script to export benchmark information as Line Protocol format

benchmarks/lineformat.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Dict, List, Any
+from pathlib import Path
+from argparse import ArgumentParser
+import sys
+print = sys.stdout.write
+
+
+@dataclass
+class QueryResult:
+    elapsed: float
+    row_count: int
+
+    @classmethod
+    def load_from(cls, data: Dict[str, Any]) -> QueryResult:
+        return cls(elapsed=data["elapsed"], row_count=data["row_count"])
+
+
+@dataclass
+class QueryRun:
+    query: int
+    iterations: List[QueryResult]
+    start_time: int
+
+    @classmethod
+    def load_from(cls, data: Dict[str, Any]) -> QueryRun:
+        return cls(
+            query=data["query"],
+            iterations=[QueryResult(**iteration) for iteration in data["iterations"]],
+            start_time=data["start_time"],
+        )
+
+    @property
+    def execution_time(self) -> float:
+        assert len(self.iterations) >= 1
+
+        # Use minimum execution time to account for variations / other
+        # things the system was doing
+        return min(iteration.elapsed for iteration in self.iterations)
+
+
+@dataclass
+class Context:
+    benchmark_version: str
+    datafusion_version: str
+    num_cpus: int
+    start_time: int
+    arguments: List[str]
+    name: str
+
+    @classmethod
+    def load_from(cls, data: Dict[str, Any]) -> Context:
+        return cls(
+            benchmark_version=data["benchmark_version"],
+            datafusion_version=data["datafusion_version"],
+            num_cpus=data["num_cpus"],
+            start_time=data["start_time"],
+            arguments=data["arguments"],
+            name=data["arguments"][0]
+        )
+
+
+@dataclass
+class BenchmarkRun:
+    context: Context
+    queries: List[QueryRun]
+
+    @classmethod
+    def load_from(cls, data: Dict[str, Any]) -> BenchmarkRun:
+        return cls(
+            context=Context.load_from(data["context"]),
+            queries=[QueryRun.load_from(result) for result in data["queries"]],
+        )
+
+    @classmethod
+    def load_from_file(cls, path: Path) -> BenchmarkRun:
+        with open(path, "r") as f:
+            return cls.load_from(json.load(f))
+
+
+def lineformat(
+    baseline: Path,
+) -> None:
+    baseline = BenchmarkRun.load_from_file(baseline)
+    context = baseline.context
+    benchamrk_str = f"benchmark,name={context.name},version={context.benchmark_version},datafusion_version={context.datafusion_version},num_cpus={context.num_cpus}"
+    for query in baseline.queries:
+        query_str = f"query=\"{query.query}\""
+        timestamp = f"{query.start_time*10**9}"
+        for iter_num, result in enumerate(query.iterations):
+            print(f"{benchamrk_str} {query_str},iteration={iter_num},row_count={result.row_count},elapsed_ms={result.elapsed*1000:.0f} {timestamp}\n")
+
+def main() -> None:
+    parser = ArgumentParser()
+    compare_parser = parser
+    compare_parser.add_argument(
+        "baseline_path",
+        type=Path,
+        help="Path to the baseline summary file.",
+    )
+
+    options = parser.parse_args()
+
+    lineformat(options.baseline_path)
+
+
+
+if __name__ == "__main__":
+    main()