Polber commented on code in PR #27284:
URL: https://github.com/apache/beam/pull/27284#discussion_r1513143678
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examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
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@@ -0,0 +1,556 @@
+{
+ "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 an overwhelming amount of
boilerplate.\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—any text
editor will do.\n",
+ "\n",
+ "Please note: YAML API is still EXPERIMENTAL and subject to change.\n",
+ "\n",
+ "In this notebook, we set up your development environment and write a
simple pipeline using YAML. We'll 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, you can click the **Run cell** button at the top left
of the cell, or by 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, which includes installing
`apache-beam` and downloading files from Cloud Storage to your local file
system. We'll use these files as an input to the pipelines in this guide."
+ ]
+ },
+ {
+ "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('gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt')\n",
+ "run('gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Inspect the data\n",
+ "We'll be working with 2 datasets. We'll use `kinglear.txt` for the first
example - word count, and `SMSSpamCollection.csv` for the second and third.\n",
+ "Let's first take a loot 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 - it 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 with 5,574 rows and 2 columns recording the
following attributes separated by a tab sign:\n",
+ "1. `Column 1`: The label (either `ham` or `spam`)\n",
+ "2. `Column 2`: The SMS as raw text (type `string`)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Example 1: word count\n",
+ "In this popular introductory exercise, we will build a pipeline that
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",
Review Comment:
I'm not overly concerned with how it displays in IntelliJ (especially
considering the target audience is not using an IDE anyway), and I think the
lack of highlighting in raw Jupyter does not make it unclear what is going on,
and makes editing the yaml easier.
For reference, this is how it looks in raw Jupyter
I would opt for the inline function - WDYT @robertwb
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