValues( const std::vector& values, diff --git a/velox/type/Timestamp.cpp b/velox/type/Timestamp.cpp index e159596cab99..29565ce1f6dd 100644 --- a/velox/type/Timestamp.cpp +++ b/velox/type/Timestamp.cpp @@ -268,17 +268,22 @@ StringView Timestamp::tmToStringView( *writePosition++ = ':'; writePosition += appendDigits(tmValue.tm_min, 2, writePosition); - // Second. - *writePosition++ = ':'; - writePosition += appendDigits(tmValue.tm_sec, 2, writePosition); - if (options.precision == TimestampToStringOptions::Precision::kMilliseconds) { nanos /= 1'000'000; } else if ( options.precision == TimestampToStringOptions::Precision::kMicroseconds) { nanos /= 1'000; } - if (options.skipTrailingZeros && nanos == 0) { + + // Second. + const bool shouldSkipSeconds = + options.skipTrailingZeroSeconds && tmValue.tm_sec == 0 && nanos == 0; + if (!shouldSkipSeconds) { + *writePosition++ = ':'; + writePosition += appendDigits(tmValue.tm_sec, 2, writePosition); + } + + if ((options.skipTrailingZeros && nanos == 0) || shouldSkipSeconds) { return StringView(startPosition, writePosition - startPosition); } diff --git a/velox/type/Timestamp.h b/velox/type/Timestamp.h index ee27d0b0abc8..7cce24f6201d 100644 --- a/velox/type/Timestamp.h +++ b/velox/type/Timestamp.h @@ -48,6 +48,12 @@ struct TimestampToStringOptions { /// '2000-01-01 12:21:56.129000' becomes '2000-01-01 12:21:56.129'. bool skipTrailingZeros = false; + /// Whether to skip trailing zeros of second part. E.g. when true, + /// '2000-01-01 12:21:00' becomes '2000-01-01 12:21'. + /// '2000-01-01 12:21:00.000' becomes '2000-01-01 12:21'. + /// '2000-01-01 12:21:00.123' will not be impacted by this option. + bool skipTrailingZeroSeconds = false; + /// Whether padding zeros are added when the digits of year is less than 4. /// E.g. when true, '1-01-01 05:17:32.000' becomes '0001-01-01 05:17:32.000', /// '-03-24 13:20:00.000' becomes '0000-03-24 13:20:00.000', and '-1-11-29 diff --git a/velox/type/tests/TimestampTest.cpp b/velox/type/tests/TimestampTest.cpp index 44f1ffc9e427..dc55ed73c052 100644 --- a/velox/type/tests/TimestampTest.cpp +++ b/velox/type/tests/TimestampTest.cpp @@ -576,5 +576,44 @@ TEST(TimestampTest, skipTrailingZeros) { "0384-01-01 08:00:00.7266"); } +TEST(TimestampTest, skipTrailingZeroSeconds) { + TimestampToStringOptions options = { + .precision = TimestampToStringOptions::Precision::kMicroseconds, + .skipTrailingZeros = true, + .skipTrailingZeroSeconds = true, + .zeroPaddingYear = true, + .dateTimeSeparator = ' ', + }; + + ASSERT_EQ( + timestampToString(Timestamp(-946684800, 0), options), "1940-01-02 00:00"); + ASSERT_EQ(timestampToString(Timestamp(0, 0), options), "1970-01-01 00:00"); + ASSERT_EQ(timestampToString(Timestamp(0, 365), options), "1970-01-01 00:00"); + ASSERT_EQ( + timestampToString(Timestamp(0, 65873), options), + "1970-01-01 00:00:00.000065"); + ASSERT_EQ( + timestampToString(Timestamp(94668480000, 0), options), + "4969-12-04 00:00"); + ASSERT_EQ( + timestampToString(Timestamp(946729316, 129999999), options), + "2000-01-01 12:21:56.129999"); + ASSERT_EQ( + timestampToString(Timestamp(946729316, 129900000), options), + "2000-01-01 12:21:56.1299"); + ASSERT_EQ( + timestampToString(Timestamp(946729316, 129000000), options), + "2000-01-01 12:21:56.129"); + ASSERT_EQ( + timestampToString(Timestamp(946729316, 0), options), + "2000-01-01 12:21:56"); + ASSERT_EQ( + timestampToString(Timestamp(946729316, 129001000), options), + "2000-01-01 12:21:56.129001"); + ASSERT_EQ( + timestampToString(Timestamp(-50049331200, 726600000), options), + "0384-01-01 08:00:00.7266"); +} + } // namespace } // namespace facebook::velox diff --git a/website/.gitignore b/website/.gitignore deleted file mode 100644 index b2d6de30624f..000000000000 --- a/website/.gitignore +++ /dev/null @@ -1,20 +0,0 @@ -# Dependencies -/node_modules - -# Production -/build - -# Generated files -.docusaurus -.cache-loader - -# Misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/website/README.md b/website/README.md deleted file mode 100644 index 09465bc0eb9e..000000000000 --- a/website/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Website - -This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. - -### Installation - -``` -$ yarn -``` - -### Local Development - -``` -$ yarn start -``` - -This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. - -### Build - -``` -$ yarn build -``` - -This command generates static content into the `build` directory and can be served using any static contents hosting service. - -### Deployment - -Velox's website is automatically deployed using -[Netlify](https://www.netlify.com/). Whenever a pull request changing one of -the files under *velox/website* is submitted, a live preview link is generated -by Netlify. The link is posted in the pull request as a comment by the Netlify -bot. When the pull request is merged, the changes are automatically deployed to -the website by Netlify. diff --git a/website/babel.config.js b/website/babel.config.js deleted file mode 100644 index e00595dae7d6..000000000000 --- a/website/babel.config.js +++ /dev/null @@ -1,3 +0,0 @@ -module.exports = { - presets: [require.resolve('@docusaurus/core/lib/babel/preset')], -}; diff --git a/website/blog/2022-07-01-june-2022-updates.mdx b/website/blog/2022-07-01-june-2022-updates.mdx deleted file mode 100644 index a3f528534ffc..000000000000 --- a/website/blog/2022-07-01-june-2022-updates.mdx +++ /dev/null @@ -1,62 +0,0 @@ ---- -slug: june-2022-updates -title: June 2022 Update -authors: [majetideepak] -tags: [monthly-updates] ---- - - -## Documentation - -* Add documentation for :ref:`complex types writers`. - -## Core Library - -* Add support for INTERVAL DAY TO SECOND Presto type. -* Allow cast between DATE and TIMESTAMP types. -* Allow cast from JSON to scalar, ARRAY, and MAP types. -* Add :ref:`GroupIdNode` and GroupId operator to support aggregations over grouping sets. -* Add support for function signatures with DECIMAL input and return types using flex and bison to evaluate formulas for calculating the return precision and scale based on input precisions and scales. -* Add support for conversion of DuckDB DECIMALS to Velox DECIMALS. -* Add support for running tasks on the caller's thread. -* Fix expression evaluation to disable sub-expression optimization for non-deterministic functions. - -## Presto Functions - -* Add :func:`degrees`, :func:`e`, and :func:`sha512` functions. -* Add aggregate function :func:`map_union`. -* Optimize :func:`zip` for the case when all arrays are flat and the same size. -* Extend :func:`plus`, :func:`minus` functions to support DATE, INTERVAL DAY TO SECOND argument types. - -## Hive Connector - -* Add support for reading files from HDFS. -* Add limited ORC support. -* Optimize `NOT IN ()` filters pushed down into DWRF reader. - -## TPC-H Connector - -* Add `totalParts` and `partNumber` to TpchSplit. - -## Performance and Correctness - -* Add q3 to TPC-H benchmark. -* Add utility to benchmark dataset generation speed to TPC-H connector. -* Optimize constant aggregation mask. -* Optimize VectorWriter for a subset of simple functions that return strings. -* Optimize DictionaryVector wrapping LazyVector to load only necessary rows. - -## Debugging Experience - -* Separate the user exception stack from the runtime exception stack trace collection control. - -## Credits - -Adam Simpkins, Aditi Pandit, Amit Dutta, Behnam Robatmili, Chad Austin, -Connor Devlin, Daniel Ng, Dark Knight, Deepak Majeti, Denis Yaroshevskiy, -Huameng Jiang, Jake Jung, Jialiang Tan, Jie1 Zhang, Jimmy Lu, Karteek Murthy, -Katie Mancini, Ke Jia, Kevin Wilfong, Krishna Pai, Laith Sakka, Masha Basmanova, -Michael Shang, Mindaugas Rukas, Orri Erling, Patrick Stuedi, Paul Saab, -Pedro Eugenio Rocha Pedreira, Pramod Sathyanarayana, Sahana CB, Sergey Pershin, -Wei He, Xavier Deguillard, Xiaoxuan Meng, Yating Zhou, Yoav Helfman, Zeyi (Rice) Fan, -Zhenyuan Zhao, artem.malyshev, benitakbritto, frankobe, usurai, yingsu00, zhaozhenhui diff --git a/website/blog/2022-07-31-public-announcement.mdx b/website/blog/2022-07-31-public-announcement.mdx deleted file mode 100644 index 340c50bac89c..000000000000 --- a/website/blog/2022-07-31-public-announcement.mdx +++ /dev/null @@ -1,23 +0,0 @@ ---- -slug: public-announcement -title: 'Velox: The Unified Execution Engine Now Available' -tags: [public-announcement] ---- -![img alt](/img/velox_blog_pic.png) - -We are extremely excited to introduce Velox as an [open-source project](https://engineering.fb.com/2022/08/31/open-source/velox/), with a mission to define common standards for modular data processing systems. Velox provides reusable, extensible, high-performance, and dialect-agnostic data processing components for building execution engines, and enhancing data management systems. We envision Velox to be the defacto execution engine for [Arrow-compatible](https://www.globenewswire.com/en/news-release/2022/06/23/2468271/0/en/Voltron-Data-Announces-Commitment-to-Improve-Interoperability-Between-Apache-Arrow-and-the-Velox-Open-Source-Project-Created-by-Meta.html) data format powering ML and Analytical workloads. - -The Velox team has been partnering with companies such as [Ahana, Intel, and Voltron Data](https://www.techtarget.com/searchdatamanagement/news/252524446/Meta-and-partners-build-Velox-open-source-execution-engine?amp=1) as well as various academic institutions to accelerate innovation and development in the data management industry. - -Looking at the future, we believe Velox’s unified and modular nature has the potential to disrupt the data management industry. It will allow us to deepen our partnership with hardware vendors and proactively adapt our unified software stack as hardware advances. We believe that modularity and reusability are the future of database system development and hope a vibrant open source community will help us in this journey. - -Quick Introduction - - - -If you are excited to learn what’s under the hood, refer to the Velox research [paper](https://research.facebook.com/publications/velox-metas-unified-execution-engine/). - -If you are interested in contributing, visit our [Contributing](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) guide on Github. For technical discussions and exploring what’s happening within our community, refer to the [discussions](https://github.com/facebookincubator/velox/discussions) section. - -We will be publishing a series of technical blogs on various topics on our [website](https://velox-lib.io/blog) soon! - -For more updates and exciting news follow us on [Twitter](https://twitter.com/velox_lib). diff --git a/website/blog/2022-10-31-simple-1.mdx b/website/blog/2022-10-31-simple-1.mdx deleted file mode 100644 index 88109d8c36a0..000000000000 --- a/website/blog/2022-10-31-simple-1.mdx +++ /dev/null @@ -1,109 +0,0 @@ ---- -slug: simple-functions-1 -title: "Simple Functions: Introduction and Basic Optimizations" -authors: [lsakka] -tags: [tech-blog, simple-functions] ---- - -_This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface in Velox_. - - -## Introduction to Simple Functions - -Scalar functions are one of the most used extension points in Velox. Since Velox is a vectorized engine, by nature functions are "vector functions" that consume Velox vectors (batches of data) and produce vectors. Velox allows users to write functions as vector functions or as single-row operations "simple functions" that are converted to vector functions using template expansion through SimpleFunctionAdapter. - -
- -
- -Writing functions as vector functions directly gives the user `complete` control over the function implementations and optimizations, however it comes with some cost that can be summarized in three points: - -- **Complexity** : Requires an understanding of Velox vectorized data representation and encodings, which requires additional work for our customers, specially those without DB background. Moreover, Writing optimized vector functions requires even deeper understanding. -- **Repetition** : Involves repeated efforts and code; in each function, authors have to decode the input vectors, apply the same optimizations, and build the output vectors. For example, most arithmetic functions need benefits from a fast path when all the inputs are flat-encoded, authors need to implement that for every function that benefits from it. -- **Reliability** : More code means more bugs, especially in such a complex context. - -Writing functions through the simple interface mitigates the previously mentioned drawbacks, and significantly simplifies the function authoring process. For example, to add the function plus the user only needs to implement the PlusFunction struct shown in the graph above , which is then expanded using template expansion to a vector function. - -However, the simple function interface does not give the user full control over the authoring and has its own limitations, for example the function map_keys can be implemented in O(1) as a vector function by moving the keys vector; this is not possible to express as a simple function. - -Another limitation is that when using the simple interface, authors do not have access to the encodings of the input vectors, nor control over the encoding of the result vector. Hence, do not have the power to optimize the code for specific input encodings or optimize it by generating specific output encodings. The array_sort function for instance does not need to re-order the elements and copy them during sorting; instead it can generate a dictionary vector as an output, which is something not expressible as a simple function. - - -In the ideal world we would like to add most of the optimization that someone can do in a vector function to the simple functions adapter, so it would be enabled automatically. We have identified a number of optimizations that apply to all functions and implemented these generically in the SimpleFunctionAdapter. In this way, we can achieve the best of the two worlds and gain **Simplicity**, **Efficiency** and **Reliability** for most functions. - -In the past year, we have been working on several improvements to the simple function interface on both the `expressivity` and `performance` axes that we will discuss in this series of notes. - -In this blog post, we will talk about some of the general optimizations that we have in the adapter, the optimizations discussed in this post make the performance of most simple functions that operates on primitive types matches their counter optimized vector function implementations. In the next blog post, we will discuss complex types in simple functions. - -## General Optimizations - -#### Vector Reuse -If the output type matches one of the input types, and the input vector is to die after the function invocation, then it is possible to reuse it for the results instead of allocating a new vector. For example, in the expression plus(a, b), if a is stored in a flat vector that is not used after the invocation of the plus function, then that vevtor can be used to store the reults of the computation instead of allocating a new vevtor for the results. - -#### Bulk Null Setting -Nulls are represented in a bit vector, hence, writing each bit can be expensive specially for primitive operations (like plus and minus). One optimization is -to optimize for the `not null` case, and bulk setting the nulls to not null. After that during the computation, only if the results are null, the null bit is set to null. - -#### Null Setting Avoidance -The adapter can statically infer if a function never generates null; In the simple function interface if the `call` function return's type is `void`, it means the output is never null, and if it's `bool`, then the function returns true for not null and false for null). - -When the function does not generate nulls, then null setting is **completely avoided** during the computation (only the previous bulk setting is needed). The consequence of that is that the hot loop applying the function becomes simdizable triggering a huge boost in performance for primitive operations. - -Worth to note also that if the simple function happens to be inlined in the adapter, then even if its return type is not void, but it always returns `true` then the compiler will be able to infer that setting nulls is never executed and would remove the null setting code. - - -#### Encoding Based Fast Path -Vectors in Velox can have different encodings (flat, constant..etc). The generic way of reading a vector of arbitrary encoding is to use a decoded vector to guarantee correct data access. Even though decoded vectors provide a consistent API and make it easier to handle arbitrarily encoded input data, they translate into an overhead each time an input value is accessed (we need to check the encoding of the vector to know how to read the value for every row). - -When the function is a primitive operation like plus or minus, such overhead is expensive! To avoid that, encoding based fast paths can be added, the code snippet below illustrates the idea. -
- -
- -In the code above, the overhead of checking the encoding is switched outside the loop that applies the functions (the plus operation here). And the inner loops are simple operations that are potentially simdizable and free of encoding checks. -One issue with this optimization is that the core loop is replicated many times. In general, the numbers of times it will be replicated -is `n^m` where `n` is the number of args, and `m` is the number of encodings. - -To avoid code size blowing, we only apply this optimization when all input arguments are primitives and the number of input arguments is \<=3. -The figure below shows the effect of this optimization on the processing time of a query of primitive operations (the expression is a common pattern in ML use cases). - -
- -
- -To compromise for both (performance and code size) when the conditions for specializing for all encodings are not met, we have a pseudo specialization mode that does not blow up the code size, but still reduce the overhead of decoding to a single multiplication per argument. This mode is enabled when all the primitive arguments are either flat or constant. The code below illustrates the idea: - -
- -
- -When the input vector is constant we can read the value always from index 0 of the values buffer, and when it is flat we can read it from the index row; this can be achieved by assigning a factor to either 0 or 1 and reducing the decoding operation per row into a multiplication with that factor Note that such a multiplication does not prevent simd. The graph above shows that the psudeo specialization makes the program 1.6X fatser wi, while the complete specialization makes the program 2.5X faster. - -#### ASCII Fast Path -Functions with string inputs can be optimized when the inputs are known to be ascii. For example the length function for ascii strings is the size of the StringView O(1). But for non-ascii inputs the computation is a more complicated O(n) operation. -Users can define a function `callAscii()` that will be called when all the string input arguments are ascii. - -
- -
- -#### Zero-Copy Optimization -When an input string (or portion of it, reaches the output as is) it does not need to be deep copied. Instead only a StringView needs to be set. Substring is an example of a function that benefits from this. This can be done in the simple function interface in two simple steps. - -1. Using setNoCopy(); to set the output results without copying string vectors. -2. Inform the function to make the output vector share ownership of input string buffers, this can be by setting the field reuse_strings_from_arg. - -The graph below shows the effect of the previous two optimizations on the performance of the substring function. -
- -
Runtime of function substring with different optimizations.
-
