amotley commented on code in PR #27284:
URL: https://github.com/apache/beam/pull/27284#discussion_r1247127090
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,646 @@
+{
+ "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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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": 134,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> pip install --quiet apache-beam\n",
+ "\n",
+ ">> mkdir -p data\n",
+ "\n",
+ ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt\n",
+ "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\r\n",
+ "/ [1 files][153.6 KiB/153.6 KiB]
\r\n",
+ "Operation completed over 1 objects/153.6 KiB.
\r\n",
+ "\n",
+ ">> gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv\n",
+ "Copying
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection...\r\n",
+ "/ [1 files][466.7 KiB/466.7 KiB]
\r\n",
+ "Operation completed over 1 objects/466.7 KiB.
\r\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": 135,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/kinglear.txt\n",
+ "\tKING LEAR\r\n",
+ "\r\n",
+ "\r\n",
+ "\tDRAMATIS PERSONAE\r\n",
+ "\r\n",
+ "\r\n",
+ "LEAR\tking of Britain (KING LEAR:)\r\n",
+ "\r\n",
+ "KING OF FRANCE:\r\n",
+ "\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 136,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/SMSSpamCollection.csv\n",
+ "ham\tGo until jurong point, crazy.. Available only in bugis n great
world la e buffet... Cine there got amore wat...\r\n",
+ "ham\tOk lar... Joking wif u oni...\r\n",
+ "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May
2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply
08452810075over18's\r\n",
+ "ham\tU dun say so early hor... U c already then say...\r\n",
+ "ham\tNah I don't think he goes to usf, he lives around here though\r\n",
+ "spam\tFreeMsg Hey there darling it's been 3 week's now and no word
back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50
to rcv\r\n",
+ "ham\tEven my brother is not like to speak with me. They treat me like
aids patent.\r\n",
+ "ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu
Vettam)' has been set as your callertune for all Callers. Press *9 to copy your
friends Callertune\r\n",
+ "spam\tWINNER!! As a valued network customer you have been selected to
receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid
12 hours only.\r\n",
+ "spam\tHad your mobile 11 months or more? U R entitled to Update to the
latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on
08002986030\r\n",
+ "\n",
+ ">> wc -l data/SMSSpamCollection.csv\n",
+ "5574 data/SMSSpamCollection.csv\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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."
Review Comment:
```suggestion
"To start, we'll create a `YAML` file specifying our pipeline."
```
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,646 @@
+{
+ "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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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": 134,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> pip install --quiet apache-beam\n",
+ "\n",
+ ">> mkdir -p data\n",
+ "\n",
+ ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt\n",
+ "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\r\n",
+ "/ [1 files][153.6 KiB/153.6 KiB]
\r\n",
+ "Operation completed over 1 objects/153.6 KiB.
\r\n",
+ "\n",
+ ">> gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv\n",
+ "Copying
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection...\r\n",
+ "/ [1 files][466.7 KiB/466.7 KiB]
\r\n",
+ "Operation completed over 1 objects/466.7 KiB.
