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bharathts07Bharath Swamykesmit13
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Add python UDF template for Free shared Tier users (#134)
* add(notebooks): python udf template * add(author): entry for bharath * update(python-udf-template): author * Update notebook.ipynb * update(python-udf-template): add directions on how to run the udf --------- Co-authored-by: Bharath Swamy <[email protected]> Co-authored-by: Kevin D Smith <[email protected]>
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‎authors/bharath-swamy.toml‎

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name="Bharath Swamy"
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title="Product Team"
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image="singlestore"
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external=false

‎notebooks/python-udf-template/meta.toml‎

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[meta]
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authors=["bharath-swamy"]
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title="Run your first Python UDF"
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description="""\
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Learn how to connect to create and\
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publish a python UDF and call it in SQL.
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"""
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icon="browser"
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difficulty="beginner"
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tags=["starter", "notebooks", "python"]
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lesson_areas=[]
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destinations=["spaces"]
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minimum_tier="free-shared"

‎notebooks/python-udf-template/notebook.ipynb‎

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{
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"cells": [
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{
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"id": "1ae3a481",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div id=\"singlestore-header\" style=\"display: flex; background-color: rgba(235, 249, 245, 0.25); padding: 5px;\">\n",
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" <div id=\"icon-image\" style=\"width: 90px; height: 90px;\">\n",
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" <img width=\"100%\" height=\"100%\" src=\"https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/header-icons/browser.png\" />\n",
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" </div>\n",
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" <div id=\"text\" style=\"padding: 5px; margin-left: 10px;\">\n",
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" <div id=\"badge\" style=\"display: inline-block; background-color: rgba(0, 0, 0, 0.15); border-radius: 4px; padding: 4px 8px; align-items: center; margin-top: 6px; margin-bottom: -2px; font-size: 80%\">SingleStore Notebooks</div>\n",
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" <h1 style=\"font-weight: 500; margin: 8px 0 0 4px;\">Run your first Python UDF</h1>\n",
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" </div>\n",
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"</div>"
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]
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},
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{
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"id": "bd0ae268",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-block alert-warning\">\n",
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" <b class=\"fa fa-solid fa-exclamation-circle\"></b>\n",
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" <div>\n",
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" <p><b>Note</b></p>\n",
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" <p>This notebook can be run on a Free Starter Workspace. To create a Free Starter Workspace navigate to <tt>Start</tt> using the left nav. You can also use your existing Standard or Premium workspace with this Notebook.</p>\n",
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" </div>\n",
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"</div>"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bcb6e6a7",
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"metadata": {},
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"source": [
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"This Jupyter notebook will help you build your first Python UDF using Notebooks, registering it with your database and calling it as part of SQL query."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "5776ded1",
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"metadata": {},
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"source": [
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"## Create some simple tables\n",
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"\n",
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"This setup establishes a basic relational structure to store some reviews for restaurants. Ensure you have selected a database."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "2bbf6a44",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%sql\n",
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"DROP TABLE IF EXISTS reviews;\n",
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"\n",
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"CREATE TABLE IF NOT EXISTS\n",
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"reviews (\n",
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" review_id INT PRIMARY KEY,\n",
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" store_name VARCHAR(255) NOT NULL,\n",
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" review TEXT NOT NULL\n",
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");"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "3aace2e9",
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"metadata": {},
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"source": [
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"## Insert sample data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0a123cd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%sql INSERT into reviews (review_id, store_name, review) values\n",
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"(\"1\", \"Single Pizza\", \"The staff were very respectful and made thoughtful suggestions. I will definitely go again. 10/10!\"),\n",
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"(\"2\", \"Single Pizza\", \"The food was absolutely amazing and the service was fantastic!\"),\n",
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"(\"3\", \"Single Pizza\", \"The experience was terrible. The food was cold and the waiter was rude.\"),\n",
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"(\"4\", \"Single Pizza\", \"I loved the ambiance and the desserts were out of this world!\"),\n",
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"(\"5\", \"Single Pizza\", \"Not worth the price. I expected more based on the reviews\");"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "d58c8382",
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"metadata": {},
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"source": [
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"## Define Python UDF functions\n",
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"\n",
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"Next, we will be Python UDF function using the `@udf` annotation. We will be using the `VADER` model of `nltk` library to perform sentiment analysis on the review text."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1556ad3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install nltk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f3f3b047",
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"metadata": {},
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"outputs": [],
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"source": [
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"from singlestoredb.functions import udf\n",
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"import nltk\n",
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"from nltk.sentiment import SentimentIntensityAnalyzer\n",
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"\n",
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"nltk.download('vader_lexicon')\n",
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"sia = SentimentIntensityAnalyzer()\n",
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"\n",
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"@udf\n",
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"def review_sentiment(review: str) -> str:\n",
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" print(\"review:\" + review)\n",
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" scores = sia.polarity_scores(review)\n",
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" sentiment = (\n",
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" \"Positive\" if scores['compound'] > 0.05 else\n",
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" \"Negative\" if scores['compound'] < -0.05 else\n",
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" \"Neutral\"\n",
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" )\n",
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" print(\"sentiment:\" + sentiment)\n",
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" return sentiment"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "40e2ad59",
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"metadata": {},
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"source": [
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"## Start the Python UDF server\n",
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"\n",
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"This will start the server as well as register all the functions annotated with `@udf` as external user defined functions on your selected database."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "ed4b22cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import singlestoredb.apps as apps\n",
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"connection_info = await apps.run_udf_app(replace_existing=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "b53cd3d1",
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"metadata": {},
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"source": [
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"## List all registered UDFs\n",
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"\n",
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"In interactive notebooks, the udf function will be suffixed with `_test` to differentiate it from the published version"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6008982d",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%sql\n",
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"SHOW functions"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "58560b03",
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"metadata": {},
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"source": [
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"## Call the UDF from SQL\n",
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"\n",
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"You will now be able to run queries like\n",
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"\n",
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"```\n",
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"SELECT review_id, store_name, review, review_sentiment_test(review) from reviews order by review_id;\n",
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"```\n",
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"from the SQL editor or any other SQL client. Try it out by opening another notebook, selecting the current Database and running this query in a new cell."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "4a825f0d",
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"metadata": {},
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"source": [
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"## Publish Python UDF\n",
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"\n",
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"After validating the Python UDF interactively, you can publish it and access it like\n",
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"\n",
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"```\n",
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"%%sql\n",
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"SELECT review_id, store_name, review, review_sentiment(review) from reviews order by review_id\n",
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"```\n",
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"\n",
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"enriching your data exploration experience seamlessly!"
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]
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},
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{
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"id": "b6c75678",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div id=\"singlestore-footer\" style=\"background-color: rgba(194, 193, 199, 0.25); height:2px; margin-bottom:10px\"></div>\n",
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"<div><img src=\"https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/singlestore-logo-grey.png\" style=\"padding: 0px; margin: 0px; height: 24px\"/></div>"
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]
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}
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],
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"metadata": {
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"jupyterlab": {
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"notebooks": {
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"version_major": 6,
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"version_minor": 4
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}
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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