- - -#### Constant Inputs Pre-processing - -Users can pre-process constant inputs of functions to avoid repeated computation by defining `initialize` function which is called once during query compilations and receives the constant inputs. For example, a regex function with constant pattern would only needs to compile the pattern expressions only once when its constant. - - - -For more information about how to write simple functions check the documentation and the examples. diff --git a/website/blog/2023-03-15-build-experience.mdx b/website/blog/2023-03-15-build-experience.mdx deleted file mode 100644 index d5ab05290cdd..000000000000 --- a/website/blog/2023-03-15-build-experience.mdx +++ /dev/null @@ -1,50 +0,0 @@ ---- -slug: velox-build-experience -title: "Improving the Velox Build Experience" -authors: [jwujciak, raulcd, kgpai] -tags: [tech-blog, packaging] ---- - - -When Velox was open sourced in August 2021, it was not nearly as easily usable and portable as it is today. In order for Velox to become the unified execution engine blurring the boundaries for data analytics and ML, we needed Velox to be easy to build and package on multiple platforms, and support a wide range of hardware architectures. If we are supporting all these platforms, we also need to ensure that Velox remains fast and regressions are caught early. - -To improve the Velox experience for users and community developers, Velox has partnered with Voltron Data to help make Velox more accessible and user-friendly. In this blog post, we will examine the challenges we faced, the improvements that have already been made, and the ones yet to come. - -## Enhancements & Improvements - -Velox was a product of the mono repo and required installation of dependencies on the system via a script. Any change in the state of the host system could cause a build failure and introduce version conflicts of dependencies. Fixing these challenges was a big focus to help the Velox Community and we worked in collaboration with the Voltron Data Team. We wanted to improve the overall Velox user experience by making Velox easy to consume across a wide range of platforms to accelerate its adoption. - -We choose hermetic builds as a solution to the aforementioned problems, as they provide a number of benefits. Hermetic builds[^1] improve reproducibility by providing isolation from the state of the host machine and produce the same result for any given commit in the Velox repository. This requires precise dependency management. - -[^1]: [Hermeticity](https://bazel.build/basics/hermeticity) - why hermetic builds are recommended - -The first major step in moving towards hermetic builds was the integration of a new [dependency management system](https://github.com/facebookincubator/velox/tree/main/CMake/resolve_dependency_modules) that is able to download, configure and build the necessary dependencies within the Velox build process. This new system also gives users the option to use already installed system dependencies. We hope this work will increase adoption of Velox in downstream projects and make troubleshooting of build issues easier, as well as improve overall reliability and stability. - -We also wanted to lower the barrier to entry for contributions to Velox. Therefore, we created Docker [Development images](https://github.com/facebookincubator/velox/pkgs/container/velox-dev) for both Ubuntu and CentOS, and we now publish them automatically when changes are merged. We hope this work will help speed up the development process by allowing developers to stand up a development environment quickly, without the requirement of installing third-party dependencies locally. We also use these images in the Velox CI to lower build times and speed up the feedback loop for proposing a PR. - -```bash -# Run the development image from the root of the Velox repository -# to build and test Velox -docker compose run --rm ubuntu-cpp -``` - -An important non-technical improvement is the introduction of new issue templates and utility scripts. These will help guide troubleshooting and getting support from the relevant Velox developers via Github. This helps to improve the experience for the community and make it easier for users and contributors to get help and support when they need it. - -Lastly, we implemented new [nightly builds](https://app.circleci.com/pipelines/github/facebookincubator/velox?branch=main) to increase the overall reliability and stability of Velox, as well as test the integration with downstream community projects. - -To enable easy access to Velox from Python, we built CI infrastructure to generate and publish pre-built binary wheels for [PyVelox](https://pypi.org/project/pyvelox/) (the Velox Python Bindings). We also improved Conda support by contributing to upstream feedstocks. - -```bash -# Try PyVelox today! -pip install pyvelox -``` - -## Future Goals - -We will continue the work of moving all dependencies to the new dependency management system to move closer to hermetic builds and make development and usage as smooth as possible. - -In the same theme, the next major goal is the refactoring of the existing CMake build system to use a target based "modern" style. This will allow us to properly install Velox as a system library to be used by other projects. This project will improve the development experience overall by creating a stable, reliable build infrastructure, but also allows us to publish Velox as a conda-forge package and make it easier to further improve support for non x86_64 architectures like Apple Silicon, arm64 systems, various compilers and older CPUs that don’t support the currently obligatory instructions sets like BMI2 and make Velox available to an even larger community. - -Confidence in the stability and reliability of a project are key when you want to deploy it as part of your stack. Therefore, we are working on a release process and versioning scheme for Velox so that you can deploy with confidence! - -In conclusion, the collaboration between Velox and Voltron Data has led to several key improvements in Velox's packaging and CI. Setting up a new environment with Velox has never been this easy! With the new improvements, this new broader community of developers and contributors can expect a smoother and more user-friendly experience when using Velox. The Velox team is continuously working towards further improving the developer and user experience, and we invite you to [join us](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) in building the next generation unified execution engine! diff --git a/website/blog/2023-05-01-simple-2.mdx b/website/blog/2023-05-01-simple-2.mdx deleted file mode 100644 index 7d0753a15f5d..000000000000 --- a/website/blog/2023-05-01-simple-2.mdx +++ /dev/null @@ -1,111 +0,0 @@ ---- -slug: simple-functions-2 -title: "Simple Functions: Efficient Complex Types" -authors: [lsakka] -tags: [tech-blog, simple-functions] ---- -_This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface_. - - -## Efficient Complex Types -In this blogpost, we will discuss two major recent changes to the simple function interface to make its performance comparable to the vector function implementations for functions that produce or consume complex types (Arrays, Maps and Rows). - -To show how much simpler simple functions are. The figure below shows a function _NestedMapSum_ written in both the simple and vector interfaces. The function consumes a nested map and computes the summations of all values and keys. -Note that the vector function implementation is minimal without any special optimization (ex: vector reuse, fast path for flat inputs ..etc). Adding optimizations will make it even longer. - -
- -
NestedMapSum function implemented using vector(left) and simple(right) interfaces.
-
- - -### View types for inputs -The previous representations of input complex types in the simple function interface were computationally expensive. Data from vectors used to be copied into std containers and passed to simple functions to process it. Arrays, Maps and Structs used to be materialized into std::vectors, folly::F14FastMap and std::tuples. - The graph below illustrates the previous approach. -
- -
- -The previous approach has two key inefficiencies; **Eager materialization** : For each row, all the data in the input vector is decoded and read before calling the function. And **Double reading**, the data is read twice once when the input is constructed, and again in the function when it's used. - -In order to mitigate those regressions, Velox introduced View types: ArrayViews, MapViews ...etc. The goal is to keep the authoring simple but achieve at least the performance of a basic vector implementation that decodes input and applies some logic for every row without any special optimizations. - -The view types are **Lazy**, very cheap to construct and do not materialize the underlying data unless the code accesses it.For example, the function _array_first_ only needs to read the first element in every array, moreover the _cardinality_ function does not need to read any elements in the array. They view types have interfaces similar to those of std::containers. - -In a simplified form, an ArrayView stores the length and the offset of the array within the vector, in addition to a pointer to the elements array. Only when an element is accessed then an OptionalAccessor is created, which contains the index of the accessed element and a pointer -to the containing vector. Only when the user calls value() or has_value() on that accessor then the nullity or the value is read. Other view types are implemented in a similar way, -The graph below illustrates the process. - -
- -
- - -The graph below compares the runtime of some functions written in the simple interface before and after the introduction of the view types. The speedup for arrays is around 2X, for maps the speed is much higher > `10X` because materializing the intermediate representation previously involves hashing the elements while constructing the hashmap. Furthermore, the overhead of materialization for nested complex types is very high as well, as reflected in _row_arrays_sum_. - -
- -
Runtimes of functions before and after the introduction of view types, normalized to the runtime of the version that uses the view types.
-
- - -The graph below compares the runtimes of some functions written using the simple interface, a basic vector function implementation with no special optimizations for the non general case, and a vector implementation that is specialized for flat and null free. The bars are annotated with the line of codes (LOC) used to implement each function. - - -
- -
- -We can see that LOC are significantly lower for simple functions. ArraySum with flat and null free optimization is faster because the summation can be optimized much better when it's performed over a sequential array of data. -The reason the simple is faster than the vector for some benchmarks is because we have several optimizations in the simple interface that are not implemented in the basic vector versions. - -### Writer types for outputs - - -A similar pattern of inefficiency existed for functions with complex output types. The graph below shows the previous path of writing complex types through the simple function interface. In the previous path, for each row, the result is first written to a temporary object (std::vector, folly::f14FastMap\<\>, etc.), then serialized into the Velox vector. -
- -
- -We changed the writing path so that the data is written directly into the Velox vector during the function evaluation. By introducing writer types: ArrayWriter, MapWriter, RowWriter. This avoids the double materialization and the unnecessary sorting and hashing for maps. - -Consider the function below for example that constructs an array [0, n). - -
- -
- - -outerArray is an array writer and whenever push_back is called, the underlying vector array is updated directly and a new element is written to it. - -**In order & final elements writing**: Unlike the previous interface, the new writer interface needs to write things in order, since it directly serializes elements into Velox vector buffers. Written elements also can not be modified. - -For example, for a function with an Array\ output , we can't add three maps, and write to them concurrently. The new interface should enforce that one map is written completely before the next one starts. This is because we are serializing things directly in the map vector, and to determine the offset of the new map we need first to know the end offset of the previous one. - -The code below shows a function with Array\ output: -
- -
- -**Compatibility with std::like containers.**: Unfortunately, the new interface is not completely compatible with std::like interfaces, in fact, it deviates syntactically and semantically (for example a std::map enforces unique keys and ordering of elements) while map writer does not. -When the element type is primitive (ex: Array\) we enable std::like APIs (push_back, emplace()). - -But we can not do that for nested complex types (ex:Array\\>) since it breaks the in-order & final elements writing rule mentioned above. - -The figure below shows the performance gain achieved by this change, functions' performance is evaluated before and after the change. -
- -
- - -The chart below compares the performance of those functions with vector functions implementations, a vector function with an optimization that precomputes the total size needed for the output vector and a single resize is also added. -Note that those functions do almost no computation other than constructing the output map. Hence the resize cost becomes very critical, if those were doing more work, then its effect would be less. -Furthermore, the gap indicates that it might be worth it to add a way in the simple interface that enables pre-computing/resizing the output vector size. -
- -
- - -### Examples: - -For full documentation of the view and writer types, APIs, and how to write simple functions follow thelink. diff --git a/website/blog/2023-07-19-array-sort.mdx b/website/blog/2023-07-19-array-sort.mdx deleted file mode 100644 index 279179f39b1b..000000000000 --- a/website/blog/2023-07-19-array-sort.mdx +++ /dev/null @@ -1,203 +0,0 @@ ---- -slug: array-sort -title: array_sort lambda function -authors: [mbasmanova] -tags: [tech-blog,functions] ---- - -Presto provides an array_sort -function to sort arrays in ascending order with nulls placed at the end. - -``` -presto> select array_sort(array[2, 5, null, 1, -1]); - _col0 ---------------------- - [-1, 1, 2, 5, null] -``` - -There is also an array_sort_desc -function that sorts arrays in descending order with nulls placed at the end. - -``` -presto> select array_sort_desc(array[2, 5, null, 1, -1]); - _col0 ---------------------- - [5, 2, 1, -1, null] -``` - -Both array_sort and array_sort_desc place nulls at the end of the array. - -There is also a version of array_sort -function that takes a comparator lambda function and uses it to sort the array. - -``` -array_sort(array(T), function(T, T, int)) -> array(T) -``` - -A common use case is to sort an array of structs using one of the struct fields as the sorting key. - -``` -presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)], - -> (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0))); - - _col0 ---------------------------------------------------------------------------------------- - [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}] -``` - -This is all very nice and convenient, but there is a catch. - -The documentation says that the "comparator will take two nullable arguments representing two -nullable elements of the array."" Did you notice the word "nullable" in "nullable arguments" -and "nullable elements"? Do you think it is important? It is Ok if the answer is No or Not Really. -Turns out this "nullable" thing is very important. The comparator is expected to handle null -inputs and should not assume that inputs are not null or that nulls are handled automatically. - -Why is it important to handle null inputs? Let’s see what happens if the comparator doesn’t -handle nulls. - -``` -presto> select array_sort(array[2, 3, null, 1], - (x, y) -> if (x < y, -1, if (x > y, 1, 0))); - _col0 ------------------ - [2, 3, null, 1] -``` - -The result array is not sorted! If subsequent logic relies on the array to be sorted the query -will silently return wrong results. And if there is no logic that relies on the sortedness of -the array then why waste CPU cycles on sorting? - -Why is the array not sorted? That’s because the comparator returns 0 whenever x or y is null. - -``` - x < y returns null if x or y is null, then - x > y returns null if x or y is null, then - result is 0 -``` - -This confuses the sorting algorithm as it sees that 1 == null, 2 == null, 3 == null, -but 1 != 2 and 1 != 3. The algorithm assumes that the comparator function is written correctly, -e.g. if a < b then b > a and if a == b and b == c then a == c. Comparator function that doesn’t -handle nulls does not satisfy these rules and causes unpredictable results. - -To handle null inputs, the comparator function needs to be modified, for example, like so: - -``` - (x, y) -> CASE WHEN x IS NULL THEN 1 - WHEN y IS NULL THEN -1 - WHEN x < y THEN -1 - WHEN x > y THEN 1 - ELSE 0 END -``` - -``` -presto> select array_sort(array[2, 3, null, 1], - -> (x, y) -> CASE WHEN x IS NULL THEN 1 - -> WHEN y IS NULL THEN -1 - -> WHEN x < y THEN -1 - -> WHEN x > y THEN 1 - -> ELSE 0 END - -> ); - _col0 ------------------ - [1, 2, 3, null] -``` - -This is longer and harder to read, but the result array is sorted properly. The new -comparator says that null is greater than any other value, so null is placed at the -end of the array. - -Note: When (x, y) return -1, the algorithm assumes that x \<= y. - - -Writing comparators correctly is not easy. Writing comparators that handle null inputs -is even harder. Having no feedback when a comparator is written incorrectly makes it -yet harder to spot bugs and fix them before a query lands in production and starts -producing wrong results. - -I feel that Presto’s array_sort function with a custom comparator is dangerous and hard -to use and I wonder if it makes sense to replace it with a safer, easier to use alternative. - -``` -array_sort(array(T), function(T, U)) -> array(T) -``` - -This version takes an array and a transform lambda function that specifies how to compute -sorting keys from the array elements. - -To sort array of structs by one of the struct fields, one would write - -``` -presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)], - x -> x[2]) - - _col0 ---------------------------------------------------------------------------------------- - [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}] -``` - -This version would sort the array by the sorting keys computed using the specified lambda -in ascending order placing nulls at the end of the array. - -A matching array_sort_desc function would sort in descending order placing nulls at the end -of the array. - -These functions would be easier to write and read and null handling will happen automatically. - -We implemented these functions in Velox. - - -We also added partial support for array_sort with a comparator lambda function. Expression -compiler in Velox analyzes the comparator expression to determine whether it can be re-written -to the alternative version of array_sort. If so, it re-writes the expression and evaluates it. -Otherwise, it throws an unsupported exception. - -For example, - -``` - array_sort(a, (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0))) -``` - -is re-written to - -``` - array_sort(a, x -> x[2]) -``` - -This rewrite allows Prestissimo and Presto-on-Spark-on-Velox to support common use cases -and do so efficiently. - -The rewrite handles a few different ways to express the same comparator. Some examples: - -``` - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) < f(y), -1, if(f(x) > f(y), 1, 0)) - - // becomes array_sort_desc(a, f(x)) - (x, y) -> if(f(x) < f(y), 1, if(f(x) > f(y), -1, 0)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) < f(y), -1, if(f(x) = f(y), 0, 1)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) = f(y), 0, if(f(x) < f(y), -1, 1)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(y) < f(x), 1, if(f(x) < f(y), -1, 0)) -``` - -Why didn’t we implement full support for comparator lambda functions in array_sort? Actually, -we couldn’t think of an efficient way to do that in a vectorized engine. Velox doesn’t use code -generation and interprets expressions. It can do that efficiently if it can process data in large -batches. array_sort with custom comparator doesn’t lend itself well to such processing. - -array_sort with a transform lambda works well in a vectorized engine. To process a batch of arrays, -Velox first evaluates the transform lambda on all the elements of the arrays, then sorts the results. - - -For further reading, consider the Vectorized and performance-portable Quicksort -blog post from Google. - -Thank you Orri Erling for brainstorming -and Xiaoxuan Meng for reviewing the code. diff --git a/website/blog/2023-09-06-optimize-try-cast.mdx b/website/blog/2023-09-06-optimize-try-cast.mdx deleted file mode 100644 index 8a823e7467cd..000000000000 --- a/website/blog/2023-09-06-optimize-try-cast.mdx +++ /dev/null @@ -1,111 +0,0 @@ ---- -slug: optimize-try_cast. -title: "Learnings from optimizing try_cast" -authors: [lsakka] -tags: [tech-blog, performance] ---- - -One of the queries shadowed internally at Meta was much slower in Velox compared to presto(2 CPU days vs. 4.5 CPU hours). Initial investigation identified that the overhead is related to casting empty strings inside a try_cast. - -In this blogpost I summarize my learnings from investigating and optimizing try_cast. - --------------------------------------------------------------------------------------------- - -### Start and end results - -Initial benchmark: -``` -name total time -try_cast(empty_string_col as int) 4.88s -try_cast(valid_string_col as int) 2.15ms -``` - -The difference between casting a valid and invalid input is huge (>1000X), although ideally casting an invalid string should be -just setting a null and should not be that expensive. - -Benchmark results after optimization: -``` -name total time -try_cast(empty_string_col as int) 1.24ms -try_cast(valid_string_col as int) 2.15ms -``` --------------------------------------------------------------------------------------------- - -### Sources of regression -The investigation revealed several factors that contributed to the huge gap, summarized in the points below in addition to -their approximate significance. - - -**Error logs overhead.** - -Whenever a VeloxUserError is thrown an error log used to be generated, however those errors are expected to, (1) either get converted to null if is -thrown from within a try, (2) or show up to the user otherwise. Hence, no need for that expensive logging . - -Moreover, each failing row used to generate two log message because VELOX_USER_FAIL was called twice. Disabling logging for user error helped save 2.6s of the 4.88s. - - -**Throwing overhead.** - -Each time a row is casted **four** exception were thrown: - -1. From within Folly library. -2. From Cast in Conversions.h, the function catch the exception thrown by Folly and convert it to Velox exception and throw it. -3. From castToPrimitive function, which catch the exception and threw a new exception with more context. -4. Finally, a forth throw came from applyToSelectedNoThrow which caught an exception and called toVeloxException -to check exception type and re-throw. - -Those are addressed and avoided using the following: - -1. When the input is an empty string, avoid calling folly by directly checking if the input is empty. -2. Remove the catch and re-throw from Conversions.h -3. Introduce setVeloxExceptionError, which can be used to set the error directly in evalContext without throwing (does not call toVeloxException). -4. Optimize applyToSelectedNoThrow to call setVeloxExceptionError if it catches Velox exception. - -With all those changes throwing exceptions is completely avoided when casting empty strings. This takes the runtime down to 382.07ms, -but its still much higher than 2.15ms. - - -** Velox exception construction overhead.