[GitHub] [beam] bzablocki commented on a diff in pull request #27284: Yaml API: Day Zero tutorial notebook

["bzablocki (via GitHub)" <gi...@apache.org>]

bzablocki commented on code in PR #27284:
URL: https://github.com/apache/beam/pull/27284#discussion_r1284568439


##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -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&mdash;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:
   Raw jupyter will not highlight the `%%writefile` nor a String version, so I'd still opt for my solution, given it is better in Intellij and it doesn't matter for the raw Jupyter



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