bzablocki commented on code in PR #27284:
URL: https://github.com/apache/beam/pull/27284#discussion_r1329917400
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examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
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@@ -0,0 +1,582 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Try Apache Beam - Python",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [],
+ "toc_visible": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python2",
+ "display_name": "Python 2"
+ }
+ },
+ "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/get-started/try-apache-beam-yaml.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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."
+ ],
+ "outputs": [],
+ "metadata": {
+ "cellView": "form"
+ }
+ },
+ {
+ "metadata": {
+ "id": "lNKIMlEDZ_Vw",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Try Apache Beam - YAML\n",
+ "\n",
+ "While Beam provides powerful APIs for authoring sophisticated data
processing pipelines, it still has a high barrier for getting started and
authoring simple pipelines. Even setting up the environment, installing the
dependencies, and setting up the project can be a challenge.\n",
+ "\n",
+ "Here we provide a simple YAML syntax for describing pipelines that does
not require coding experience or learning how to use an SDK. You can use any
text editor.\n",
+ "\n",
+ "Please note: YAML API is still EXPERIMENTAL and subject to change.\n",
+ "\n",
+ "In this notebook, you set up your development environment and write a
simple pipeline using YAML. Then you run it locally, using the
[DirectRunner](https://beam.apache.org/documentation/runners/direct/). You can
explore other runners with the [Beam Capatibility
Matrix](https://beam.apache.org/documentation/runners/capability-matrix/).\n",
+ "\n",
+ "To navigate through different sections, use the table of contents. From
**View** drop-down list, select **Table of contents**.\n",
+ "\n",
+ "To run a code cell, click the **Run cell** button at the top left of the
cell, or select it and press **`Shift+Enter`**. Try modifying a code cell and
re-running it to see what happens.\n",
+ "\n",
+ "To learn more about Colab, see [Welcome to
Colaboratory!](https://colab.sandbox.google.com/notebooks/welcome.ipynb)."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Fz6KSQ13_3Rr",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Setup\n",
+ "\n",
+ "First, you need to set up your environment. The following code installs
`apache-beam` and downloads some text files from Cloud Storage to your local
file system. We'll use these text files as input to the pipelines."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "GOOk81Jj_yUy",
+ "colab_type": "code",
+ "outputId": "d283dfb2-4f51-4fec-816b-f57b0cb9b71c",
+ "colab": {
+ "base_uri": "https://localhost:8080/";,
+ "height": 170
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Run and print a shell command.\n",
+ "def run(cmd):\n",
+ " print('>> {}'.format(cmd))\n",
+ " !{cmd}\n",
+ " print('')\n",
+ "\n",
+ "def save_to_file(content, file_name):\n",
+ " with open(file_name, 'w') as f:\n",
+ " f.write(content)\n",
+ "\n",
+ "# Install apache-beam.\n",
+ "run('pip install --quiet apache-beam')\n",
+ "\n",
+ "# Copy the input files into the local file system.\n",
+ "run('mkdir -p data')\n",
+ "run('wget -O data/kinglear.txt
https://storage.googleapis.com/dataflow-samples/shakespeare/kinglear.txt')\n",
+ "run('wget -O data/SMSSpamCollection.csv
https://storage.googleapis.com/apache-beam-samples/SMSSpamCollection/SMSSpamCollection')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Inspect the data\n",
+ "We'll be working with two datasets. We'll use `kinglear.txt` for the
first example, and `SMSSpamCollection.csv` for the second and third
examples.\n",
+ "Let's first take a look at the `kinglear.txt` dataset."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('head data/kinglear.txt')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "This is just a `txt` file that contains lines of text.\n",
+ "Let's take a look at the other dataset."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('head data/SMSSpamCollection.csv')\n",
+ "run('wc -l data/SMSSpamCollection.csv')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "This dataset is a `csv` file that contains 5,574 rows of SMS messages
labeled either spam or not-spam (\"ham\"). Each row contains two columns
separated by a tab character:\n",
+ "1. `Column 1`: The label, either `ham` or `spam`\n",
+ "2. `Column 2`: The SMS message as raw text (type `string`)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Example 1: Word count\n",
+ "This example is a version of the
[WordCount](https://beam.apache.org/get-started/wordcount-example/)). It reads
lines of text from the input dataset `kinglear.txt` and counts the number of
times each word appears in the text.\n",
+ "To start, we'll create a `.yaml` file specifying our pipeline."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " # Read input data. Each line from the txt file is a String.\n",
+ " - type: ReadFromText\n",
+ " name: InputText\n",
+ " config:\n",
+ " file_pattern: data/kinglear.txt\n",
+ "\n",
+ " # Using a regex, we'll split the content of the message (one long
string) into words (list of strings).\n",
+ " # The 'fn' parameter accepts functions written in Python\n",
+ " - type: PyFlatMap\n",
+ " name: FindWords\n",
+ " input: InputText\n",
+ " config:\n",
+ " fn: |\n",
+ " import re\n",
+ " lambda line: re.findall(r\"[a-zA-Z]+\", line)\n",
+ "\n",
+ " # Transforming each word to lower case and combining it with a '1'.
Result of this step are pairs (word: 1).\n",
+ " - type: PyMap\n",
+ " name: PairWordsWith1\n",
+ " input: FindWords\n",
+ " config:\n",
+ " fn: 'lambda word: (word, 1)'\n",
+ "\n",
+ " # Using CombinePerKey transform with the 'sum' function as a combine
function,\n",
+ " # we'll calculate the occurrence of each word.\n",
+ " - type: CombinePerKey\n",
+ " config:\n",
+ " combine_fn: sum\n",
+ " name: GroupAndSum\n",
+ " input: PairWordsWith1\n",
+ "\n",
+ " # Format results - each record should be represented as 'word:
count'.\n",
+ " # The 'fn' parameter accepts functions written in Python\n",
+ " - type: PyMap\n",
+ " name: FormatResults\n",
+ " input: GroupAndSum\n",
+ " config:\n",
+ " fn: \"lambda word_count_tuple: f'{word_count_tuple[0]}:
{word_count_tuple[1]}'\"\n",
+ "\n",
+ " # Save results to a text file.\n",
+ " - type: WriteToText\n",
+ " name: SaveToText\n",
+ " input: FormatResults\n",
+ " config:\n",
+ " file_path_prefix: \"data/result-pipeline-01\"\n",
+ " file_name_suffix: \".txt\"\n",
+ "'''\n",
+ "save_to_file(pipeline, 'pipeline-01.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Each pipeline specification must start with a `pipeline` key on the first
line.\n",
+ "The `pipeline` keyh is followed by a list of transforms. For example, the
first transform reads the input file:\n",
+ "```\n",
+ " # Read input data. Each line from the csv file is a String.\n",
+ " - type: ReadFromText\n",
+ " name: InputText\n",
+ " config:\n",
+ " file_pattern: data/kinglear.txt\n",
+ "```\n",
+ "Note: The indentation is important, because it specifies object
hierarchy.\n",
+ "YAML supports comments. Everything after the `#` is always treated as a
comment. Use them to improve readability.\n",
+ "\n",
+ "Each operation must specify the `type` descriptor and other fields, such
as `name` and other transform-specific parameters.\n",
+ "For a list of available transforms and their parameters, see the YAML API
documentation. # todo(yaml) add link\n",
Review Comment:
Hi @amotley, this comment is also still open.
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