** - -Constructing Velox exception is expensive, even when there is no throw at all! Luckily this can be avoided for try_cast, since -the output can be directly set to null without having to use the errorVector to track errors. By doing so the benchmark runtime goes -down to 1.24ms. - --------------------------------------------------------------------------------------------- - - -### Follow up tasks -After all the changes we have the following performance numbers for other patterns of similar expressions -(much better than before but still can be optimized a lot). -``` -try_cast(empty_string_col as int) 1.24ms 808.79 - -try_cast(invalid_string_col as int) 393.61ms 2.54 - -try(cast(empty_string_col as int)) 375.82ms 2.66 - -try(cast(invalid_string_col as int)) 767.74ms 1.30 -``` - -All these can be optimized to have the same runtime cost of the first expression 1.24ms. - -To do that two thing are needed: - - -**1) Tracking errors for try, should not require constructing exceptions** - -The way errors are tracked when evaluating a try expression is by setting values in an ErrorVector; which is a vector of VeloxException pointers. -This forces the construction of a Velox exception for each row, but that is not needed (for try expressions) since only row numbers need to be -tracked to be converted eventually to nulls, but not the actual errors. - -This can be changed such that errors are tracked using a selectivity vector. Its worth noting that for other expressions such as conjunct -expressions this tracking is needed, hence we need to distinguish between both. - -This would help optimize any try(x) expression where x throws for large number of rows. - -**2)Use throw-free cast library** - -Avoiding throw and instead returning a boolean should allow us to directly set null in try_cast and avoid construction of exceptions completely. - -While this is done now for empty strings, its not done for all other types of errors. Folly provides a non-throwing API (folly::tryTo) that can be tried for that purpose. -folly::tryTo. According to the folly documentation *On the error path, you can expect tryTo to be roughly three orders of magnitude faster than the throwing to and to completely avoid any lock contention arising from stack unwinding*. diff --git a/website/blog/2023-10-02-reduce-agg.mdx b/website/blog/2023-10-02-reduce-agg.mdx deleted file mode 100644 index 1e7d2dda745f..000000000000 --- a/website/blog/2023-10-02-reduce-agg.mdx +++ /dev/null @@ -1,178 +0,0 @@ ---- -slug: reduce-agg -title: reduce_agg lambda aggregate function -authors: [mbasmanova] -tags: [tech-blog,functions] ---- - -## Definition - -Reduce_agg -is the only lambda aggregate Presto function. It allows users to define arbitrary aggregation -logic using 2 lambda functions. - -``` -reduce_agg(inputValue T, initialState S, inputFunction(S, T, S), combineFunction(S, S, S)) → S - -Reduces all non-NULL input values into a single value. inputFunction will be invoked for -each non-NULL input value. If all inputs are NULL, the result is NULL. In addition to taking -the input value, inputFunction takes the current state, initially initialState, and returns the -new state. combineFunction will be invoked to combine two states into a new state. The final -state is returned. Throws an error if initialState is NULL or inputFunction or combineFunction -returns a NULL. -``` - -Once can think of reduce_agg as using inputFunction to implement partial aggregation and -combineFunction to implement final aggregation. Partial aggregation processes a list of -input values and produces an intermediate state: - -``` -auto s = initialState; -for (auto x : input) { - s = inputFunction(s, x); -} - -return s; -``` - -Final aggregation processes a list of intermediate states and computes the final state. - -``` -auto s = intermediates[0]; -for (auto i = 1; i < intermediates.size(); ++i) - s = combineFunction(s, intermediates[i]); -} - -return s; -``` - -For example, one can implement SUM aggregation using reduce_agg as follows: - -``` -reduce_agg(c, 0, (s, x) -> s + x, (s, s2) -> s + s2) -``` - -Implementation of AVG aggregation is a bit trickier. For AVG, state is a tuple of sum and -count. Hence, reduce_agg can be used to compute (sum, count) pair, but it cannot compute -the actual average. One needs to apply a scalar function on top of reduce_agg to get the -average. - -``` -SELECT id, sum_and_count.sum / sum_and_count.count FROM ( - SELECT id, reduce_agg(value, CAST(row(0, 0) AS row(sum double, count bigint)), - (s, x) -> CAST(row(s.sum + x, s.count + 1) AS row(sum double, count bigint)), - (s, s2) -> CAST(row(s.sum + s2.sum, s.count + s2.count) AS row(sum double, count bigint))) AS sum_and_count - FROM t - GROUP BY id -); -``` - -The examples of using reduce_agg to compute SUM and AVG are for illustrative purposes. -One should not use reduce_agg if a specialized aggregation function is available. - -One use case for reduce_agg we see in production is to compute a product of input values. - -``` -reduce_agg(c, 1.0, (s, x) -> s * x, (s, s2) -> s * s2) -``` - -Another example is to compute a list of top N distinct values from all input arrays. - -``` -reduce_agg(x, array[], - (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000), - (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000)) -``` - -Note that this is equivalent to the following query: - -``` -SELECT array_agg(v) FROM ( - SELECT DISTINCT v - FROM t, UNNEST(x) AS u(v) - ORDER BY v DESC - LIMIT 1000 -) -``` - -## Implementation - -Efficient implementation of reduce_agg lambda function is not straightforward. Let’s -consider the logic for partial aggregation. - -``` -auto s = initialState; -for (auto x : input) { - s = inputFunction(s, x); -} -``` - -This is a data-dependent loop, i.e. the next loop iteration depends on the results of -the previous iteration. inputFunction needs to be invoked on each input value `x` -separately. Since inputFunction is a user-defined lambda, invoking inputFunction means -evaluating an expression. And since expression evaluation in Velox is optimized for -processing large batches of values at a time, evaluating expressions on one value at -a time is very inefficient. To optimize the implementation of reduce_agg we need to -reduce the number of times we evaluate user-defined lambdas and increase the number -of values we process each time. - -One approach is to - -1. convert all input values into states by evaluating inputFunction(initialState, x); -1. split states into pairs and evaluate combineFunction on all pairs; -1. repeat step (2) until we have only one state left. - -Let’s say we have 1024 values to process. Step 1 evaluates inputFunction expression -on 1024 values at once. Step 2 evaluates combineFunction on 512 pairs, then on 256 -pairs, then on 128 pairs, 64, 32, 16, 8, 4, 2, finally producing a single state. -Step 2 evaluates combineFunction 9 times. In total, this implementation evaluates -user-defined expressions 10 times on multiple values each time. This is a lot more -efficient than the original implementation that evaluates user-defined expressions -1024 times. - -In general, given N inputs, the original implementation evaluates expressions N times -while the new one log2(N) times. - -Note that in case when N is not a power of two, splitting states into pairs may leave -an extra state. For example, splitting 11 states produces 5 pairs + one extra state. -In this case, we set aside the extra state, evaluate combineFunction on 5 pairs, then -bring extra state back to a total of 6 states and continue. - -A benchmark, velox/functions/prestosql/aggregates/benchmarks/ReduceAgg.cpp, shows that -initial implementation of reduce_agg is 60x slower than SUM, while the optimized - implementation is only 3x slower. A specialized aggregation function will always be - more efficient than generic reduce_agg, hence, reduce_agg should be used only when - specialized aggregation function is not available. - -Finally, a side effect of the optimized implementation is that it doesn't support -applying reduce_agg to sorted inputs. I.e. one cannot use reduce_agg to compute an -equivalent of - -``` - SELECT a, array_agg(b ORDER BY b) FROM t GROUP BY 1 -``` - -The array_agg computation depends on order of inputs. A comparable implementation -using reduce_agg would look like this: - -``` - SELECT a, - reduce_agg(b, array[], - (s, x) -> concat(s, array[x]), - (s, s2) -> concat(s, s2) - ORDER BY b) - FROM t GROUP BY 1 -``` - -To respect ORDER BY b, the reduce_agg would have to apply inputFunction to each -input value one at a time using a data-dependent loop from above. As we saw, this -is very expensive. The optimization we apply does not preserve the order of inputs, -hence, cannot support the query above. Note that -Presto doesn't -support applying reduce_agg to sorted inputs either. - - -Thank you Orri Erling for brainstorming -and Xiaoxuan Meng and -Pedro Eugênio Rocha Pedreira for -reviewing the code. diff --git a/website/blog/2024-01-27-like-optimization.mdx b/website/blog/2024-01-27-like-optimization.mdx deleted file mode 100644 index 57ba15f647c7..000000000000 --- a/website/blog/2024-01-27-like-optimization.mdx +++ /dev/null @@ -1,147 +0,0 @@ ---- -slug: like -title: "Improve LIKE's performance" -authors: [xumingming] -tags: [tech-blog,performance] ---- - -## What is LIKE? - -LIKE is a very useful SQL operator. -It is used to do string pattern matching. The following examples for LIKE usage are from the Presto doc: - -``` -SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name) -WHERE name LIKE '%b%' ---returns 'abc' and 'bcd' - -SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name) -WHERE name LIKE '_b%' ---returns 'abc' - -SELECT * FROM (VALUES ('a_c'), ('_cd'), ('cde')) AS t (name) -WHERE name LIKE '%#_%' ESCAPE '#' ---returns 'a_c' and '_cd' -``` - -These examples show the basic usage of LIKE: - -- Use `%` to match zero or more characters. -- Use `_` to match exactly one character. -- If we need to match `%` and `_` literally, we can specify an escape char to escape them. - -When we use Velox as the backend to evaluate Presto's query, LIKE operation is translated -into Velox's function call, e.g. `name LIKE '%b%'` is translated to -`like(name, '%b%')`. Internally Velox converts the pattern string into a regular -expression and then uses regular expression library RE2 -to do the pattern matching. RE2 is a very good regular expression library. It is fast -and safe, which gives Velox LIKE function a good performance. But some popularly used simple patterns -can be optimized using direct simple C++ string functions instead of regex. -e.g. Pattern `hello%` matches inputs that start with `hello`, which can be implemented by direct memory -comparison of prefix ('hello' in this case) bytes of input: - -``` -// Match the first 'length' characters of string 'input' and prefix pattern. -bool matchPrefixPattern( - StringView input, - const std::string& pattern, - size_t length) { - return input.size() >= length && - std::memcmp(input.data(), pattern.data(), length) == 0; -} -``` - -It is much faster than using RE2. Benchmark shows it gives us a 750x speedup. We can do similar -optimizations for some other patterns: - -- `%hello`: matches inputs that end with `hello`. It can be optimized by direct memory comparison of suffix bytes of the inputs. -- `%hello%`: matches inputs that contain `hello`. It can be optimized by using `std::string_view::find` to check whether inputs contain `hello`. - -These simple patterns are straightforward to optimize. There are some more relaxed patterns that -are not so straightforward: - -- `hello_velox%`: matches inputs that start with 'hello', followed by any character, then followed by 'velox'. -- `%hello_velox`: matches inputs that end with 'hello', followed by any character, then followed by 'velox'. -- `%hello_velox%`: matches inputs that contain both 'hello' and 'velox', and there is a single character separating them. - -Although these patterns look similar to previous ones, but they are not so straightforward -to optimize, `_` here matches any single character, we can not simply use memory comparison to -do the matching. And if user's input is not pure ASCII, `_` might match more than one byte which -makes the implementation even more complex. Also note that the above patterns are just for -illustrative purpose. Actual patterns can be more complex. e.g. `h_e_l_l_o`, so trivial algorithm -will not work. - -## Optimizing Relaxed Patterns - -We optimized these patterns as follows. First, we split the patterns into a list of sub patterns, e.g. -`hello_velox%` is split into sub-patterns: `hello`, `_`, `velox`, `%`, because there is -a `%` at the end, we determine it as a `kRelaxedPrefix` pattern, which means we need to do some prefix -matching, but it is not a trivial prefix matching, we need to match three sub-patterns: - -- kLiteralString: hello -- kSingleCharWildcard: _ -- kLiteralString: velox - -For `kLiteralString` we simply do a memory comparison: - -``` -if (subPattern.kind == SubPatternKind::kLiteralString && - std::memcmp( - input.data() + start + subPattern.start, - patternMetadata.fixedPattern().data() + subPattern.start, - subPattern.length) != 0) { - return false; -} -``` - -Note that since it is a memory comparison, it handles both pure ASCII inputs and inputs that -contain Unicode characters. - -Matching `_` is more complex considering that there are variable length multi-bytes character in -unicode inputs. Fortunately there are existing libraries which provides unicode related operations: utf8proc. -It provides functions that tells us whether a byte in input is the start of a character or not, -how many bytes current character consists of etc. So to match a sequence of `_` our algorithm is: - -``` -if (subPattern.kind == SubPatternKind::kSingleCharWildcard) { - // Match every single char wildcard. - for (auto i = 0; i < subPattern.length; i++) { - if (cursor >= input.size()) { - return false; - } - - auto numBytes = unicodeCharLength(input.data() + cursor); - cursor += numBytes; - } -} -``` - -Here: - -- `cursor` is the index in the input we are trying to match. -- `unicodeCharLength` is a function which wraps utf8proc function to determine how many bytes current character consists of. - -So the logic is basically repeatedly calculate size of current character and skip it. - -It seems not that complex, but we should note that this logic is not effective for pure ASCII input. -Every character is one byte in pure ASCII input. So to match a sequence of `_`, we don't need to calculate the size -of each character and compare in a for-loop. In fact, we don't need to explicitly match `_` for pure ASCII input as well. -We can use the following logic instead: -``` -for (const auto& subPattern : patternMetadata.subPatterns()) { - if (subPattern.kind == SubPatternKind::kLiteralString && - std::memcmp( - input.data() + start + subPattern.start, - patternMetadata.fixedPattern().data() + subPattern.start, - subPattern.length) != 0) { - return false; - } -} -``` - -It only matches the kLiteralString pattern at the right position of the inputs, `_` is automatically -matched(actually skipped). No need to match it explicitly. With this optimization we get 40x speedup -for kRelaxedPrefix patterns, 100x speedup for kRelaxedSuffix patterns. - -Thank you Maria Basmanova for spending a lot of time -reviewing the code. diff --git a/website/blog/2024-05-31-optimize-try-more.mdx b/website/blog/2024-05-31-optimize-try-more.mdx deleted file mode 100644 index d72f6aadb295..000000000000 --- a/website/blog/2024-05-31-optimize-try-more.mdx +++ /dev/null @@ -1,355 +0,0 @@ ---- -slug: optimize-try-more -title: Further Optimizing TRY_CAST and TRY -authors: [mbasmanova] -tags: [tech-blog,expressions] ---- - -## TL;DR - -Queries that use TRY or TRY_CAST may experience poor performance and high CPU -usage due to excessive exception throwing. We optimized CAST to indicate -failure without throwing and introduced a mechanism for scalar functions to do -the same. Microbenchmark measuring worst case performance of CAST improved -100x. Samples of production queries show 30x cpu time improvement. - -## TRY and TRY(CAST) - -TRY construct can be applied to any expression to suppress errors and turn them -into NULL results. TRY_CAST is a version of CAST that suppresses errors and -returns NULL instead. - -For example, `parse_datetime('2024-05-', 'YYYY-MM-DD')` fails: - -``` - Invalid format: "2024-05-" is too short -``` - -, but `TRY(parse_datetime('2024-05-', 'YYYY-MM-DD'))` succeeds and returns NULL. - -Similarly, `CAST('foo' AS INTEGER)` fails: - -``` - Cannot cast 'foo' to INT -``` - -, but `TRY_CAST('foo' AS INTEGER)` succeeds and returns NULL. - -## TRY_CAST vs. TRY(CAST) - -TRY can wrap any expression, so one can wrap CAST as well: - -``` - TRY(CAST('foo' AS INTEGER)) -``` - -Wrapping CAST in TRY is similar to TRY_CAST, but not equivalent. TRY_CAST -suppresses only cast errors, while TRY suppresses any error in the expression -tree. - -For example, `CAST(1/0 AS VARCHAR)` fails: - -``` - Division by zero -``` - -, `TRY_CAST(1/0 AS VARCHAR)` also fails: - -``` - Division by zero -``` - -, but `TRY(CAST(1/0 AS VARCHAR))` succeeds and returns NULL. - -In this case, the error is generated by division operation (1/0). TRY_CAST -cannot suppress that error, but TRY can. More generally, TRY(CAST(...)) -suppresses all errors in all expressions that are evaluated to produce -an input for CAST as well as errors in CAST itself, but TRY_CAST suppresses -errors in CAST only. - -## What happens when many rows fail? - -In most cases only a fraction of rows generates an error. However, there are -queries where a large percentage of rows fail. In these cases, a lot of CPU -time goes into handling exceptions. - -For example, one Prestissimo query used 3 weeks of CPU time, 93% of -which was spent processing try(date_parse(...)) expressions where most rows -failed. Here is a profile for that query that shows that all the time went into -stack unwinding: - -
- -
- -This query processes 14B rows, ~70% of which fail in date_parse(...) function -due to the date string being empty. - -``` - presto> select try(date_parse('', '%Y-%m-%d')); - _col0 - ------- - NULL - (1 row) - - - – TRY suppressed Invalid format: "" error and produced a NULL. -``` - -Velox tracks the number of suppressed exceptions per operator / plan node and -reports these as numSilentThrow runtime stat. For this query, Velox reported -21B throws for a single FilterProject node that processed 14B rows. Before the -optimizations, each failing row used to throw twice. An earlier blog post -from Laith Sakka explains why. -After the optimizations this query’s CPU time dropped -to 17h: 30x difference from the original cpu time. Compared to -Presto Java, this query uses 4x less cpu time (originally it used 6x more). - -
- -
- -We observed similar issues with queries that use other functions that parse -strings as well as casts from strings. - -## Solution - -To avoid the performance penalty of throwing exceptions we need to report errors -differently. Google’s Abseil library uses absl::Status to return errors from -void functions and absl::StatusOr to return value or error from non-void -functions. Arrow library -has similar Status and Result. Our own Folly has folly::Expected. -Inspired by these examples we introduced velox::Status and velox::Expected. - -velox::Status holds a generic error code and an error message. - -`velox::Expected` is a typedef for `folly::Expected`. - -For example, a non-throwing modulo operation can be implemented like this: - -``` - Expected mod(int a, int b) { - if (b == 0) { - return folly::makeUnexpected(Status::UserError(“Division by zero”)); - } - - return a % b; - } -``` - -## Non-throwing Simple Functions - -We extended the Simple Function API to allow authoring non-throwing scalar -functions. The function author can now define a ‘call’ method that returns -Status. Such a function can indicate an error by returning a non-OK status. - -``` - Status call(result&, arg1, arg2,..) -``` - -These functions are still allowed to throw and exceptions will be handled -properly, but not throwing improves performance of expressions that use TRY. - -Modulo SQL function would look like this: - -``` - template - struct NoThrowModFunction { - VELOX_DEFINE_FUNCTION_TYPES(TExec); - - Status call(int64_t& result, const int64_t& a, const int64_t& b) { - if (b == 0) { - return Status::UserError("Division by zero"); - } - - result = a % b; - return Status::OK(); - } - }; -``` - -We changed date_parse, parse_datetime, and from_iso8601_date Presto functions -to use the new API and report errors without throwing. - -## Non-throwing Vector functions - -Vector functions can implement non-throwing behavior by leveraging the new -EvalCtx::setStatus(row, status) API. However, nowadays we expect virtually all -functions to be written using Simple Function API. - -## Non-throwing CAST - -CAST is complex. A single name refers to multiple dozen individual operations. -The full matrix of supported conversions is available in the Velox -documentation. Not all casts throw. For example, cast from an integer to a -string does not throw. However, casts from strings may fail in multiple ways. A -common failure scenario is cast from an empty string. Laith Sakka optimized -this use case earlier. - -``` -> select cast('' as integer); -Cannot cast '' to INT -``` - -However, we are also seeing failures in casting non-empty strings and NaN floating point values to integers. - -``` -> select cast(nan() as bigint); -Unable to cast NaN to bigint - -> select cast('123x' as integer); -Cannot cast '123x' to INT -``` - -CAST from string to integer and floating point value is implemented using -`folly::to` template. Luckily there is a non-throwing version: `folly::tryTo`. -We changed our CAST implementation to use `folly::tryTo` to avoid throwing. -Not throwing helped improve performance of TRY_CAST by 20x. - -Still, the profile showed that there is room for further improvement. - -## Do not produce or store error messages under TRY - -After switching to non-throwing implementation, the profile showed that half the -cpu time went into folly::makeConversionError. folly::tryTo returns result or -ConversionCode enum. CAST uses folly::makeConversionError to convert -ConversionCode into a user-friendly error message. This involves allocating and -populating a string for the error message, copying it into the std::range_error -object, then copying it again into Status. This error message is very helpful -if it is being propagated all the way to the user, but it is not needed if the -error is suppressed via TRY or TRY_CAST. - -
- -
- -To solve this problem we introduced a thread-local flag, threadSkipErrorDetails, -that indicates whether Status needs to include a detailed error message or not. -By default, this flag is ‘false’, but TRY and TRY_CAST set it to ‘true’. CAST -logic checks this flag to decide whether to call folly::makeConversionError or -not. This change gives a 3x performance boost to TRY_CAST and 2x -to TRY. - - -``` - if (threadSkipErrorDetails()) { - return folly::makeUnexpected(Status::UserError()); - } - - return folly::makeUnexpected(Status::UserError( - "{}", folly::makeConversionError(result.error(), "").what())); -``` - - -After this optimization, we observed that TRY(CAST(...)) is up to 5x slower than -TRY_CAST when many rows fail. - -The profile revealed that 30% of cpu time went to -EvalCtx::ensureErrorsVectorSize. For every row that fails, we call -EvalCtx::ensureErrorsVectorSize to resize the error vector to accommodate that -row. When many rows fail we end up resizing a lot: resize(1), resize(2), -resize(3),...resize(n). We fixed this by pre-allocating the error vector in the TRY -expression. - -Another 30% of cpu time went into managing reference counts for -`std::shared_ptr` stored in the errors vector. We do not need -error details for TRY, hence, no need to store these values. We fixed this by -making error values in error vector optional and updating EvalCtx::setStatus to -skip writing these under TRY. - -