\r\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": 135,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/kinglear.txt\n",
+ "\tKING LEAR\r\n",
+ "\r\n",
+ "\r\n",
+ "\tDRAMATIS PERSONAE\r\n",
+ "\r\n",
+ "\r\n",
+ "LEAR\tking of Britain (KING LEAR:)\r\n",
+ "\r\n",
+ "KING OF FRANCE:\r\n",
+ "\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 136,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/SMSSpamCollection.csv\n",
+ "ham\tGo until jurong point, crazy.. Available only in bugis n great
world la e buffet... Cine there got amore wat...\r\n",
+ "ham\tOk lar... Joking wif u oni...\r\n",
+ "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May
2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply
08452810075over18's\r\n",
+ "ham\tU dun say so early hor... U c already then say...\r\n",
+ "ham\tNah I don't think he goes to usf, he lives around here though\r\n",
+ "spam\tFreeMsg Hey there darling it's been 3 week's now and no word
back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50
to rcv\r\n",
+ "ham\tEven my brother is not like to speak with me. They treat me like
aids patent.\r\n",
+ "ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu
Vettam)' has been set as your callertune for all Callers. Press *9 to copy your
friends Callertune\r\n",
+ "spam\tWINNER!! As a valued network customer you have been selected to
receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid
12 hours only.\r\n",
+ "spam\tHad your mobile 11 months or more? U R entitled to Update to the
latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on
08002986030\r\n",
+ "\n",
+ ">> wc -l data/SMSSpamCollection.csv\n",
+ "5574 data/SMSSpamCollection.csv\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 153,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " type: chain\n",
+ " transforms:\n",
+ "\n",
+ " # Read input data. Each line from the csv file is a String.\n",
+ " - type: ReadFromText\n",
+ " name: InputText\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",
+ " - type: PyFlatMap\n",
+ " name: FindWords\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",
+ " fn: 'lambda word: (word, 1)'\n",
+ "\n",
+ " # Using SumPerKey transform, we'll calculate the occurrence of each
word.\n",
+ " - type: SumPerKey\n",
+ " name: GroupAndSum\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",
+ " 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",
+ " 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": [
+ "Let's tun the pipeline executing the Python script with the pipeline file
as an argument:"
Review Comment:
```suggestion
"Let's run the pipeline executing the Python script with the pipeline
file as an argument:"
```
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,646 @@
+{
+ "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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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": 134,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> pip install --quiet apache-beam\n",
+ "\n",
+ ">> mkdir -p data\n",
+ "\n",
+ ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt\n",
+ "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\r\n",
+ "/ [1 files][153.6 KiB/153.6 KiB]
\r\n",
+ "Operation completed over 1 objects/153.6 KiB.
\r\n",
+ "\n",
+ ">> gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv\n",
+ "Copying
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection...\r\n",
+ "/ [1 files][466.7 KiB/466.7 KiB]
\r\n",
+ "Operation completed over 1 objects/466.7 KiB.
\r\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": 135,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/kinglear.txt\n",
+ "\tKING LEAR\r\n",
+ "\r\n",
+ "\r\n",
+ "\tDRAMATIS PERSONAE\r\n",
+ "\r\n",
+ "\r\n",
+ "LEAR\tking of Britain (KING LEAR:)\r\n",
+ "\r\n",
+ "KING OF FRANCE:\r\n",
+ "\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 136,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/SMSSpamCollection.csv\n",
+ "ham\tGo until jurong point, crazy.. Available only in bugis n great
world la e buffet... Cine there got amore wat...\r\n",
+ "ham\tOk lar... Joking wif u oni...\r\n",
+ "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May
2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply
08452810075over18's\r\n",
+ "ham\tU dun say so early hor... U c already then say...\r\n",
+ "ham\tNah I don't think he goes to usf, he lives around here though\r\n",
+ "spam\tFreeMsg Hey there darling it's been 3 week's now and no word
back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50
to rcv\r\n",
+ "ham\tEven my brother is not like to speak with me. They treat me like
aids patent.\r\n",
+ "ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu
Vettam)' has been set as your callertune for all Callers. Press *9 to copy your
friends Callertune\r\n",
+ "spam\tWINNER!! As a valued network customer you have been selected to
receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid
12 hours only.\r\n",
+ "spam\tHad your mobile 11 months or more? U R entitled to Update to the
latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on
08002986030\r\n",
+ "\n",
+ ">> wc -l data/SMSSpamCollection.csv\n",
+ "5574 data/SMSSpamCollection.csv\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 153,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " type: chain\n",
+ " transforms:\n",
+ "\n",
+ " # Read input data. Each line from the csv file is a String.\n",
+ " - type: ReadFromText\n",
+ " name: InputText\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",
+ " - type: PyFlatMap\n",
