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Colab Notebook for Unit Tests in Beam (#32336)
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* Update unittests_in_beam.ipynb

* Created using Colab

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* Update examples/notebooks/blog/unittests_in_beam.ipynb

Co-authored-by: Danny McCormick <dannymccormick@google.com>

* Created using Colab

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Co-authored-by: Danny McCormick <dannymccormick@google.com>
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyM16129G+tIfKxNIGenSDL1",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/blog/unittests_in_beam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7DSE6TgWy7PP"
},
"outputs": [],
"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"
]
},
{
"cell_type": "code",
"source": [
"# Install the Apache Beam library\n",
"!pip install apache_beam[gcp] --quiet"
],
"metadata": {
"id": "5W2nuV7uzlPg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"***Notebook Description***\n",
"\n",
"This colab notebook is referenced in the Unit Testing in Beam blog post, an opinionated guide on authoring unit tests for Apache Beam pipelines. Note that this code is used solely for conveying the best practices of unit testing, and thus is not intended to be used out of the box in user pipelines. The code references files native in colab, and thus executing the cells in order will provide the intended output.\n",
"\n",
"\n",
"***Note: Running the cells does not invoke the tests written in this notebook. These could be run in a local IDE using [pytest](https://docs.pytest.org/en/stable/).***"
],
"metadata": {
"id": "_KqwMLN9kRXf"
}
},
{
"cell_type": "markdown",
"source": [
"**Example 1**\n",
"\n",
"This `DoFn` (and corresponding pipeline) is used to convey a situation in which a `DoFn` makes an API call. Note that an error is raised here if the length of the API response (returned_record) is less than length 10."
],
"metadata": {
"id": "Z8__izORM3r8"
}
},
{
"cell_type": "code",
"source": [
"# Fake client to simulate an external call\n",
"\n",
"import time\n",
"class Client():\n",
" def get_data(self, api):\n",
" time.sleep(3)\n",
" return [0,1,2,3,4,5,6,7,8,9]\n",
"\n",
"MyApiCall = Client()"
],
"metadata": {
"id": "GGPF7cY3Ntyj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#The following packages are used to run the example pipelines\n",
"\n",
"import apache_beam as beam\n",
"from apache_beam.io import ReadFromText, WriteToText\n",
"from apache_beam.options.pipeline_options import PipelineOptions\n",
"\n",
"class MyDoFn(beam.DoFn):\n",
" def process(self,element):\n",
" returned_record = MyApiCall.get_data(\"http://my-api-call.com\")\n",
" if len(returned_record)!=10:\n",
" raise ValueError(\"Length of record does not match expected length\")\n",
" yield returned_record\n",
"\n",
"with beam.Pipeline() as p:\n",
" result = (\n",
" p\n",
" | ReadFromText(\"/content/sample_data/anscombe.json\")\n",
" | beam.ParDo(MyDoFn())\n",
" | WriteToText(\"/content/example1\")\n",
" )"
],
"metadata": {
"id": "Ktk9EVIFzGfP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Mocking Example**\n",
"\n",
"The following blocks of code illustrate how we can mock an API response, to test out the error message we've written. Note that we can use mocking to avoid making the actual API call in our test."
],
"metadata": {
"id": "58GVMyMa2PwE"
}
},
{
"cell_type": "code",
"source": [
"!pip install mock # Install the 'mock' module"
],
"metadata": {
"id": "ESclJ_G-6JcW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# We import the mock package for mocking functionality.\n",
"from unittest.mock import Mock,patch\n",
"# from MyApiCall import get_data\n",
"import mock\n",
"\n",
"\n",
"# MyApiCall is a function that calls get_data to fetch some data via an API call.\n",
"@patch('MyApiCall.get_data')\n",
"def test_error_message_wrong_length(self, mock_get_data):\n",
" response = ['field1','field2']\n",
" mock_get_data.return_value = Mock()\n",
" mock_get_data.return_value.json.return_value=response\n",
"\n",
" input_elements = ['-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000'] #input length 9\n",
" with self.assertRaisesRegex(ValueError,\n",
" \"Length of record does not match expected length'\"):\n",
" p = beam.Pipeline()\n",
" result = p | beam.create(input_elements) | beam.ParDo(MyDoFn())\n",
" result\n"
],
"metadata": {
"id": "IRuv8s8a2O8F"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Example 2**\n",
"\n",