- -
- -After all these optimizations, the microbenchmark that measures performance of -casting invalid strings into integers showed 100x improvement. The benchmark -evaluates 4 expressions: - -* TRY_CAST(‘’ AS INTEGER) -* TRY(CAST(‘’ AS INTEGER)) -* TRY_CAST(‘$’ AS INTEGER) -* TRY(CAST(‘$’ AS INTEGER)) - -When we started, the benchmark results were: - -``` -=============================================================== -[...]hmarks/ExpressionBenchmarkBuilder.cpp relative time/iter iters/s -================================================================ -cast##try_cast_invalid_empty_input 2.40ms 417.47 -cast##tryexpr_cast_invalid_empty_input 402.63ms 2.48 -cast##try_cast_invalid_nan 392.14ms 2.55 -cast##tryexpr_cast_invalid_nan 827.09ms 1.21 -``` - -At the end the numbers improved 100x: - -``` -cast##try_cast_invalid_empty_input 2.16ms 463.62 -cast##tryexpr_cast_invalid_empty_input 4.29ms 232.95 -cast##try_cast_invalid_nan 5.47ms 182.83 -cast##tryexpr_cast_invalid_nan 7.76ms 128.81 -``` - -Note: The performance of TRY_CAST(‘’ AS INTEGER) hasn’t changed because this -particular use case has been optimized by Laith Sakka earlier. - -## Next steps - -We can identify queries with a high percentage of numSilentThrow rows and -change throwing functions to not throw. - -For simple functions this involves changing the ‘call’ method to return Status -and replacing ‘throw’ statements with return Status::UserError(...). You get -extra points for producing error messages conditionally based on thread-local -flag threadSkipErrorDetails(). - -``` -template -struct NoThrowModFunction { - VELOX_DEFINE_FUNCTION_TYPES(TExec); - - Status call(int64_t& result, const int64_t& a, const int64_t& b) { - if (b == 0) { - If (threadSkipErrorDetails()) { - return Status::UserError(); - } - return Status::UserError("Division by zero"); - } - - result = a % b; - return Status::OK(); - } -}; -``` - -We are changing CAST(varchar AS date) to not throw. - -We provided a non-throwing ‘call’ API for simple functions that never return a -NULL for a non-NULL input. This covers the majority of Presto functions. For -completeness, we would want to provide non-throwing ‘call’ APIs for all other -use cases: - -- bool call() for returning NULL sometimes -- callAscii for processing all-ASCII inputs -- callNullable for processing possibly NULL inputs -- callNullFree for processing complex inputs with all NULLs removed. - -## Acknowledgements - -Thank you Laith Sakka -for doing the initial work to investigate and optimize TRY_CAST for empty strings -and sharing your findings in a blog post. - -Thank you Orri Erling for -adding numSilentThrow runtime stat to report number of suppressed exceptions. - -Thank you Pedro Eugenio Rocha Pedreira -for introducing the velox::Status class. - -Thank you Bikramjeet Vig, -Jimmy Lu, -Orri Erling, -Pedro Eugenio Rocha Pedreira and -Xiaoxuan Meng for brainstorming and -helping with code reviews. diff --git a/website/blog/2024-08-23-ci-migration.mdx b/website/blog/2024-08-23-ci-migration.mdx deleted file mode 100644 index f93a0d335da1..000000000000 --- a/website/blog/2024-08-23-ci-migration.mdx +++ /dev/null @@ -1,76 +0,0 @@ ---- -slug: ci-migration -title: "Optimizing and Migrating Velox CI Workloads to Github Actions" -authors: [jwujciak, kgpai] -tags: [tech-blog, packaging] ---- - -
- -
- -## TL;DR - -In late 2023, the Meta OSS (Open Source Software) Team requested all Meta teams to move the CI deployments from CircleCI to Github Actions. [Voltron Data](http://voltrondata.com) and Meta in collaboration migrated all the deployed Velox CI jobs. For the year 2024, Velox CI spend was on track to overshoot the allocated resources by a considerable amount of money. As part of this migration effort, the CI workloads were consolidated and optimized by Q2 2024, bringing down the projected 2024 CI spend by 51%. - -## Velox’s Continuous Integration Workload - -Continuous Integration (CI) is crucial for Velox’s success as an open source project as it helps protect from bugs and errors, reduces likelihood of conflicts and leads to increased community trust in the project. This is to ensure the Velox builds works well on a myriad of system architectures, operating systems and compilers - along with the ones used internally at Meta. The OSS build version of Velox also supports additional features that aren't used internally in Meta (for example, support for Cloud blob stores, etc.). - -When a pull request is submitted to Velox, the following jobs are executed: - -1. Linting and Formatting workflows: - 1. Header checks - 2. License checks - 3. Basic Linters -2. Ensure Velox builds on various platforms - 1. MacOS (Intel, M1) - 2. Linux (Ubuntu/Centos) -3. Ensure Velox builds under its various configurations - 1. Debug / Release builds - 2. Build default Velox build - 3. Build Velox with support for Parquet, Arrow and External Adapters (S3/HDFS/GCS etc.) - 4. PyVelox builds -4. Run prerequisite tests - 1. Unit Tests - 2. Benchmarking Tests - 1. [Conbench](https://velox-conbench.voltrondata.run/runs/5bd139fffa9b4e0eb020da4d63211121/) is used to store and compare results, and also alert users on regressions - 3. Various Fuzzer Tests (Expression / Aggregation/ Exchange / Join etc) - 4. Signature Check and Biased Fuzzer Tests ( Expression / Aggregation) - 5. Fuzzer Tests using Presto as source of truth -5. Docker Image build jobs - 1. If an underlying dependency is changed, a new Docker CI image is built for - 1. Ubuntu Linux - 2. Centos - 3. Presto Linux image -6. Documentation build and publish Job - 1. If underlying documentation is changed, Velox documentation pages are rebuilt and published - 2. Netlify is used for publishing Velox web pages - -## Velox CI Optimization - -Previous implementation of CI in CircleCI grew organically and was unoptimized, resulting in long build times, and also significantly costlier. This opportunity to migrate to Github Actions helped to take a holistic view of CI deployments and actively optimized to reduce build times and CI spend. Note however, that there has been continued investment in reducing test times to further improve Velox reliability, stability and developer experience. Some of the optimizations completed are: - -1. **Persisting build artifacts across builds**: During every build, the object files and binaries produced are cached. In addition to this, artifacts such as scalar function signatures and aggregate function signatures are produced. These signatures are used to compare with the baseline version, by comparing against the changes in the current PR to determine if the current changes are backwards incompatible or bias the newly added changes. Using a stash to persist these artifacts helps save one build cycle. - -2. **Optimizing our Instances**: Building Velox is Memory and CPU intensive job. Some beefy instances (16-core machines) are used to build Velox. After the build, the build artifacts are copied to smaller instances (4 core machines) to run fuzzer tests and other jobs. Since these fuzzers often run for an hour and are less intensive than the build process, it resulted in significant CI savings while increasing the test coverage. - -## Instrumenting Velox CI Builds - -Velox CI builds were instrumented in Conbench so that it can capture various metrics about the builds: -1. Build times at translation unit / library/ project level. -2. Binary sizes produced at TLU/ .a,.so / executable level. -3. Memory pressure -4. Measure across time how our changes affect binary sizes - -A nightly job is run to capture these build metrics and it is uploaded to Conbench. Velox build metrics report is available here: [Velox Build Metrics Report](https://facebookincubator.github.io/velox/bm-report/) - - - -## Acknowledgements - -A large part of the credit goes to Jacob Wujciak and the team at Voltron Data. We would also like to thank other collaborators in the Open Source Community and at Meta, including but not limited to: - -**Meta**: Sridhar Anumandla, Pedro Eugenio Rocha Pedreira, Deepak Majeti, Meta OSS Team, and others - -**Voltron Data**: Jacob Wujciak, Austin Dickey, Marcus Hanwell, Sri Nadukudy, and others diff --git a/website/blog/2024-12-15-tracing-query-tracing.mdx b/website/blog/2024-12-15-tracing-query-tracing.mdx deleted file mode 100644 index aca5aeb772e1..000000000000 --- a/website/blog/2024-12-15-tracing-query-tracing.mdx +++ /dev/null @@ -1,157 +0,0 @@ ---- -slug: velox-query-tracing -title: "Velox Query Tracing" -authors: [duanmeng, xiaoxmeng, tanjialiang] -tags: [tech-blog, tracing] ---- - -## TL;DR - -The query trace tool helps analyze and debug query performance and correctness issues. It helps prevent -interference from external noise in a production environment (such as storage, network, etc.) by allowing -replay of a part of the query plan and dataset in an isolated environment, such as a local machine. -This is much more efficient for query performance analysis and issue debugging, as it eliminates the need -to replay the whole query in a production environment. - -## How Tracing Works - -The tracing process consists of two distinct phases: the tracing phase and the replaying phase. The -tracing phase is executed within a production environment, while the replaying phase is conducted in -a local development environment. - -**Tracing Phase** -1. Trace replay required metadata, including the query plan fragment, query configuration, -and connector properties, is recorded during the query task initiation. -2. Throughout query processing, each traced operator logs the input vectors or splits -storing them in a designated storage location. -3. The metadata and splits are serialized in JSON format, and the operator data inputs are -serialized using a [presto serializer](https://prestodb.io/docs/current/develop/serialized-page.html). - -**Replaying Phase** -1. Read and deserialize the recorded query plan, extract the traced plan node, and assemble a plan -fragment with customized source and sink nodes. -2. The source node reads the input from the serialized operator inputs on storage and the sink operator -prints or logs out the execution stats. -3. Build a task with the assembled plan fragment in step 1. Apply the recorded query configuration and -connector properties to replay the task with the same input and configuration setup as in production. - -**NOTE**: The presto serialization might lose input vector encoding, such as lazy vector and nested dictionary -encoding, which affects the operator’s execution. Hence, it might not always be the same as in production. - -
- -
- -## Tracing Framework - -### Trace Writers - -There are three types of writers: `TaskTraceMetadataWriter`, `OperatorTraceInputWriter`, -and `OperatorTraceSplitWriter`. They are used in the prod or shadow environment to record -the real execution data. - -- The `TaskTraceMetadataWriter` records the query metadata during task creation, serializes it, - and saves it into a file in JSON format. -- The `OperatorTraceInputWriter` records the input vectors from the target operator, it uses a Presto - serializer to serialize each vector batch and flush immediately to ensure that replay is possible - even if a crash occurs during execution. -- The `OperatorTraceSplitWriter` captures the input splits from the target `TableScan` operator. It - serializes each split and immediately flushes it to ensure that replay is possible even if a crash - occurs during execution. - -### Storage Location - -It is recommended to store traced data in a remote storage system to ensure its preservation and -accessibility even if the computation clusters are reconfigured or encounter issues. This also -helps prevent nodes in the cluster from failing due to local disk exhaustion. - -Users should start by creating a root directory. Writers will then create subdirectories within -this root directory to organize the traced data. A well-designed directory structure will keep -the data organized and accessible for replay and analysis. - -**Metadata Location** - -The `TaskTraceMetadataWriter` is set up during the task creation so it creates a trace directory -named `$rootDir/$queryId/$taskId`. - -**Input Data and Split Location** - -The node ID consolidates the tracing for the same tracing plan node. The pipeline ID isolates the -tracing data between operators created from the same plan node (e.g., HashProbe and HashBuild from -the HashJoinNode). The driver ID isolates the tracing data of peer operators in the same pipeline -from different drivers. - -Correspondingly, to ensure the organized and isolated tracing data storage, the `OperatorTraceInputWriter` -and `OpeartorTraceSplitWriter` are set up during the operator initialization and create a data or split -tracing directory in - -```Shell -$rootDir/$queryId$taskId/$nodeId/$pipelineId/$driverId -``` - -### Memory Management - -Add a new leaf system pool named tracePool for tracing memory usage, and expose it -like `memory::MemoryManager::getInstance()->tracePool()`. - -### Trace Readers - -Three types of readers correspond to the query trace writers: `TaskTraceMetadataReader`, -`OperatorTraceInputReader`, and `OperatorTraceSplitReader`. The replayers typically use -them in the local environment, which will be described in detail in the Query Trace Replayer section. - -- The `TaskTraceMetadataReader` can load the query metadata JSON file and extract the query - configurations, connector properties, and a plan fragment. The replayer uses these to build - a replay task. -- The `OperatorTraceInputReader` reads and deserializes the input vectors in a tracing data file. - It is created and used by a `QueryTraceScan` operator which will be described in detail in - the **Query Trace Scan** section. -- The `OperatorTraceSplitReader` reads and deserializes the input splits in tracing split info files, - and produces a list of `exec::Split` for the query replay. - -### Trace Scan - -As outlined in the **How Tracing Works** section, replaying a non-leaf operator requires a -specialized source operator. This operator is responsible for reading data records during the -tracing phase and integrating with Velox’s `LocalPlanner` with a customized plan node and -operator translator. - -**TraceScanNode** - -We introduce a customized ‘TraceScanNode’ to replay a non-leaf operator. This node acts as -the source node and creates a specialized scan operator, known as `OperatorTraceScan` with -one per driver during the replay. The `TraceScanNode` contains the trace directory for the -designated trace node, the pipeline ID associated with it, and a driver ID list passed during -the replaying by users so that the OperatorTraceScan can locate the right trace input data or -split directory. - -**OperatorTraceScan** - -As described in the **Storage Location** section, a plan node may be split into multiple pipelines, -each pipeline can be divided into multiple operators. Each operator corresponds to a driver, which -is a thread of execution. There may be multiple tracing data files for a single plan node, one file -per driver. - -### Query Trace Replayer - -The query trace replayer is typically used in the local environment and works as follows: -1. Load traced query configurations, connector properties, -and a plan fragment. -2. Extract the target plan node from the plan fragment using the specified plan node ID. -3. Use the target plan node in step 2 to create a replay plan node. Create a replay plan. -4. If the target plan node is a `TableScanNode`, add the replay plan node to the replay plan - as the source node. Get all the traced splits using `OperatorInputSplitReader`. - Use the splits as inputs for task replaying. -5. For a non-leaf operator, add a `QueryTraceScanNode` as the source node to the replay plan and - then add the replay plan node. -6. Add a sink node, apply the query configurations (disable tracing), and connector properties, - and execute the replay plan. - -*Detail usage please see the tracing doc in https://facebookincubator.github.io/velox/develop/debugging/tracing.html* - -## Future Work - -- Add support for more operators -- Customize the replay task execution instead of AssertQueryBuilder -- Supports IcebergHiveConnector -- Add trace replay for an entire pipeline diff --git a/website/blog/2025-02-17-velox-primer-part-1.mdx b/website/blog/2025-02-17-velox-primer-part-1.mdx deleted file mode 100644 index 4066f96b8da2..000000000000 --- a/website/blog/2025-02-17-velox-primer-part-1.mdx +++ /dev/null @@ -1,135 +0,0 @@ ---- -slug: velox-primer-part-1 -title: "A Velox Primer, Part 1" -authors: [oerling, pedroerp] -tags: [tech-blog, primer] ---- - -This is the first part of a series of short articles that will take you through -Velox’s internal structures and concepts. In this first part, we will discuss -how distributed queries are executed, how data is shuffled among different -stages, and present Velox concepts such as Tasks, Splits, Pipelines, Drivers, -and Operators that enable such functionality. - -## Distributed Query Execution - -Velox is a library that provides the functions that go into a *query fragment* in -a distributed compute engine. Distributed compute engines, like Presto and -Spark run so-called *exchange parallel plans*. Exchange is also known as a data -shuffle, and allows data to flow from one stage to the next. Query fragments -are the things connected by shuffles, and comprise the processing that is -executed within a single worker node. A shuffle takes input from a set of -fragments and routes rows of input to particular consumers based on a -characteristic of the data, i.e., a partitioning key. The consumers of the -shuffle read from the shuffle and get rows of data from whatever producer, such -that the partitioning key of these rows matches the consumer. - -Take the following query as an example: - -```Shell -SELECT key, count(*) FROM T GROUP BY key -``` - -Suppose it has **n** leaf fragments that scan different parts of **T** (the -green circles at stage 1). At the end of the leaf fragment, assume there is a -shuffle that shuffles the rows based on key. There are **m** consumer fragments -(the yellow circles at stage 2), where each gets a non-overlapping selection of -rows based on column **key**. Each consumer then constructs a hash table keyed -on **key**, where they store a count of how many times the specific value of -**key** has been seen. - -
- -
- -Now, if there are 100B different values of **key**, the hash table would not -conveniently fit on a single machine. For efficiency, there is a point in -dividing this into 100 hash tables of 1B entries each. This is the point of -exchange parallel scale-out. Think of shuffle as a way for consumers to each -consume their own distinct slice of a large stream produced by multiple -workers. - -Distributed query engines like Presto and Spark both fit the above description. -The difference is in, among other things, how they do the shuffle, but we will -get back to that later. - -## Tasks and Splits - -Within a worker node, the Velox representation of a query fragment is called a -Task (`velox::exec::Task`). A task is informed by mainly two things:  - -* **Plan** - `velox::core::PlanNode`: specifies what the Task does. -* **Splits** - `velox::exec::Split`: specifies the data the Task operates on. - -The Splits correspond to the plan that the Task is executing. For the first -stage Tasks (table scanning), the Splits specify pieces of files to scan. For -the second stage Tasks (group by), their Splits identify the table scan Tasks -from which the group by reads its input. There are file splits -(`velox::connector::ConnectorSplit`) and remote splits -(`velox::exec::RemoteConnectorSplit`). The first identifies data to read, the -second identifies a running Task. - -The distributed engine makes PlanNodes and Splits. Velox takes these and makes -Tasks. Tasks send back statistics, errors and other status information to the -distributed engine.  - -## Pipelines, Drivers and Operators - -Inside a Task, there are *Pipelines*. Each pipeline is a linear sequence of -operators (`velox::exec::Operator`), and operators are the objects that implement -relational logic. In the case of the group by example, the first task has one -pipeline, with a TableScan (`velox::exec::TableScan`) and a PartitionedOutput -(`velox::exec::PartitionedOutput`). The second Task too has one pipeline, with -an Exchange, a LocalExchange, HashAggregation, and a -PartitionedOutput. - -Each pipeline has one or more *Drivers*. A Driver is a container for one linear -sequence of Operators, and typically runs on its own thread. The pipeline is -the collection of Drivers with the same sequence of Operators. The individual -Operator instances belong to each Driver. The Drivers belong to the Task. These -are interlinked with smart pointers such that they are kept alive for as long as -needed. - -An example of a Task with two pipelines is a hash join, with separate pipelines -for the build and for the probe side. This makes sense because the build must -be complete before the probe can proceed. We will talk more about this later. - -The Operators on each Driver communicate with each other through the Driver. -The Driver picks output from one Operator, keeps tabs on stats and passes it to -the next Operator. The data passed between Operators consists of vectors. In -particular, an Operator produces/consumes a RowVector, which is a vector with a -child vector for every column of the relation - it is the equivalent of -RecordBatch in Arrow. All vectors are subclasses of `velox::BaseVector`. - -## Operator Sources, Sinks and State - -The first Operator in a Driver is called a *source*. The source operator takes -Splits to figure the file/remote Task that provides its data, and produces a -sequence of RowVectors. The last operator in the pipeline is called a sink. The -sink operator does not produce an output RowVector, but rather puts the data -somewhere for a consumer to retrieve. This is typically a PartitionedOutput. -The consumer of the PartitionedOutput is an Exchange in a different task, where -the Exchange is in the source position. Operators that are neither sources or -sinks are things like FilterProject, HashProbe, HashAggregation and so on, more -on these later. - -Operators also contain a state. They can be blocked, accepting input, have -output to produce or not, or may be notified that no more input is coming. -Operators do not call the API functions of other Operators directly, but -instead the Driver decides which Operator to advance next. This has the benefit -that no part of the Driver's state is captured in nested function calls. The -Driver has a flat stack and therefore can go on and off thread at any time, -without having to unwind and restore nested function frames.  - -## Recap - -We have seen how a distributed query consists of fragments joined by shuffles. -Each fragment has a Task, which has pipelines, Drivers and Operators. PlanNodes -represent what the task is supposed to do. These tell the Task how to set up -Drivers and Operators. Splits tell the Task where to find input, e.g. from -files or from the other Tasks. A Driver corresponds to a thread of execution. -It may be running or blocked, e.g. waiting for data to become available or for -its consumer to consume already produced data. Operators pass data between -themselves as vectors.  - -In the next article we will discuss the different stages in the life of a query. diff --git a/website/blog/2025-03-25-velox-primer-part-2.mdx b/website/blog/2025-03-25-velox-primer-part-2.mdx deleted file mode 100644 index 27407953695e..000000000000 --- a/website/blog/2025-03-25-velox-primer-part-2.mdx +++ /dev/null @@ -1,200 +0,0 @@ ---- -slug: velox-primer-part-2 -title: "A Velox Primer, Part 2" -authors: [oerling, pedroerp] -tags: [tech-blog, primer] ---- - -In this article, we will discuss how a distributed compute engine executes a -query similar to the one presented in -[our first article](https://velox-lib.io/blog/velox-primer-part-1): - -```Shell -SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey; -``` - -We use the TPC-H schema to illustrate the example, and -[Prestissimo](https://github.com/prestodb/presto/tree/master/presto-native-execution) -as the compute engine orchestrating distributed query execution. Prestissimo is -responsible for the query engine frontend (parsing, resolving metadata, -planning, optimizing) and distributed execution (allocating resources and -shipping query fragments), and Velox is responsible for the execution of plan -fragments within a single worker node. Throughout this article, we will present -which functions are performed by Velox and which by the distributed engine - -Prestissimo, in this example. - -## Query Setup - -Prestissimo first receives the query through a *coordinator* node, which is -responsible for parsing and planning the query. For our sample query, a -distributed query plan with three query fragments will be created: - -1. The first fragment reads the *l_partkey* column from the lineitem table and -divides its output according to a hash of *l_partkey*. -2. The second fragment reads the output from the first fragment and updates a -hash table from *l_partkey* containing the number of times the particular value -of *l_partkey* has been seen (the count(*) aggregate function implementation). -3. The final fragment then reads the content of the hash tables, once the -second fragment has received all the rows from the first fragment. - -
- -
- -The shuffle between the two first fragments partitions the data according to -*l_partkey*. Suppose there are 100 instances of the second fragment. If the hash -of *l_partkey* modulo 100 is 0, the row goes to the first task in the second -stage; if it is 1, the row goes to the second task, and so forth. In this way, -each second stage Task gets a distinct subset of the rows. The shuffle between -the second and third stage is a *gather*, meaning that there is one Task in the -third stage that will read the output of all 100 tasks in the second stage. - -A *Stage* is the set of Tasks that share the same plan fragment. A Task is the -main integration point between Prestissimo and Velox; it’s the Velox execution -instance that physically processes all or part of the data that passes through -the stage. - -To set up the distributed execution, Prestissimo first selects the workers from -the pool of Prestissimo server processes it manages. Assuming that stage 1 is -10 workers wide, it selects 10 server processes and sends the first stage plan -to them. It then selects 100 workers for stage 2 and sends the second stage -plan to these. The last stage that gathers the result has only one worker, so -Prestissimo sends the final plan to only one worker. The set of workers for -each stage may overlap, so a single worker process may host multiple stages of -one query. - -Let's now look more closely at what each worker does at query setup time. - -## Task Setup - -In Prestissimo, the message that sets up a Task in a worker is called *Task -Update*. A Task Update has the following information: the plan, configuration -settings, and an optional list of splits. Splits are further qualified by what -plan node they are intended for, and whether more splits for the recipient plan -node and split group will be coming. - -Since split generation involves enumerating files from storage (so they may -take a while), Presto allows splits to be sent to workers asynchronously, such -that the generation of splits can run in parallel with the execution of the -first splits. Therefore, the first task update specifies the plan and the -config. Subsequent ones only add more splits. - -Besides the plan, the coordinator provides configs as maps from string key to -string value, both top level and connector level. The connector configs have -settings for each connector; connectors are used by table scan and table writer -to deal with storage and file formats. These configs and other information, -like thread pools, top level memory pool etc. are handed to the Task in a -QueryCtx object. See `velox/core/QueryCtx.h` in the Velox repo for details. - -## From Plans to Drivers and Operators - -Once the Velox Task is created, a TaskManager hands it splits to work on. This -is done with `Task::addSplit()`, and can be done after the Task has started -executing. See `velox/exec/Task.h` for details. - -Let's zoom into what happens at Task creation: A PlanNode tree specifying what -the Task does is given to the Task as part of a *PlanFragment*. The most -important step done at Task creation is splitting the plan tree into pipelines. -Each pipeline then gets a DriverFactory, which is the factory class that makes -Drivers for the pipeline. The Drivers, in their turn, contain the Operators -that do the work of running the query. The DriverFactories are made in -LocalPlanner.cpp. See `LocalPlanner::plan` for details. - -
- -
- -Following the execution model known as Volcano, the plan is represented by an -operator tree where each node consumes the output of its child operators, and -returns output to the parent operator. The root node is typically a -PartitionedOutputNode or a TableWriteNode. The leaf nodes are either -TableScanNode, ExchangeNode or ValuesNode (used for query literals). The full -set of Velox PlanNode can be found at velox/core/PlanNode.h. - -The PlanNodes mostly correspond to Operators. PlanNodes are not executable as -such; they are only a structure describing how to make Drivers and Operators, -which do the actual execution. If the tree of nodes has a single branch, then -the plan is a single pipeline. If it has nodes with more than one child -(input), then the second input of the node becomes a separate pipeline. - -`Task::start()` creates the DriverFactories, which then create the Drivers. To -start execution, the Drivers are queued on a thread pool executor. The main -function that runs Operators is `Driver::runInternal()`. See this function for -the details of how Operators and the Driver interface: `Operator::isBlocked()` -determines if the Driver can advance. If it cannot, it goes off thread until a -future is realized, which then puts it back on the executor. - -getOutput() retrieves data from an Operator and addInputs() feeds data into -another Operator. The order of execution is to advance the last Operator which -can produce output and then feed this to the next Operator. if an Operator -cannot produce output, then getOutput() is called on the Operator before it -until one is found that can produce data. If no operator is blocked and no -Operator can produce output, then the plan is at end. The noMoreInput() method -is called on each operator. This can unblock production of results, for -example, an OrderBy can only produce its output after it knows that it has all -the input. - -## The Minimal Pipeline: Table Scan and Repartitioning - -**Table Scan.** With the table scan stage of our sample query, we have one pipeline -with two operators: TableScan and PartitionedOutput. Assume this pipeline has -five Drivers, and that all these five Drivers go on the thread pool executor. -The PartitionedOutput cannot do anything because it has no input. The -TableScan::getOutput() is then called. See `velox/exec/TableScan.cpp` for -details. The first action TableScan takes is to look for a Split with -task::getSplitOrFuture(). If there is no split available, this returns a -future. The Driver will then park itself off thread and install a callback on -the future that will reschedule the Driver when a split is available. - -It could also be the case that there is no split and the Task has been notified -that no more splits will be coming. In this case TableScan would be at the end. -Finally, if a Split is available, TableScan interprets it. Given a TableHandle -specification provided as part of the plan (list of columns and filters), the -Connector (as specified in the Split) makes a DataSource. The DataSource -handles the details of IO and file and table formats. - -The DataSource is then given the split. After this, DataSource::next() can be -called repeatedly to get vectors (batches) of output from the file/section of -file specified by the Split. If the DataSource is at the end, TableScan looks -for the next split. See Connector.h for the Connector and DataSource -interfaces. - -**Repartitioning.** Now we have traced execution up to the TableScan returning its -first batch of output. The Driver feeds this to PartitionedOutput::addInput(). -See PartitionedOutput.cpp for details. PartitionedOutput first calculates a -hash on the partitioning key, in this case, *l_partkey*, producing a destination -number for each row in the batch RowVectorPtr input_. - -Each destination has a partly filled serialization buffer (VectorStreamGroup) -for each destination worker. If there are 100 Tasks in the second stage, each -PartitionedOutput has 100 destinations, each with one VectorStreamGroup. The -main function of a VectorStreamGroup is append(), which takes a RowVectorPtr -and a set of row numbers in it. It serializes each value identified by the row -numbers and adds it to the partly formed serialization. When enough rows are -accumulated in the VectorStreamGroup, it produces a SerializedPage. See flush() -in PartitionedOutput.cpp. - -The SerializedPage is a self contained serialized packet of information that -can be transmitted over the wire to the next stage. Each such page only -contains rows intended for the same recipient. These pages are then queued up -in the worker process' OutputBufferManager. Note the code with BlockingReason -in flush(). The buffer manager maintains separate queues of all consumers of -all Tasks. If a queue is full, adding output may block. This returns a future -that is realized when there is space to add more data to the queue. This -depends on when the Task's consumer Task fetches the data. - -Shuffles in Prestissimo are implemented by PartitionedOutput at the producer -and Exchange at the consumer end. The OutputBufferManager keeps ready -serialized data for consumers to pick up. The binding of these to the Presto -wire protocols is in TaskManager.cpp for the producer side, and in -PrestoExchangeSource.cpp for the consumer side. - -## Recap - -We presented how a plan becomes executable and how data moves between and -inside Operators. We discussed that a Driver can block (go off thread) to wait -for a Split to become available, or to wait for its output to be consumed. We -have now scratched the surface of running a leaf stage of a distributed query. -There is much more to Operators and vectors, though. In the next installment of -Velox Primer, we will look at what the second stage of our minimal sample query -does. diff --git a/website/blog/2025-05-12-velox-primer-part-3.mdx b/website/blog/2025-05-12-velox-primer-part-3.mdx deleted file mode 100644 index 424c6736cc94..000000000000 --- a/website/blog/2025-05-12-velox-primer-part-3.mdx +++ /dev/null @@ -1,211 +0,0 @@ ---- -slug: velox-primer-part-3 -title: "A Velox Primer, Part 3" -authors: [oerling, pedroerp] -tags: [tech-blog, primer] ---- - -At the end of the [previous -article](https://velox-lib.io/blog/velox-primer-part-2), we were halfway -through running our first distributed query: - -```Shell -SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey; -``` - -We discussed how a query starts, how tasks are set up, and the interactions -between plans, operators, and drivers. We have also presented how the first -stage of the query is executed, from table scan to partitioned output - or the -producer side of the shuffle.   - -In this article, we will discuss the second query stage, or the consumer side -of the shuffle. - -## Shuffle Consumer - -As presented in the previous post, on the first query stage each worker reads -the table, then produces a series of information packets (*SerializedPages*) -intended for different workers of stage 2. In our example, the lineitem table -has no particular physical partitioning or clustering key. This means that any -row of the table can have any value of *l_partkey* in any of the files forming -the table. So in order to group data based on *l_partkey*, we first need to make -sure that rows containing the same values for *l_partkey* are processed by the -same worker – the data shuffle at the end of stage 1. - -The overall query structure is as follows: - -
- -
- -The query coordinator distributes table scan splits to stage 1 workers in no -particular order. The workers process these and, as a side effect, fill -destination buffers that will be consumed by stage 2 workers. Assuming there -are 100 stage 2 workers, every stage 1 Driver has its own PartitionedOutput -which has 100 destinations. When the buffered serializations grow large enough, -these are handed off to the worker's OutputBufferManager.  - -Now let’s focus on the stage 2 query fragment. Each stage 2 worker has the -following plan fragment: PartitionedOutput over Aggregation over LocalExchange -over Exchange.   - -Each stage 2 Task corresponds to one destination in the OutputBufferManager of -each stage 1 worker. The first stage 2 Tasks fetches destination zero from all -the stage 1 Tasks. The second stage 2 fetches destination 1 from the first -stage Tasks and so on.  Everybody talks to everybody else. The shuffle proceeds -without any central coordination. - -But let's zoom in to what actually happens at the start of stage 2. - -
- -
- -The plan has a LocalExchange node after the Exchange. This becomes two -pipelines: Exchange and LocalPartition on one side, and LocalExchange, -HashAggregation, and PartitionedOutput on the other side.  - -The Velox Task is intended to be multithreaded, with typically 5 to 30 Drivers. -There can be hundreds of Tasks per stage, thus amounting to thousands of -threads per stage. So, each of the 100 second stage workers is consuming 1/100 -of the total output of the first stage. But it is doing this in a multithreaded -manner, with many threads consuming from the ExchangeSource. We will explain -this later. - -In order to execute a multithreaded group by, we can either have a thread-safe -hash table or we can partition the data in **n** disjoint streams, and then proceed -aggregating each stream on its own thread. On a CPU, we almost always prefer to -have threads working on their own memory, so data will be locally partitioned -based on *l_partkey* using a local exchange. CPUs have complex cache coherence -protocols to give observers a consistent ordered view of memory, so -reconciliation after many cores have written the same cache line is both -mandatory and expensive. Strict memory-to-thread affinity is what makes -multicore scalability work.  - -## LocalPartition and LocalExchange - -To create efficient and independent memory access patterns, the second stage -reshuffles the data using a local exchange. In concept, this is like the remote -exchange between tasks, but is scoped inside the Task. The producer side -(LocalPartition) calculates a hash on the partitioning column *l_partkey*, and -divides this into one destination per Driver in the consumer pipeline. The -consumer pipeline has a source operator LocalExchange that reads from the queue -filled by LocalPartition. See -[LocalPartition.h](https://github.com/facebookincubator/velox/blob/main/velox/exec/LocalPartition.h) -for details. Also see the code in -[Task.cpp](https://github.com/facebookincubator/velox/blob/main/velox/exec/Task.cpp) -for setting up the queues between local exchange producers and consumers. - -While remote shuffles work with serialized data, local exchanges pass in-memory -vectors between threads. This is also the first time we encounter the notion of -using columnar encodings to accelerate vectorized execution. Velox became known -by its extensive use of such techniques, which we call *compressed execution*. In -this instance, we use dictionaries to slice vectors across multiple -destinations – we will discuss it next. - -## Dictionary Magic - -Query execution often requires changes to the cardinality (number of rows in a -result) of the data. This is essentially what filters and joins do – they -either reduce the cardinality of the data, e.g., filters or selective joins, or -increase the cardinality of the data, e.g. joins with multiple key matches. - -Repartitioning in LocalPartition assigns a destination to each row of the input -based on the partitioning key. It then makes a vector for each destination with -just the rows for that destination. Suppose rows 2, 8 and 100 of the current -input hash to 1. Then the vector for destination 1 would only have rows 2, 8 -and 100 from the original input. We could make a vector of three rows by -copying the data. Instead, we save the copy by wrapping each column of the -original input in a DictionaryVector with length 3 and indices 2, 8 and 100. -This is much more efficient than copying, especially for wide and nested data. - -Later on, the LocalExchange consumer thread running destination 1 will see a -DictionaryVector of 3 rows. When this is accessed by the HashAggregation -Operator, the aggregation sees a dictionary and will then take the indirection -and will access for row 0 the value at 2 in the base (inner) vector, for row 1 -the value at 8, and so forth. The consumer for destination 0 does the exact -same thing but will access, for example, rows 4, 9 and 50. - -The base of the dictionaries is the same on all the consumer threads. Each -consumer thread just looks at a different subset. The cores read the same cache -lines, but because the base is not written to (read-only), there is no -cache-coherence overhead.  - -To summarize, a *DictionaryVector\* is a wrapper around any vector of T. The -DictionaryVector specifies indices, which give indices into the base vector. -Dictionary encoding is typically used when there are few distinct values in a -column. Take the strings *“experiment”* and *“baseline”*. If a column only has -these values, it is far more efficient to represent it as a vector with -*“experiment”* at 0 and *“baseline”* at 1, and a DictionaryVector that has, -say, 1000 elements, where these are either the index 0 or 1.   - -Besides this, DictionaryVectors can also be used to denote a subset or a -reordering of elements of the base. Because all places that accept vectors also -accept DictionaryVectors, the DictionaryVector becomes the universal way of -representing selection. This is a central precept of Velox and other modern -vectorized engines. We will often come across this concept. - -## Aggregation and Pipeline Barrier - -We have now arrived at the second pipeline of stage 2. It begins with -LocalExchange, which then feeds into HashAggregation. The LocalExchange picks a -fraction of the Task's input specific to its local destination, about -1/number-of-drivers of the task's input. - -We will talk about hash tables, their specific layout and other tricks in a -later post. For now, we look at HashAggregation as a black box. This specific -aggregation is a final aggregation, which is a full pipeline barrier that only -produces output after all input is received. - -How does the aggregation know it has received all its input? Let's trace the -progress of the completion signal through the shuffles. A leaf worker knows -that it is at end if the Task has received the “no more splits” message in the -last task update from the coordinator. - -So, if one DataSource inside a tableScan is at end and there are no more -splits, this particular TableScan is not blocked and it is at end. This will -have the Driver call *PartitionedOutput::noMoreInput()* on the tableScan's -PartitionedOutput. This will cause any buffered data for any destination to get -flushed and moved over to OutputBufferManager with a note that no more is -coming. OutputBufferManager knows how many Drivers there are in the TableScan -pipeline. After it has received this many “no more data coming” messages, it -can tell all destinations that this Task will generate no more data for them.  - -Now, when stage 2 tasks query stage 1 producers, they will know that they are -at end when all the producers have signalled that there is no more data coming. -The response to the get data for destination request has a flag identifying the -last batch. The ExchangeSource on the stage 2 Task set the no-more-data flag. -This is then queried by all the Drivers and each of the Exchange Operators sees -this. This then calls noMoreInput in the LocalPartition. This queues up a “no -more data” signal in the local exchange queues. If the LocalExchange at the -start of the second pipeline of stage 2 sees a “no more data” from each of its -sources, then it is at end and noMoreInput is called on the HashAggregation.  - -This is how the end of data propagates. Up until now, HashAggregation has -produced no output, since the counts are not known until all input is received. -Now, HashAggregation starts producing batches of output, which contain the -*l_partkey* value and the number of times this has been seen. This reaches the -last PartitionedOutput, which in this case has only one destination, the final -worker that produces the result set. This will be at end when all the 100 -sources have reported their own end of data. - -## Recap - -We have finally walked through the distributed execution of a simple query. We -presented how data is partitioned between workers in the cluster, and then a -second time over inside each worker. - -Velox and Presto are designed to aggressively parallelize execution, which -means creating distinct, non-overlapping sets of data to process on each -thread. The more threads, the more throughput. Also remember that for CPU -threads to be effective, they must process tasks that are large enough (often -over 100 microseconds worth of cpu time), and not communicate too much with -other threads or write to memory other threads are writing to. This is -accomplished with local exchanges. - -Another important thing to remember from this article is how columnar encodings -(DictionaryVectors, in particular) can be used as a zero-copy way of -representing selection/reorder/duplication. We will see this pattern again with -filters, joins, and other relational operations. - -Next we will be looking at joins, filters, and hash aggregations. Stay tuned! diff --git a/website/blog/2025-07-07-segfault-dependency-update.mdx b/website/blog/2025-07-07-segfault-dependency-update.mdx deleted file mode 100644 index fb02f44d0668..000000000000 --- a/website/blog/2025-07-07-segfault-dependency-update.mdx +++ /dev/null @@ -1,82 +0,0 @@ ---- -slug: segfault-dependency-update -title: "SEGFAULT due to Dependency Update" -authors: [majetideepak, czentgr] -tags: [tech-blog, build] ---- - -## Background -Velox depends on several [libraries](https://github.com/facebookincubator/velox/blob/main/CMake/resolve_dependency_modules/README.md). -Some of these dependencies include open-source libraries from Meta, including [Folly](https://github.com/facebook/folly) and -[Facebook Thrift](https://github.com/facebook/fbthrift). These libraries are in active development and also depend on each other, so they all have to be updated to the same version at the same time. - -Updating these dependencies typically involves modifying the Velox code to align with any public API or semantic changes in these dependencies. -However, a recent upgrade of Folly and Facebook Thrift to version *v2025.04.28.00* caused a *SEGFAULT* only in one unit test in Velox -named *velox_functions_remote_client_test*. - -## Investigation -We immediately put on our [gdb](https://en.wikipedia.org/wiki/GNU_Debugger) gloves and looked at the stack traces. This issue was also reproducible in a debug build. -The SEGFAULT occurred in Facebook Thrift's *ThriftServer* Class during it's initialization but the offending call was invoking a destructor of a certain handler. -However, the corresponding source code was pointing to an invocation of a different function. And this code was present inside a Facebook Thrift -header called *AsyncProcessor.h*. - -This handler (*RemoteServer*) was implemented in Velox as a Thrift definition. Velox compiled this thrift file using Facebook Thrift, and the generated code -was using the *ServerInterface* class in Facebook Thrift. This *ServerInterface* class was further extended from both the *AsyncProcessorFactory* and -*ServiceHandlerBase* interfaces in Facebook Thrift. - -One of the culprits resulting in SEGFAULTs in the past was the conflict due to the usage of Apache Thrift and Facebook Thrift. -However, this was not the root cause this time because we were able to reproduce this issue by just building the test without the Apache Thrift dependency installed. -We were entering a new territory to investigate, and we were not sure where to start. - -We then compiled an example called *EchoService* in Facebook Thrift that was very similar to the *RemoteServer*, and it worked. Then we copied and compiled the Velox *RemoteServer* -in Facebook Thrift and that worked too! So the culprit was likely in the compilation flags, which likely differed between Facebook Thrift and Velox. -We enabled the verbose logging for both builds and were able to spot one difference. We saw the GCC *coroutines* flag being used in the Facebook Thrift build. - -We were also curious about the invocation of the destructor instead of the actual function. We put our gdb gloves back on and dumped the entire -[vtable](https://en.wikipedia.org/wiki/Virtual_method_table) for the *RemoteServer* class and its base classes. The vtables were different when it was built in Velox vs. Facebook Thrift. -Specifically, the list of functions inherited from *ServiceHandlerBase* was different. - -The vtable for the *RemoteServer* handler in the Velox build had the following entries: - -``` -folly::SemiFuture semifuture_onStartServing() -folly::SemiFuture semifuture_onStopRequested() -Thunk ServiceHandlerBase::~ServiceHandlerBase -``` - -The vtable for the *RemoteServer* handler in the Facebook Thrift build had the following entries: - -``` -folly::coro::Task co_onStartServing() -folly::coro::Task co_onStopRequested() -folly::SemiFuture semifuture_onStartServing() -folly::SemiFuture semifuture_onStopRequested() -Thunk ServiceHandlerBase::~ServiceHandlerBase -``` - -Tying up both pieces of evidence, we could conclude that Velox generated a different vtable structure compared to what Facebook Thrift (and thus ThriftServer) was built with. -Looking around further, we noticed that the ServiceHandlerBase was conditionally adding functions based on the *coroutines* compile flag that influences the *FOLLY_HAS_COROUTINES* macro from the portability header. - -``` -class ServiceHandlerBase { - .... - public: -#if FOLLY_HAS_COROUTINES - virtual folly::coro::Task co_onStartServing() { co_return; } - virtual folly::coro::Task co_onStopRequested() { - throw MethodNotImplemented(); - } -#endif - - virtual folly::SemiFuture semifuture_onStartServing() { -#if FOLLY_HAS_COROUTINES - return co_onStartServing().semi(); -#else - return folly::makeSemiFuture(); -#endif - } -``` - -As a result, the ThriftServer would access an incorrect function (*~ServiceHandlerBase* destructor at offset 3 in the first vtable above) instead of the expected -initialization function (*semifuture_onStartServing* at offset 3 in the second vtable above), thus resulting in a SEGFAULT. -We recompiled the Facebook Thrift dependency for Velox with the *coroutines* compile flag disabled, and the test passed. diff --git a/website/blog/2025-07-11-extending-velox-with-cudf.mdx b/website/blog/2025-07-11-extending-velox-with-cudf.mdx deleted file mode 100644 index 80e61b30b2dd..000000000000 --- a/website/blog/2025-07-11-extending-velox-with-cudf.mdx +++ /dev/null @@ -1,34 +0,0 @@ ---- -slug: extending-velox-with-cudf -title: "Extending Velox - GPU Acceleration with cuDF" -authors: [GregoryKimball] -tags: [tech-blog] ---- - -## TL;DR - -This post describes the design principles and software components for extending Velox with hardware acceleration libraries like NVIDIA's [cuDF](https://github.com/rapidsai/cudf). Velox provides a flexible execution model for hardware accelerators, and cuDF's data structures and algorithms align well with core components in Velox. - -## How Velox and cuDF Fit Together - -Extensibility is a key feature in Velox. The cuDF library integrates with Velox using the [DriverAdapter](https://github.com/facebookincubator/velox/blob/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/exec/ToCudf.cpp#L293) interface to rewrite query plans for GPU execution. The rewriting process allows for operator replacement, operator fusion and operator addition, giving cuDF a lot of freedom when preparing a query plan with GPU operators. Once Velox converts a query plan into executable pipelines, the cuDF DriverAdapter rewrites the plan to use GPU operators: - -
- -
- -Generally the cuDF DriverAdapter replaces operators one-to-one. An exception happens when a GPU operator is not yet implemented in cuDF, in which case a [CudfConversion](https://github.com/facebookincubator/velox/blob/main/velox/experimental/cudf/exec/CudfConversion.h) operator must be added to transfer from GPU to CPU, fallback to CPU execution, and then transfer back to GPU. For end-to-end GPU execution where cuDF replaces all of the Velox CPU operators, cuDF relies on Velox's [pipeline-based execution model](https://facebookincubator.github.io/velox/develop/task.html) to separate stages of execution, partition the work across drivers, and schedule concurrent work on the GPU. - -Velox and cuDF benefit from a shared data format, using columnar layout and [Arrow-compatible buffers](https://engineering.fb.com/2024/02/20/developer-tools/velox-apache-arrow-15-composable-data-management/). The [CudfVector](https://github.com/facebookincubator/velox/tree/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/vector) type in the experimental cuDF backend inherits from Velox's [RowVector](https://facebookincubator.github.io/velox/develop/vectors.html#rowvector) type, and manages a `std::unique_ptr` to a `cudf::table` ([link](https://docs.rapids.ai/api/libcudf/stable/classcudf_1_1table)) which owns the GPU-resident data. CudfVector also maintains an `rmm::cuda_stream_view` ([link](https://docs.rapids.ai/api/rmm/stable/librmm_docs/cuda_streams/)) to ensure that asynchronously launched GPU operations are [stream-ordered](https://developer.nvidia.com/blog/using-cuda-stream-ordered-memory-allocator-part-1/). Even when the data resides in GPU memory, CudfVector allows the operator interfaces to follow Velox's standard execution model of producing and consuming RowVector objects. - -The first set of GPU operators in the experimental cuDF backend include [TableScan](https://github.com/facebookincubator/velox/tree/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/connectors), [HashJoin](https://github.com/facebookincubator/velox/blob/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/exec/CudfHashJoin.h), [HashAggregation](https://github.com/facebookincubator/velox/blob/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/exec/CudfHashAggregation.h), [FilterProject](https://github.com/facebookincubator/velox/blob/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/exec/CudfFilterProject.h), [OrderBy](https://github.com/facebookincubator/velox/blob/2a9c9043264a60c9a1b01324f8371c64bd095af9/velox/experimental/cudf/exec/CudfOrderBy.h) and more. cuDF's C++ API covers many features beyond this initial set, including broad support for list and struct types, high-performance string operations, and configurable null handling. These features are critical for running workloads in Velox with correct semantics for Presto and Spark users. Future work will integrate cuDF support for more Velox operators. - -## GPU execution: fewer drivers and larger batches - -Compared to the typical settings for CPU execution, GPU execution with cuDF benefits from a lower driver count and larger batch size. Whereas Velox CPU performs best with ~1K rows per batch and driver count equal to the number of physical CPU cores, we have found Velox's cuDF backend to perform best with ~1 GiB batch size and 2-8 drivers for a single GPU. cuDF GPU operators launch one or more device-wide kernels and a single driver may not fully utilize GPU compute. Additional drivers improve throughput by queuing up more GPU work and avoiding stalling during host-to-device copies. Adding more drivers increases memory pressure linearly, and query processing throughput saturates once GPU compute is fully utilized. Please check out our talk, “[Accelerating Velox with RAPIDS cuDF](https://www.youtube.com/watch?v=l1JEo-mTNlw)” from VeloxCon 2025 to learn more. - -## Call to Action - -The [experimental cuDF backend in Velox](https://github.com/facebookincubator/velox/tree/main/velox/experimental/cudf) is under active development. It is implemented entirely in C++ and does not require CUDA programming knowledge. There are dozens more APIs in cuDF that can be integrated as Velox operators, and many design areas to explore such as spilling, remote storage connectors, and expression handling. - -If you're interested in bringing GPU acceleration to the Velox ecosystem, please join us in the #velox-oss-community channel on the Velox Slack workspace. Your contributions will help push the limits of performance in Presto, Spark and other tools powered by Velox. diff --git a/website/blog/authors.yml b/website/blog/authors.yml deleted file mode 100644 index d616967290c9..000000000000 --- a/website/blog/authors.yml +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -pedroerp: - name: Pedro Pedreira - title: Software Engineer @ Meta - url: https://github.com/pedroerp - image_url: https://github.com/pedroerp.png - -mbasmanova: - name: Masha Basmanova - title: Software Engineer @ Meta - url: https://github.com/mbasmanova - image_url: https://github.com/mbasmanova.png - -majetideepak: - name: Deepak Majeti - title: Software Engineer @ IBM - url: https://github.com/majetideepak - image_url: https://github.com/majetideepak.png - -lsakka: - name: Laith Sakka - title: Software Engineer @ Meta - url: https://github.com/laithsakka - image_url: https://github.com/laithsakka.png - -kgpai: - name: Krishna Pai - title: Software Engineer @ Meta - url: https://github.com/kgpai - image_url: https://github.com/kgpai.png - -jwujciak: - name: Jacob Wujciak-Jens - title: Software Engineer @ Voltron Data - url: https://github.com/assignUser - image_url: https://github.com/assignuser.png - -raulcd: - name: Raúl Cumplido - title: Software Engineer @ Voltron Data - url: https://github.com/raulcd - image_url: https://github.com/raulcd.png - -xumingming: - name: James Xu - title: Software Engineer @ Alibaba - url: https://github.com/xumingming - image_url: https://github.com/xumingming.png - -duanmeng: - name: Meng Duan (macduan) - title: Software Engineer @ ByteDance - url: https://github.com/duanmeng - image_url: https://github.com/duanmeng.png - -xiaoxmeng: - name: Xiaoxuan Meng - title: Software Engineer @ Meta - url: https://github.com/xiaoxmeng - image_url: https://github.com/xiaoxmeng.png - -tanjialiang: - name: Jialiang Tan - title: Software Engineer @ Meta - url: https://github.com/tanjialiang - image_url: https://github.com/tanjialiang.png - -oerling: - name: Orri Erling - title: Software Engineer @ Meta - url: https://github.com/oerling - image_url: https://github.com/oerling.png - -czentgr: - name: Christian Zentgraf - title: Software Engineer @ IBM - url: https://github.com/czentgr - image_url: https://github.com/czentgr.png - -GregoryKimball: - name: Gregory Kimball - title: Software Engineer @ NVIDIA - url: https://github.com/GregoryKimball - image_url: https://github.com/GregoryKimball.png diff --git a/website/docs/community/01-design-philosophy.md b/website/docs/community/01-design-philosophy.md deleted file mode 100644 index 8220f890ccc5..000000000000 --- a/website/docs/community/01-design-philosophy.md +++ /dev/null @@ -1,89 +0,0 @@ ---- -id: design-philosophy -title: Design Philosophy ---- - -## Summary - -This page lists a set of directional principles and values meant to guide -contributors and maintainers as they develop the Velox project. These are not -meant to be hard-and-fast rules, but to inform decision making and help guide -discussions that may come up during the development of Velox. - -## Velox Mission - -The Velox project aspires to: - -> “Accelerate data management systems by providing a **reliable** and -> **unified** **state-of-the-art** **open-source** execution platform” - -The *Velox community* is composed of a set of highly-skilled individuals with the -shared purpose of achieving this mission through the development and -maintenance of the **Velox open-source library** and its ecosystem. In order to -support and inform community efforts as we progress towards achieving the -mission, a few overarching principles and values are highlighted below. - -## Velox Principles - -* **Reliable.** To become the true execution underpinnings for data management - systems across the industry, Velox strives for excellence in reliability. - This means that as a community we heavily invest in enhancing our test - infrastructure, and building sophisticated database testing and validation - frameworks. During Velox development, we do not compromise on stability, - reliability, or correctness. - -* **Unified.** We envision that by unifying execution across data management - systems (hence reducing the existing fragmentation), we can be more efficient - as an engineering community. Therefore, Velox aspires to provide components - which are engine- and dialect-agnostic, containing extensibility APIs through - which the behavior of the library can be extended to match a desired - semantic. - -* **State-of-the-art.** As a unified platform, we believe in the opportunity of - centralizing advanced optimizations and techniques previously only available - in individual monolithic systems (many of which proprietary), making them - easily available everywhere. Although there is a large set of optimizations - that can be added (each adding a layer of complexity), we prioritize adding - the features that provide the largest performance improvement to real - workloads - as opposed to synthetic benchmarks. - -* **Open-source.** We take the old saying by heart: “if you want to go fast, go - alone; if you want to go far, go together”. We believe that by developing - Velox as a community, there is an opportunity to create a virtuous cycle - between developers who would like to leverage state-of-the-art execution in - their systems; and developers, researchers, and hardware vendors who would - like their techniques and hardware platforms to be pervasively adopted in the - industry. - -## Velox Values - -* **High quality.** The development process in Velox is optimized for ensuring - (a) reliability and high-quality, and (b) ease of maintenance in the long - term, not for the productivity of the developer adding the feature. - -* **Test and verification.** Every change in Velox should be accompanied by - tests, and a summary describing how the correctness of the change can be - verified. Oftentimes, extensions to Velox’s automated testing infrastructure - (fuzzers) should also be added by the author to further validate correctness. - -* **Readability.** As a library, Velox coding is optimized for the reader, not - the writer. We appropriately add comments to our code, document APIs, and - produce [documentation](https://facebookincubator.github.io/velox/) about the - main library components. It might take longer to write, but eases maintenance - and other enhancements in the long run. - -* **Code consistency.** The Velox codebase favors consistency over personal - preference. When contributing code, first review and follow the - [CONTRIBUTING.md](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) - guidelines, and strictly adhere to our - [CODING_STYLE.md](https://github.com/facebookincubator/velox/blob/main/CODING_STYLE.md) - document. - -* **Data-driven.** As an execution engine, there is a large number of - optimizations that can be implemented to improve performance. We prioritize - the ones that have clear data (in the form of benchmarks based on real-world - use cases) supporting their efficacy. - -* **Adaptivity.** Exposing too many configuration knobs to users increases the - API complexity, and makes it more error-prone. As much as possible, we try to - make the library self-adapt to find optimal execution configurations. diff --git a/website/docs/community/02-technical-governance.md b/website/docs/community/02-technical-governance.md deleted file mode 100644 index a959b03c4e05..000000000000 --- a/website/docs/community/02-technical-governance.md +++ /dev/null @@ -1,278 +0,0 @@ ---- -id: technical-governance -title: Technical Governance ---- - -This document describes the Velox technical governance structure, how it -operates, and the decision-making process behind the project. - -## Summary - -The Velox community is composed of a set of highly-skilled individuals with the -shared purpose of achieving the project mission. The project adopts a -hierarchical technical governance structure, solely based on individuals and -their merit in the community. Velox’s governing structure is composed of: - -* A community of **contributors** who file issues, make pull requests, ask and - answer questions, and overall contribute to the project. -* A small set of **component maintainers** who are responsible for supporting, - enhancing, and maintaining the health of each component of the Velox library. -* They are supported by a **project leadership council (PLC)**, responsible for - long-term direction and strategy of the project. - * The leadership council has a **chair** who is responsible for settling - eventual disputes. - -All maintainers are expected to have a strong bias towards [Velox’s design -philosophy.](./design-philosophy) - -Beyond the maintainers, the community is encouraged to contribute, file issues, -make proposals, review pull requests, help answer questions, publish blog -posts, share best practices, and be present in the community. Given -contributions, technical skills, and willingness to invest, anyone can be -accepted as a maintainer and granted responsibility over parts of the codebase. - -Technical governance is merit-based and strictly separated from business -governance. Separating technical from business governance ensures that the only -way for an individual to join the technical governance body is through -continued contributions and demonstration of deep technical skills in the -domain, great judgment, and alignment with project principles and philosophy.  - -Furthermore, membership in the technical governance body is for **individuals**, -not companies. That is, there are no seats reserved for specific companies, and -membership is associated with the person rather than their affiliation.  - -## Technical Governance Structure - -### Component Maintainers - -Due to its large breadth and complexity, Velox’s codebase is organized into a -few logical components. Note that these components are logical and do not -necessarily reflect a strict physical separation of files and directories, -although in some cases they do. - -Each Velox component will have its own set of maintainers, composed of a set of -highly-skilled individuals who have continually demonstrated strong technical -expertise, engagement, great technical judgment, and ownership over that -component. They are responsible for: - -* Review and approve pull requests. -* Respond to bug reports and questions about the component. -* Participate in design discussions about the component. -* Maintain and improve codebase health, fix broken and flaky tests, fuzzer - found bugs, and other CI issues. -* Improve test and performance coverage. -* Enhance component design and APIs. -* Produce and keep documentation up-to-date.  - -Component maintainers have independence over local decisions affecting their -component, given alignment with project principles. They also have the right to -dispute decisions made by other component maintainers - especially if it -affects them. When disputes are made, the component maintainer group should -provide a reasonable explanation of the dispute, the relevant arguments, and -the resolution.  - -In the exceptional cases where component maintainers cannot come to a -conclusion themselves, they may escalate it to the PLC for review. The -escalations are resolved by the PLC in accordance with their rules and -procedures. - -### Project Leadership Council - PLC - -The project leadership council is a small group of highly-skilled individuals -who have demonstrated deep and broad expertise in the domain of query execution -and data management, and are actively engaged in the community. They have a -deep understanding of the Velox codebase and all of its integrations, and are -responsible for the project leadership, long-term direction, and strategy. PLC -members are also accountable for upholding the project principles and -philosophy. They are responsible for: - -* Long-term directional leadership of the project. -* Dispute local decisions when in conflict with project values, principles, or - direction. -* Negotiating and resolving contentious issues, upkeeping the project’s best - interest. -* Receiving broad requests for changes from project stakeholders and evaluating - them. - * Localized component-level requests are handled by component maintainers. -* Nominate and confirm component maintainers and other PLC members. - -The project leadership council as a group has the power to dispute decisions -made at the component maintainer level, and to resolve disputes using their -best judgment. PLC members should publicly articulate their decision-making, -and give a clear reasoning for their decisions, vetoes, and dispute resolution. - -### Project Leadership Council Chair - -To ensure the PLC can always reach a decision, they have an assigned and -publicly declared chair amongst them. The chair is responsible for settling -disputes in exceptional cases where the PLC could not come to a consensus. The -PLC chair should publicly articulate their decision-making, and give a clear -reasoning for their decisions. - -## Voting and Nominations - -### The Principles - -* Membership in maintainer groups is given to **individuals** on **merit - basis** after they demonstrated strong expertise of the component through - contributions, code reviews, design discussions, and community engagement. -* For membership in the maintainer group, the individual has to demonstrate - strong and continued alignment with the project principles, philosophy, and - technical direction. -* Component maintainers must demonstrate exceptional code review skills, - proactiveness and thoroughness, in addition to great technical judgment. -* Component maintainers must demonstrate a deep care for engineering - excellence, testability, API modularity, documentation, and more, keeping a - high bar for Velox’s codebase.  -* In order to be considered, individuals must have demonstrated contributions - for a sustained period of time, typically in the order of 6 months or longer. - * This means maintainer group membership is *lagging*, not *leading*. -* No term limits for component maintainers, PLC members, or PLC chair. -* Light criteria of moving component maintenance or PLC members to *emeritus* - status if they don’t actively participate over long periods of time.  -* The membership is for an individual, not a company. - -### The Process for Nomination - -* Anyone in the community can nominate a person to become a component - maintainer. -* Periodically, the project leadership council will go through the nominations, - do light filtering around spam or desk-rejection, and draw up a list of - potential nominees. -* PLC members may request for more information on the nominee. The information - should consist of: - * The nominees depth and breadth of code, review, and design contributions on - the component. - * Testimonials (positive and negative) of the nominee’s interactions with the - maintainers, users, and the community. - * General testimonials of support from the maintainers. -* The PLC then evaluates all information and makes a final decision through - majority vote to confirm or decline the nomination.  - -### Nominating Core Maintainers - -* Any PLC member or component maintainer can nominate someone to become a PLC - member. -* PLC members are responsible for evaluating the nomination. -* The PLC may requests for additional information around the strength of the - candidate to be a PLC member: - * Evidence that the individual is in fact helping drive the overall project - direction. - * List the individual’s contributions to the project. - * Letters of support from other component maintainers or PLC members. - * General letters of support from stakeholders within the Velox community. - * Any new relevant information that is befitting for the candidacy. -* The PLC evaluates all information and makes a final decision through - unanimous vote to confirm or decline the nomination, with a clear public - articulation of their reasoning behind the decision. - -### The Process for Removal - -* Similar to the process for nomination, anyone in the community can nominate a - person to be removed from a component maintainer position or from the PLC. - * A person can also self-nominate to be removed. - * Individuals may also be moved to emeritus status after a year of continued - inactivity in the community.  -* The PLC (excluding persons with conflict of interest) will request and - evaluate the provided evidence, such as Code of Conduct violations or - activity outside the scope of the project that may conflict with the - project’s values. -* The PLC then evaluates all information and makes a final decision through - unanimous vote to confirm or decline the removal, with a clear public - articulation of their reasoning behind the decision. - -### PLC Chair Substitution - -* A super-majority of PLC members (75%) can choose to remove the PLC chair. -* After a removal of the PLC chair or in unforeseen circumstances (such as - permanent unavailability of the current PLC chair), the project leadership - council will follow a ranked-choice voting method to elect a new chair. - -## Decision Making - -### Uncontroversial Changes - -Primary work happens through Issues, Discussions, and pull requests on GitHub. -Component maintainers ultimately approve pull requests allowing them to be -merged without further process. - -Notifying relevant experts about an issue or a pull request is important. -Reviews from experts in the given interest area are strongly preferred, -especially on pull request approvals. Failure to do so might end up with the -change being reverted by the relevant expert. Contributors are also encouraged -to find local experts using git log/blame.  - -The component maintainers along with pull request authors themselves are -responsible for finding and notifying experts to help review a particular -change. - -### Controversial Decision Process - -Substantial changes in a given interest area require a GitHub Issue to be -opened for discussion. Components maintainers are responsible for participating -in the discussion, reviewing, providing feedback, and eventually approving the -changes. If disputes are made over the change, component maintainers are -responsible for settling the issue, or ultimately escalating to the PLC in case -they cannot reach consensus. - -### Re-Scope Project Components - -The PLC is responsible for taking decisions on adding, removing, and re-scoping -project components. They invite proposals from members in the community -(including themselves) for such changes, and should publicly articulate the -reasoning behind the decision. - -### Changes to Governance Mechanics - -Any changes to this document or the overall technical governance mechanics of -the project needs to be voted and unanimously approved by PLC members. - -## General Project Policies - -Velox participants acknowledge that the copyright in all new contributions will -be retained by the copyright holder as independent works of authorship and that -no contributor or copyright holder will be required to assign copyrights to the -project. Except as described below, all code contributions to the project must -be made using the Apache 2.0 License available here: - -[https://www.apache.org/licenses/LICENSE-2.0 -](https://www.apache.org/licenses/LICENSE-2.0) (the "Project License"). - -All outbound code will be made available under the Project License. The -maintainers may approve the use of an alternative open license or licenses for -inbound or outbound contributions on an exception basis. - -## FAQ - -**Q: I would like to help and get engaged with the community. Where do I -start?** -The Velox [contributing -guide](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) -provides guidelines on how new community members can get involved in the -project. - -**Q: Is it possible for an external contributor to become a maintainer and be -granted responsibilities over parts of the codebase?** -This is absolutely possible. The first step is to start contributing to the -existing project area and supporting its health and success. In addition to -this, you can make a proposal through a GitHub issue for new functionality or -changes to improve the project area. - -**Q: What if I am a company looking to use Velox internally for development, can -I be granted or purchase a maintainer or PLC member status?** -No, the Velox project is strictly driven by highly-skilled technical -individuals, and membership in these groups is based on merit in the community. - -**Q: How do I contribute code to the project?** -If the change is relatively minor, a pull request on GitHub can be opened up -immediately for review by the project maintainers. For larger changes, please -open an Issue to make a proposal to discuss prior. Please also see the -[Velox Contributor Guide](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) -for contribution guidelines. - -**Q: Can I become a committer on the project?** -Unfortunately, the commit process to Velox involves an interaction with Meta’s -infrastructure that currently can only be triggered by Meta employees. -Maintainers, however, have the authority to approve PRs, which then get merged -without further process by a Meta-employee-based oncall rotation. We intend to -improve our tooling to relax this restriction in the future.  diff --git a/website/docs/community/03-components-and-maintainers.md b/website/docs/community/03-components-and-maintainers.md deleted file mode 100644 index e23c9ac83ec1..000000000000 --- a/website/docs/community/03-components-and-maintainers.md +++ /dev/null @@ -1,98 +0,0 @@ ---- -id: components-and-maintainers -title: Components and Maintainers ---- - -## Summary - -This page lists individuals of interest in the Velox community, as described in -the [Technical Governance Mechanics](./technical-governance) -page. It is meant to be used by contributors and other stakeholders as a -blueprint to find individuals responsible for different aspects of the project -and codebase. - -Maintainership status is *lagging*, not *leading*, and it is only acquired -through active participation and demonstration of skills and domain-specific -knowledge. All individuals listed in this page are expected to uphold -[Velox’s mission, design philosophy, and principles](./design-philosophy). - -## Project Leadership Council - PLC - -The PLC is responsible for the long-term directional leadership of the project: - -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com - **Chair** -* Orri Erling - [oerling](https://github.com/oerling) / oerling@meta.com -* Pedro Pedreira - [pedroerp](https://github.com/pedroerp) / pedroerp@meta.com -* Xiaoxuan Meng - [xiaoxmeng](https://github.com/xiaoxmeng) / xiaoxmeng@meta.com - -## Component Maintainers - -Component maintainers are responsible for triaging, reviewing, and approving -pull requests concerning the component. They also maintain, enhance, -and advocate for improvements in component design, tests, CI health, and -developer documentation. - -Before working on a new feature or optimization, please review our -[CONTRIBUTING.md](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) -guide and initiate a discussion on Github with the people listed as -maintainers of that component. - -### Vectors, Types, Arrow Bindings: - -* Jimmy Lu - [Yuhta](https://github.com/Yuhta) / jimmylu@meta.com -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com -* Pedro Pedreira - [pedroerp](https://github.com/pedroerp) / pedroerp@meta.com - -### Expression Evaluation - -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com - -### Operators - -* Jimmy Lu - [Yuhta](https://github.com/Yuhta) / jimmylu@meta.com -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com -* Xiaoxuan Meng - [xiaoxmeng](https://github.com/xiaoxmeng) / xiaoxmeng@meta.com - -### Presto Functions - -* Jimmy Lu - [Yuhta](https://github.com/Yuhta) / jimmylu@meta.com -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com - -### Spark Functions - -* Chengcheng Jin - [jinchengchenghh](https://github.com/jinchengchenghh) / chengcheng.jin@ibm.com -* Rui Mo - [rui-mo](https://github.com/rui-mo) / rui.mo@ibm.com -* Zhen Li - [zhli1142015](https://github.com/zhli1142015) / zhli@microsoft.com - -### Memory Management - -* Xiaoxuan Meng - [xiaoxmeng](https://github.com/xiaoxmeng) / xiaoxmeng@meta.com - -### IO, Cache, Hive connector, and File reader/writers - -* Jimmy Lu - [Yuhta](https://github.com/Yuhta) / jimmylu@meta.com -* Xiaoxuan Meng - [xiaoxmeng](https://github.com/xiaoxmeng) / xiaoxmeng@meta.com - -#### Parquet - -* Deepak Majeti - [majetideepak](https://github.com/majetideepak) / deepak.majeti@ibm.com - -#### ORC, DWRF, and Nimble - -* Jimmy Lu - [Yuhta](https://github.com/Yuhta) / jimmylu@meta.com - -### Storage Adapters (S3/HDFS/GCS/ABFS) - -* Deepak Majeti - [majetideepak](https://github.com/majetideepak) / deepak.majeti@ibm.com - -### Builds and CI - -* Christian Zentgraf - [czentgr](https://github.com/czentgr) / czentgr@us.ibm.com -* Deepak Majeti - [majetideepak](https://github.com/majetideepak) / deepak.majeti@ibm.com -* Jacob Wujciak-Jens - [assignUser](https://github.com/assignUser) / jacob@wujciak.de -* Krishna Pai - [kgpai](https://github.com/kgpai) / kpai@meta.com - -### Fuzzers and Test Frameworks - -* Masha Basmanova - [mbasmanova](https://github.com/mbasmanova) / mbasmanova@meta.com -* Pedro Pedreira - [pedroerp](https://github.com/pedroerp) / pedroerp@meta.com diff --git a/website/docs/community/index.md b/website/docs/community/index.md deleted file mode 100644 index 6d5c82ed6535..000000000000 --- a/website/docs/community/index.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -slug: /community/ ---- - -# Community - -Velox is a project created and -[open sourced by Meta in 2023](https://engineering.fb.com/2023/03/09/open-source/velox-open-source-execution-engine/). -Today, Velox is developed and maintained by a community of 200+ individuals from -20+ different organizations. This page contains more information about Velox's -open source community. - -* [Design Philosophy, Principles, and Values](./design-philosophy) -* [Technical Governance](./technical-governance) -* [Components and Maintainers](./components-and-maintainers) diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js deleted file mode 100644 index 466f42a9e452..000000000000 --- a/website/docusaurus.config.js +++ /dev/null @@ -1,151 +0,0 @@ -// @ts-check -// Note: type annotations allow type checking and IDEs autocompletion - -const lightCodeTheme = require('prism-react-renderer').themes.github; -const darkCodeTheme = require('prism-react-renderer').themes.dracula; - -/** @type {import('@docusaurus/types').Config} */ -const config = { - title: 'Velox', - tagline: 'The open source unified execution engine.', - url: 'https://velox-lib.io', - baseUrl: '/', - onBrokenLinks: 'throw', - onBrokenMarkdownLinks: 'warn', - favicon: 'img/favicon.ico', - - // GitHub pages deployment config. - // If you aren't using GitHub pages, you don't need these. - organizationName: 'facebook', // Usually your GitHub org/user name. - projectName: 'docusaurus', // Usually your repo name. - - // Even if you don't use internalization, you can use this field to set useful - // metadata like html lang. For example, if your site is Chinese, you may want - // to replace "en" with "zh-Hans". - i18n: { - defaultLocale: 'en', - locales: ['en'], - }, - - presets: [ - [ - 'classic', - /** @type {import('@docusaurus/preset-classic').Options} */ - ({ - docs: { - sidebarPath: require.resolve('./sidebars.js'), - }, - blog: { - showReadingTime: true, - onUntruncatedBlogPosts: 'ignore', - }, - theme: { - customCss: require.resolve('./src/css/custom.css'), - }, - gtag: { - trackingID: 'G-C6M8TCJBRM', - anonymizeIP: true, - }, - }), - ], - ], - - themeConfig: - /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ - ({ - image: 'img/velox_blog_pic.png', - navbar: { - title: 'Velox', - logo: { - alt: 'Velox Logo', - src: 'img/logo.svg', - }, - items: [ - { - href: 'https://facebookincubator.github.io/velox/', - label: 'Docs', - position: 'left', - }, - { - href: '/docs/community/', - label: 'Community', - position: 'left', - }, - { - to: '/blog', - label: 'Blog', - position: 'left', - }, - { - href: 'https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md', - label: 'Contributing', - position: 'right', - }, - { - href: 'https://github.com/facebookincubator/velox', - label: 'GitHub', - position: 'right', - }, - ], - }, - footer: { - style: 'dark', - links: [ - { - title: 'Documentation', - items: [ - { - label: 'Velox in 10 Minutes', - href: 'https://facebookincubator.github.io/velox/velox-in-10-min.html', - }, - { - label: 'Developer Guides', - href: 'https://facebookincubator.github.io/velox/develop.html', - }, - { - label: 'Presto Functions', - href: 'https://facebookincubator.github.io/velox/functions.html', - }, - { - label: 'Monthly Updates', - href: 'https://facebookincubator.github.io/velox/monthly-updates.html', - }, - ], - }, - { - title: 'Community', - items: [ - { - label: 'Slack', - href: 'https://velox-oss.slack.com', - }, - { - label: 'Contributing', - href: 'https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md', - }, - ], - }, - { - title: 'More', - items: [ - { - label: 'Blog', - to: '/blog', - }, - { - label: 'GitHub', - href: 'https://github.com/facebookincubator/velox', - }, - ], - }, - ], - copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc.`, - }, - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - }, - }), -}; - -module.exports = config; diff --git a/website/package-lock.json b/website/package-lock.json deleted file mode 100644 index 05e289433196..000000000000 --- a/website/package-lock.json +++ /dev/null @@ -1,18184 +0,0 @@ -{ - "name": "website", - "version": "0.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "website", - "version": "0.0.0", - "dependencies": { - "@docusaurus/core": "3.6.3", - "@docusaurus/plugin-google-gtag": "^3.6.3", - "@docusaurus/preset-classic": "3.6.3", - "@mdx-js/react": "^3.1.0", - "axios": "^1.7.9", - "clsx": "^2.1.1", - "prism-react-renderer": "^2.4.1", - "react": "^18.3.1", - "react-dom": "^18.3.1" - }, - 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"docusaurus": "docusaurus", - "start": "docusaurus start", - "build": "docusaurus build", - "swizzle": "docusaurus swizzle", - "deploy": "docusaurus deploy", - "clear": "docusaurus clear", - "serve": "docusaurus serve", - "write-translations": "docusaurus write-translations", - "write-heading-ids": "docusaurus write-heading-ids" - }, - "dependencies": { - "@docusaurus/core": "3.6.3", - "@docusaurus/plugin-google-gtag": "^3.6.3", - "@docusaurus/preset-classic": "3.6.3", - "@mdx-js/react": "^3.1.0", - "axios": "^1.7.9", - "clsx": "^2.1.1", - "prism-react-renderer": "^2.4.1", - "react": "^18.3.1", - "react-dom": "^18.3.1" - }, - "devDependencies": { - "@docusaurus/module-type-aliases": "3.6.3" - }, - "browserslist": { - "production": [ - ">0.5%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "engines": { - "node": ">=18.0" - } -} diff --git a/website/sidebars.js b/website/sidebars.js deleted file mode 100644 index 7757dc2809c6..000000000000 --- a/website/sidebars.js +++ /dev/null @@ -1,20 +0,0 @@ -/** - * Creating a sidebar enables you to: - - create an ordered group of docs - - render a sidebar for each doc of that group - - provide next/previous navigation - - The sidebars can be generated from the filesystem, or explicitly defined here. - - Create as many sidebars as you want. - */ - -// @ts-check - -/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ -const sidebars = { - // By default, Docusaurus generates a sidebar from the docs folder structure - tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], -}; - -module.exports = sidebars; diff --git a/website/src/components/HomepageFeatures/index.js b/website/src/components/HomepageFeatures/index.js deleted file mode 100644 index beb1752ef728..000000000000 --- a/website/src/components/HomepageFeatures/index.js +++ /dev/null @@ -1,35 +0,0 @@ -import React from 'react'; -import clsx from 'clsx'; -import styles from './styles.module.css'; -import useBaseUrl from "@docusaurus/useBaseUrl"; - -const FeatureList = [ -]; - -function Feature({Svg, title, description}) { - return ( -
-
-