+ " name: FindWords\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",
+ " fn: 'lambda word: (word, 1)'\n",
+ "\n",
+ " # Using SumPerKey transform, we'll calculate the occurrence of each
word.\n",
+ " - type: SumPerKey\n",
+ " name: GroupAndSum\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",
+ " 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",
+ " 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": [
+ "Let's tun the pipeline executing the Python script with the pipeline file
as an argument:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> python -m apache_beam.yaml.main
--pipeline_spec_file=pipeline-01.yaml\n",
+ "Building pipeline...\r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"InputText\" at line 7
\r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"FindWords\" at line 12
\r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"PairWordsWith1\" at
line 19 \r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"GroupAndSum\" at line
24 \r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"FormatResults\" at
line 28 \r\n",
+ "INFO:apache_beam.yaml.yaml_transform:Expanding \"SaveToText\" at line
33 \r\n",
+ "Running pipeline...\r\n",
+ "WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in
target path matching: -*-of-%(num_shards)05d\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "run('python -m apache_beam.yaml.main
--pipeline_spec_file=pipeline-01.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's inspect the data. Each line contains a word and an associated
count."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/result-pipeline-01-00000-of-00001.txt\n",
+ "KING: 243\r\n",
+ "LEAR: 236\r\n",
+ "DRAMATIS: 1\r\n",
+ "PERSONAE: 1\r\n",
+ "king: 65\r\n",
+ "of: 447\r\n",
+ "Britain: 2\r\n",
+ "OF: 15\r\n",
+ "FRANCE: 10\r\n",
+ "DUKE: 3\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "run('head data/result-pipeline-01-00000-of-00001.txt')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Example 2: load data, filter unwanted lines and save results to a text
file.\n",
+ "In this example, we'll create a pipeline which loads the data, filters
out valid messages leaving spam, and saves only valid lines to a file.\n",
+ "Please note that this time we didn't specify the top-level type as
`chain`. This allows for more flexibility when creating a pipeline, but
requires us to specify the `input` explicitly for each transform.\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ "\n",
+ " - type: ReadFromText\n",
+ " name: SmsData\n",
+ " file_pattern: data/SMSSpamCollection.csv\n",
+ "\n",
+ " - type: PyMap\n",
+ " name: SplitLine\n",
+ " input: SmsData\n",
+ " fn: 'lambda line: line.split(\"\\\\t\")'\n",
+ "\n",
+ " - type: PyFilter\n",
+ " name: KeepSpam\n",
+ " input: SplitLine\n",
+ " keep: 'lambda row: row[0] == \"spam\"'\n",
+ "\n",
+ " - type: WriteToText\n",
+ " name: SaveToText\n",
+ " input: KeepSpam\n",
+ " file_path_prefix: \"data/result-pipeline-01\"\n",
+ " file_name_suffix: \".txt\"\n",
+ "'''\n",
+ "save_to_file(pipeline, 'pipeline-02.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Since this pipeline is linear, we can rewrite it to a `chain` and drop
the `input`s in transforms."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " type: chain\n",
+ " transforms:\n",
+ " - type: ReadFromText\n",
+ " name: SmsData\n",
+ " file_pattern: data/SMSSpamCollection.csv\n",
+ "\n",
+ " - type: PyMap\n",
+ " name: SplitLine\n",
+ " fn: 'lambda line: line.split(\"\\\\t\")'\n",
+ "\n",
+ " - type: PyFilter\n",
+ " name: KeepSpam\n",
+ " keep: 'lambda row: row[0] == \"spam\"'\n",
+ "\n",
+ " - type: WriteToText\n",
+ " name: SaveToText\n",
+ " file_path_prefix: \"data/result-pipeline-02\"\n",
+ " file_name_suffix: \".txt\"\n",
+ "'''\n",
+ "save_to_file(pipeline, 'pipeline-02-chain.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "To run the pipeline locally, using a DirectRunner, you need to run the
yaml's main python script, passing the `pipeline-02-chain.yaml` (or
`pipeline-02.yaml`) file as an input:"
Review Comment:
```suggestion
"To run the pipeline locally, using a DirectRunner, you need to run the
YAML's main python script, passing the `pipeline-02-chain.yaml` (or
`pipeline-02.yaml`) file as an input:"
```
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,424 @@
+{
+ "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 often still has too high a 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 for some (though
https://beam.apache.org/blog/beam-starter-projects/ has gone a long way in
making this easier).\n",
+ "\n",
+ "Here we provide a simple declarative syntax for describing pipelines that
does not require coding experience or learning how to use an SDK—any text
editor will do. Some installation may be required to actually *execute* a
pipeline, but we envision various services (such as Dataflow) to accept yaml
pipelines directly obviating the need for even that in the future. We also
anticipate the ability to generate code directly from these higher-level yaml
descriptions, should one want to graduate to a full Beam SDK (and possibly the
other direction as well as far as possible).\n",
+ "\n",
+ "In this notebook, we set up your development environment and write a
simple pipeline using Yaml API. 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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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 file into the local file system.\n",
+ "run('mkdir -p data')\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",
+ "Let’s see how our data looks like."