"The following example shows how we can use the `Map` construct to calculate median house value per bedroom.\n"
],
"metadata": {
"id": "IVjBkewt1sLA"
}
},
{
"cell_type": "code",
"source": [
"# The following code computes the median house value per bedroom\n",
"def median_house_value_per_bedroom(element):\n",
" # median_house_value is at index 8 and total_bedrooms is at index 4\n",
" element = element.strip().split(',')\n",
" return float(element[8])/float(element[4])\n",
"\n",
"\n",
"with beam.Pipeline() as p2:\n",
" result = (\n",
" p2\n",
" | ReadFromText(\"/content/sample_data/california_housing_test.csv\",skip_header_lines=1)\n",
" | beam.Map(median_house_value_per_bedroom)\n",
" | WriteToText(\"/content/example2\")\n",
" )"
],
"metadata": {
"id": "8Y5bQXIyfhra"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Example 3**\n",
"\n",
"The following code is an extension of example 2, but with more complex pipeline logic. Thus, you will see that the `median_house_value_per_bedroom` function is now more complex, and involves writing to various keys."
],
"metadata": {
"id": "Mh3nZZ1_12sX"
}
},
{
"cell_type": "code",
"source": [
"import random\n",
"# The following code computes the median house value per bedroom\n",
"counter=-1 #define a counter globally\n",
"\n",
"\n",
"def median_house_value_per_bedroom(element):\n",
" # median_house_value is at index 8 and total_bedrooms is at index 4, all as part of they key \"1\".\n",
" global counter\n",
" element = element.strip().split(',')\n",
" # Create multiple keys based on different fields\n",
" keys = [1,2,3]\n",
" counter+=1\n",
" value = float(element[8]) / float(element[4]) # Calculate median house value per bedroom\n",
" return keys[counter%3],value\n",
"\n",
"def multiply_by_factor(element):\n",
" key,value=element\n",
" return (key,value*10)\n",
"\n",
"\n",
"with beam.Pipeline() as p3:\n",
" result = (\n",
" p3\n",
" | ReadFromText(\"/content/sample_data/california_housing_test.csv\",skip_header_lines=1)\n",
" | beam.Map(median_house_value_per_bedroom)\n",
" | beam.Map(multiply_by_factor)\n",
" | beam.CombinePerKey(sum)\n",
" | WriteToText(\"/content/example3\")\n",
" )\n"
],
"metadata": {
"id": "hmO1Chl51vPG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"The following is code that is abstracting the two `Map` constructs, and the `CombinePerKey` construct. Note that this best practice makes our complex pipeline easier to test."
],
"metadata": {
"id": "cIuk2vT9sqOZ"
}
},
{
"cell_type": "code",
"source": [
"def transform_data_set(pcoll):\n",
" return (pcoll\n",
" | beam.Map(median_house_value_per_bedroom)\n",
" | beam.Map(multiply_by_factor)\n",
" | beam.CombinePerKey(sum))\n",
"\n",
"# Define a new class that inherits from beam.PTransform\n",
"class MapAndCombineTransform(beam.PTransform):\n",
" def expand(self, pcoll):\n",
" return transform_data_set(pcoll)\n",
"\n",
"with beam.Pipeline() as p3:\n",
" result = (\n",
" p3\n",
" | ReadFromText(\"/content/sample_data/california_housing_test.csv\",skip_header_lines=1)\n",
" | MapAndCombineTransform() # Use the new PTransform class\n",
" | WriteToText(\"/content/example3\")\n",
" )"
],
"metadata": {
"id": "XchihrbEqtlR"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Unit Test for Pipeline 3**\n",
"\n",
"We've populated some sample records here, as well as set what we're expecting our expected value to be."
],
"metadata": {
"id": "uoNJLQl_15gj"
}
},
{
"cell_type": "code",
"source": [
"import unittest\n",
"import apache_beam as beam\n",
"from apache_beam.testing.test_pipeline import TestPipeline\n",
"from apache_beam.testing.util import assert_that, equal_to\n",
"\n",
"\n",
"class TestBeam(unittest.TestCase):\n",
"\n",
"# This test corresponds to example 3, and is written to confirm the pipeline works as intended.\n",
" def test_transform_data_set(self):\n",
" expected=[(1, 10570.185786231425), (2, 13.375337533753376), (3, 13.315649867374006)]\n",
" input_elements = [\n",
" '-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000',\n",
" '121.05,99.99,23.30,39.5,55.55,41.01,10,34,74.30,91.91',\n",
" '122.05,100.99,24.30,40.5,56.55,42.01,11,35,75.30,92.91',\n",
" '-120.05,39.37,29.00,4085.00,681.00,1557.00,626.00,6.8085,364700.00'\n",
" ]\n",
" with beam.Pipeline() as p3:\n",
" result = (\n",
" p3\n",
" | beam.Create(input_elements)\n",
" | beam.Map(MapAndCombineTransform())\n",
" )\n",
" assert_that(result,equal_to(expected))"
],
"metadata": {
"id": "BU9Eil-TrtpE"
},
"execution_count": null,
"outputs": []
}
]
}

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