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Posted to github@beam.apache.org by "liferoad (via GitHub)" <gi...@apache.org> on 2023/04/24 21:16:17 UTC
[GitHub] [beam] liferoad commented on a diff in pull request #25969: Add the example to learn transforms
liferoad commented on code in PR #25969:
URL: https://github.com/apache/beam/pull/25969#discussion_r1175789554
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examples/notebooks/get-started/learn_beam_transforms_by_doing.ipynb:
##########
@@ -0,0 +1,740 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "#@title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License."
+ ],
+ "metadata": {
+ "cellView": "form",
+ "id": "QgmD1wbmT4mj"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Learn Beam PTransforms\n",
+ "\n",
+ "After this notebook, you should be able to:\n",
+ "1. Use user-defined functions in your `PTransforms`\n",
+ "2. Learn Beam SDK composite transforms\n",
+ "3. Create you own composite transforms to simplify your `Pipeline`\n",
+ "\n",
+ "For basic Beam `PTransforms`, please check out [this Notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_basics_by_doing.ipynb).\n",
+ "\n",
+ "Beam Python SDK also provides [a list of built-in transforms](https://beam.apache.org/documentation/transforms/python/overview/).\n"
+ ],
+ "metadata": {
+ "id": "RuUHlGZjVt6W"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## How To Approach This Tutorial\n",
+ "\n",
+ "This tutorial was designed for someone who likes to learn by doing. There will be code cells where you can write your own code to test your understanding.\n",
Review Comment:
Done.
##########
examples/notebooks/get-started/learn_beam_transforms_by_doing.ipynb:
##########
@@ -0,0 +1,740 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "#@title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License."
+ ],
+ "metadata": {
+ "cellView": "form",
+ "id": "QgmD1wbmT4mj"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Learn Beam PTransforms\n",
+ "\n",
+ "After this notebook, you should be able to:\n",
+ "1. Use user-defined functions in your `PTransforms`\n",
+ "2. Learn Beam SDK composite transforms\n",
+ "3. Create you own composite transforms to simplify your `Pipeline`\n",
+ "\n",
+ "For basic Beam `PTransforms`, please check out [this Notebook](https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_basics_by_doing.ipynb).\n",
+ "\n",
+ "Beam Python SDK also provides [a list of built-in transforms](https://beam.apache.org/documentation/transforms/python/overview/).\n"
+ ],
+ "metadata": {
+ "id": "RuUHlGZjVt6W"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## How To Approach This Tutorial\n",
+ "\n",
+ "This tutorial was designed for someone who likes to learn by doing. There will be code cells where you can write your own code to test your understanding.\n",
+ "\n",
+ "As such, to get the most out of this tutorial, we strongly recommend typing code by hand as you’re working through the tutorial and not using copy/paste. This will help you develop muscle memory and a stronger understanding."
+ ],
+ "metadata": {
+ "id": "Ldx0Z7nWGopE"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "To begin, run the cell below to install and import Apache Beam."
+ ],
+ "metadata": {
+ "id": "jy1zaj4NDE0T"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pNure-fW8hl3"
+ },
+ "outputs": [],
+ "source": [
+ "# Run a shell command and import beam\n",
+ "!pip install --quiet apache-beam\n",
+ "import apache_beam as beam\n",
+ "beam.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the logging level to reduce verbose information\n",
+ "import logging\n",
+ "\n",
+ "logging.root.setLevel(logging.ERROR)"
+ ],
+ "metadata": {
+ "id": "vyksB2VMtv3m"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "M1ku4nX_Gutb"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1. Simple User-Defined Function (UDF)\n",
+ "\n",
+ "Some `PTransforms` allow you to run your own functions and user-defined code to specify how your transform is applied. For example, the below `CombineGlobally` transform,"
+ ],
+ "metadata": {
+ "id": "0qDeT34SS1_8"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pc = [1, 10, 100, 1000]\n",
+ "\n",
+ "# User-defined function\n",
+ "def bounded_sum(values, bound=500):\n",
+ " return min(sum(values), bound)\n",
+ "\n",
+ "small_sum = pc | beam.CombineGlobally(bounded_sum) # [500]\n",
+ "large_sum = pc | beam.CombineGlobally(bounded_sum, bound=5000) # [1111]\n",
+ "\n",
+ "print(small_sum, large_sum)"
+ ],
+ "metadata": {
+ "id": "UZTWBGZ0TQWF"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2. Transforms: ParDo and Combine\n",
+ "\n",
+ "A `ParDo` transform considers each element in the input `PCollection`, performs your user code to process each element, and emits zero, one, or multiple elements to an output `PCollection`. `Combine` is another Beam transform for combining collections of elements or values in your data.\n",
+ "Both allow flexible UDF to define how you process the data."
Review Comment:
Done.
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