{title}

-

{description}

-
-
- ); -} - -export default function HomepageFeatures() { - return ( -
-
-
- {FeatureList.map((props, idx) => ( - - ))} -
-
- -
-
-
- ); -} diff --git a/website/src/components/HomepageFeatures/styles.module.css b/website/src/components/HomepageFeatures/styles.module.css deleted file mode 100644 index b248eb2e5dee..000000000000 --- a/website/src/components/HomepageFeatures/styles.module.css +++ /dev/null @@ -1,11 +0,0 @@ -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} - -.featureSvg { - height: 200px; - width: 200px; -} diff --git a/website/src/components/VeloxConBanner/index.js b/website/src/components/VeloxConBanner/index.js deleted file mode 100644 index 78dce089f33a..000000000000 --- a/website/src/components/VeloxConBanner/index.js +++ /dev/null @@ -1,28 +0,0 @@ -import React from 'react'; -import clsx from 'clsx'; -import styles from './styles.module.css'; -import useBaseUrl from "@docusaurus/useBaseUrl"; - -export default function VeloxConBanner() { - return ( -
-
-
-
-

VeloxCon 2025 Sessions Are Now Available On-Demand

-
- -
-
-
- ); -} diff --git a/website/src/components/VeloxConBanner/styles.module.css b/website/src/components/VeloxConBanner/styles.module.css deleted file mode 100644 index f670fe2fc7da..000000000000 --- a/website/src/components/VeloxConBanner/styles.module.css +++ /dev/null @@ -1,20 +0,0 @@ -.banner { - display: flex; - align-items: center; - padding: 1rem 0; - width: 100%; - background: url("/static/img/banner-pattern.svg") center center #143d99; - color: #fff; -} - -.banner >div>div { - max-width: 600px; - justify-content: space-around; - align-items: center; - margin: 0 auto; - text-align:center; -} - -.banner h2, .banner p { - margin-bottom: 5px; -} diff --git a/website/src/css/custom.css b/website/src/css/custom.css deleted file mode 100644 index 4c9727eaf5e3..000000000000 --- a/website/src/css/custom.css +++ /dev/null @@ -1,55 +0,0 @@ -/** - * Any CSS included here will be global. The classic template - * bundles Infima by default. Infima is a CSS framework designed to - * work well for content-centric websites. - */ - -:root { - --ifm-color-primary: #284294; - --ifm-color-primary-dark: #243b85; - --ifm-color-primary-darker: #22387e; - --ifm-color-primary-darkest: #1c2e68; - --ifm-color-primary-light: #2c49a3; - --ifm-color-primary-lighter: #2e4caa; - --ifm-color-primary-lightest: #3456c2; -} - -[data-theme='dark'] { - --ifm-color-primary: #6897e0; - --ifm-color-primary-dark: #4d84da; - --ifm-color-primary-darker: #3f7bd8; - --ifm-color-primary-darkest: #2762be; - --ifm-color-primary-light: #83aae6; - --ifm-color-primary-lighter: #91b3e8; - --ifm-color-primary-lightest: #bcd1f1; -} - -/* ========== Hero Section ========== */ - -.jumbotron { - background: url("../../static/img/dark_blue_dots.jpg") no-repeat center center; - background-size: cover; - color: white; -} - -.hero__title { - text-align: left; - margin-top: 2rem !important; -} - -.hero__subtitle { - text-align: left; -} - -.button-wrapper { - text-align: left; - margin-top: 2rem !important; -} - -/* Cleaned up heroLogo definition: only one version */ -.heroLogo { - max-width: 200px; - height: auto; - margin-bottom: 2rem; - display: block; -} diff --git a/website/src/pages/index.js b/website/src/pages/index.js deleted file mode 100644 index c97d5d25c6bd..000000000000 --- a/website/src/pages/index.js +++ /dev/null @@ -1,332 +0,0 @@ -import React from 'react'; -import clsx from 'clsx'; -import Link from '@docusaurus/Link'; -import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; -import Layout from '@theme/Layout'; -import HomepageFeatures from '@site/src/components/HomepageFeatures'; -import VeloxConBanner from '@site/src/components/VeloxConBanner'; -import useBaseUrl from '@docusaurus/useBaseUrl'; -import styles from './index.module.css'; -import Head from '@docusaurus/Head'; - -function HomepageHeader() { - const { siteConfig } = useDocusaurusContext(); - - return ( -
-
- Velox Logo - -

- An open-source composable -
- execution engine for data systems -

- -
- - Get Started - - - GitHub - -
-
-
- ); -} - - -function WhatIsVeloxSection() { - return ( -
-
-
-
-

What is Velox?

-

- Velox is a high-performance, open-source execution engine designed for flexibility and reuse. Distributed as a C++ library, it offers modular, composable components that power a wide range of data processing systems from batch and interactive queries to streaming and AI/ML workloads. -

-
-
- Velox Architecture -
-
- - {/* New Ecosystem Section */} -
-

The Velox Ecosystem

-

- Velox powers a growing ecosystem of open-source projects, from Spark acceleration with Apache Gluten to next-gen query engines like Presto C++. It’s also the foundation for ongoing work in hardware-accelerated execution. -

-
-
- Presto C++ -

Presto C++

-

A next-generation query engine built for performance using Velox components.

- - Learn More → - -
-
- Apache Gluten -

Apache Gluten

-

An accelerator for Apache Spark that integrates Velox for native execution.

- - Learn More → - -
-
- NVIDIA cuDF -

NVIDIA cuDF

-

Hardware-accelerated query execution with Velox + GPU acceleration.

- - Learn More → - -
-
-
-
-
- ); -} - -function KeyFeatures() { - return ( -
-
-

Key Features

-
-
- Efficiency Icon -

Efficiency

-

- Democratizes runtime optimizations previously only implemented in individual engines. -

-
- -
- Consistency Icon -

Consistency

-

- By leveraging the same execution library, compute engines can expose the exact same functions, data types, and semantics to their users. -

-
- -
- Reusability Icon -

Reusability

-

- Features and runtime optimizations available in Velox are developed and maintained once, reducing engineering duplication and promoting reusability. -

-
-
-
-
- ); -} - -function TheVeloxCommunity() { - return ( -
-
-

The Velox Community

-
-
-
- Meta - IBM - Nvidia - Google - Microsoft - Pinterest - Voltron Data - DataPelago - Alibaba Cloud - Uber - Bytedance - Intel - - {/* Repeat for seamless loop */} - Meta - IBM - Nvidia - Google - Microsoft - Pinterest - Voltron Data - DataPelago - Alibaba Cloud - Uber - Bytedance - Intel -
-
-
-
-
- ); -} - -function LatestCommunityNews() { - const scrollRef = React.useRef(null); - const [showLeftArrow, setShowLeftArrow] = React.useState(false); - - const scrollLeft = () => { - if (scrollRef.current) { - scrollRef.current.scrollBy({ left: -300, behavior: 'smooth' }); - } - }; - - const scrollRight = () => { - if (scrollRef.current) { - scrollRef.current.scrollBy({ left: 300, behavior: 'smooth' }); - } - }; - - const handleScroll = () => { - if (scrollRef.current) { - setShowLeftArrow(scrollRef.current.scrollLeft > 0); - } - }; - - React.useEffect(() => { - const el = scrollRef.current; - if (!el) return; - el.addEventListener('scroll', handleScroll); - return () => el.removeEventListener('scroll', handleScroll); - }, []); - - return ( -
-
-

Latest Community News

- - {/* Community Stats */} -
-
- GitHub Stars - 3.7k GitHub Stars -
-
- Contributors - 330+ Contributors -
-
- Commits - 5k+ Commits -
-
- - {/* Scrollable Tiles */} - - - {/* LinkedIn CTA */} - -
-
- ); -} - - -export default function Home() { - const { siteConfig } = useDocusaurusContext(); - -return ( - - - Velox | Open-Source Composable Execution Engine - - - - -
- - - - - - -
- - ); -} diff --git a/website/src/pages/index.module.css b/website/src/pages/index.module.css deleted file mode 100644 index e05b87372848..000000000000 --- a/website/src/pages/index.module.css +++ /dev/null @@ -1,493 +0,0 @@ -/** - * CSS files with the .module.css suffix will be treated as CSS modules - * and scoped locally. - */ - -.heroBanner { - padding: 4rem 0; - text-align: center; - position: relative; - overflow: hidden; -} - -@media screen and (max-width: 996px) { - .heroBanner { - padding: 2rem; - } -} - -.buttons { - display: flex; - align-items: center; - justify-content: center; -} - -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} - -.heroLogo { - max-width: 240px; /* Controls width on large screens */ - width: 100%; - height: auto; - margin-bottom: 2rem; - display: block; -} -.heroSection { - background: #f7f9fc; /* subtle light background */ - padding: 5rem 1rem; - text-align: center; -} - -.heroContainer { - display: flex; - flex-direction: column; - align-items: center; - max-width: 800px; - margin: 0 auto; -} - -.heroLogo { - height: 150px; - max-width: 100%; - margin-bottom: 2rem; -} - -@media (max-width: 600px) { - .heroLogo { - height: 72px; - } -} - -.heroTitle { - font-size: 2.75rem; - line-height: 1.3; - font-weight: 700; - color: #111827; /* dark neutral */ - margin-bottom: 2rem; -} - -.buttonGroup { - display: flex; - gap: 1rem; - margin-top: 1rem; -} - -.primaryButton { - background-color: #2b4eff; - color: #fff; - border-radius: 2rem; - padding: 0.75rem 1.5rem; - font-weight: 500; - text-decoration: none; - border: none; -} - -.secondaryButton { - background: transparent; - color: #2b4eff; - border: 1px solid #2b4eff; - border-radius: 2rem; - padding: 0.75rem 1.5rem; - font-weight: 500; - text-decoration: none; -} - -/* What is Velox Section */ - -.whatIsVeloxSection { - padding: 4rem 0; - background-color: #f9f9f9; -} - -.textColumn h2 { - font-size: 1.8rem; /* updated from previous smaller value */ - margin-bottom: 1rem; -} - -.contentRow { - display: flex; - flex-wrap: wrap; - align-items: center; - justify-content: space-between; - gap: 2rem; -} - -.textColumn { - flex: 1 1 45%; - padding-right: 1rem; -} - -.imageColumn { - flex: 1 1 50%; - text-align: center; -} - -.architectureImage { - max-width: 100%; - height: auto; -} - -/* Ecosystem Section */ - -.ecosystemRow { - margin-top: 3rem; -} - -.ecosystemRow h2 { - font-size: 1.8rem; - margin-bottom: 0.5rem; - text-align: left; -} - -.ecosystemDescription { - max-width: 100%; - margin-bottom: 2rem; - font-size: 1rem; - line-height: 1.6; -} - -.ecosystemGrid { - display: flex; - flex-wrap: wrap; - gap: 2rem; - justify-content: space-between; -} - -.ecosystemCard { - flex: 1 1 calc(33.333% - 2rem); - background: #fff; - border: 1px solid #e0e0e0; - padding: 1.5rem; - border-radius: 8px; - text-align: left; -} - -.ecosystemCard img { - max-width: 100px; - margin-bottom: 1rem; -} - -.ecosystemCard h3 { - margin-bottom: 0.5rem; - font-size: 1.2rem; -} - -.ecosystemCard p { - margin-bottom: 1rem; -} - -.ecosystemCard a { - color: var(--ifm-color-primary); - font-weight: 500; - text-decoration: none; -} - -/* Key Features Section */ - -.keyFeaturesSection { - padding: 4rem 0; - background-color: #ffffff; -} - -.keyFeaturesSection h2 { - text-align: left; - font-size: 1.8rem; - margin-bottom: 2rem; -} - -.featureGrid { - display: flex; - flex-wrap: wrap; - gap: 2rem; - justify-content: space-between; -} - -.featureCard { - flex: 1 1 calc(33% - 2rem); - background-color: #f4f4f4; - padding: 2rem 1.5rem; - border-radius: 8px; - text-align: center; - min-width: 240px; -} - -.featureCard img { - max-width: 100px; - height: auto; - margin-bottom: 1rem; -} - -.featureCard h3 { - font-size: 1.2rem; - margin-bottom: 0.5rem; -} - -.featureCard p { - font-size: 1rem; - line-height: 1.5; -} - -.usingSection { - padding: 4rem 0; - background-color: #f9f9f9; -} - -.usingSection h2 { - text-align: left; - font-size: 1.8rem; - margin-bottom: 1.5rem; -} - -/* Horizontal scroll container */ -.logoScroller { - overflow-x: auto; - white-space: nowrap; - padding: 1rem 0; - scrollbar-width: none; /* Firefox */ -} - -.logoScroller::-webkit-scrollbar { - display: none; /* Chrome/Safari */ -} - -.logoTrack { - display: inline-flex; - gap: 2rem; - align-items: center; -} - -.logoTrack img { - height: 40px; - width: auto; - flex-shrink: 0; - transition: transform 0.3s ease; -} - -.logoTrack img:hover { - transform: scale(1.05); -} - -.usingSection { - padding: 4rem 0; - background-color: #f9f9f9; -} - -.usingSection h2 { - text-align: left; - font-size: 1.8rem; - margin-bottom: 1.5rem; -} - -.logoScroller { - overflow: hidden; - position: relative; - width: 100%; -} - -.logoTrack { - display: flex; - width: max-content; - gap: 3rem; - align-items: center; - animation: scrollLeft 25s linear infinite; -} - -.logoScroller:hover .logoTrack { - animation-play-state: paused; -} - -.logoTrack img { - height: 60px; /* 👈 Increase for bigger logos */ - width: auto; - flex-shrink: 0; - opacity: 0.9; - transition: transform 0.3s ease; -} - -.logoTrack img:hover { - transform: scale(1.1); -} - -/* Auto-scroll animation */ -@keyframes scrollLeft { - from { - transform: translateX(0%); - } - to { - transform: translateX(-50%); - } -} - -.logoScrollerWrapper { - position: relative; -} - -.logoScroller::before, -.logoScroller::after { - content: ""; - position: absolute; - top: 0; - width: 60px; - height: 100%; - z-index: 2; - pointer-events: none; -} - -.logoScroller::before { - left: 0; - background: linear-gradient(to right, #f9f9f9 0%, transparent 100%); -} - -.logoScroller::after { - right: 0; - background: linear-gradient(to left, #f9f9f9 0%, transparent 100%); -} - -.metaLogo { - height: 130px !important; -} - -.microsoftLogo { - height: 130px !important; -} - -.googleLogo { - height: 130px !important; -} - -.latestNewsSection { - padding: 4rem 0; - background-color: #ffffff; -} - 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