+ ],
+ "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": [
+ "## First pipeline\n",
+ "We’ll start with creating a pipeline which loads the data, filters out
valid messages leaving spam, and saves only valid lines to a file.\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " - type: ReadFromText\n",
+ " name: SmsData\n",
+ " file_pattern: data/SMSSpamCollection.csv\n",
+ "\n",
+ " - type: PyMap\n",
+ " name: SplitLine\n",
+ " input: SmsData\n",
+ " fn: 'lambda line: line.split(\"\\\\t\")'\n",
+ "\n",
+ " - type: PyFilter\n",
+ " name: KeepSpam\n",
+ " input: SplitLine\n",
+ " keep: 'lambda row: row[0] == \"spam\"'\n",
+ "\n",
+ " - type: WriteToText\n",
+ " name: SaveToText\n",
+ " input: KeepSpam\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": [
+ "In this example, each transformation contains the 'input' key, but if the
pipeline is linear, such as ours, we can let the inputs be implicit by
designating the pipeline as a `chain` type.\n"
Review Comment:
I would rather keep the inputs and remove "chain" entirely since it's more
advanced syntax.
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,646 @@
+{
+ "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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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": 134,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> pip install --quiet apache-beam\n",
+ "\n",
+ ">> mkdir -p data\n",
+ "\n",
+ ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt\n",
+ "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\r\n",
+ "/ [1 files][153.6 KiB/153.6 KiB]
\r\n",
+ "Operation completed over 1 objects/153.6 KiB.
\r\n",
+ "\n",
+ ">> gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv\n",
+ "Copying
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection...\r\n",
+ "/ [1 files][466.7 KiB/466.7 KiB]
\r\n",
+ "Operation completed over 1 objects/466.7 KiB.
\r\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": 135,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/kinglear.txt\n",
+ "\tKING LEAR\r\n",
+ "\r\n",
+ "\r\n",
+ "\tDRAMATIS PERSONAE\r\n",
+ "\r\n",
+ "\r\n",
+ "LEAR\tking of Britain (KING LEAR:)\r\n",
+ "\r\n",
+ "KING OF FRANCE:\r\n",
+ "\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 136,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/SMSSpamCollection.csv\n",
+ "ham\tGo until jurong point, crazy.. Available only in bugis n great
world la e buffet... Cine there got amore wat...\r\n",
+ "ham\tOk lar... Joking wif u oni...\r\n",
+ "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May
2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply
08452810075over18's\r\n",
+ "ham\tU dun say so early hor... U c already then say...\r\n",
+ "ham\tNah I don't think he goes to usf, he lives around here though\r\n",
+ "spam\tFreeMsg Hey there darling it's been 3 week's now and no word
back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50
to rcv\r\n",
+ "ham\tEven my brother is not like to speak with me. They treat me like
aids patent.\r\n",
+ "ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu
Vettam)' has been set as your callertune for all Callers. Press *9 to copy your
friends Callertune\r\n",
+ "spam\tWINNER!! As a valued network customer you have been selected to
receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid
12 hours only.\r\n",
+ "spam\tHad your mobile 11 months or more? U R entitled to Update to the
latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on
08002986030\r\n",
+ "\n",
+ ">> wc -l data/SMSSpamCollection.csv\n",
+ "5574 data/SMSSpamCollection.csv\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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",
Review Comment:
Here it may be good to do really basic syntax introduction, similar to
"structure" section in https://s.apache.org/beam-yaml-pipelines-syntax
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,646 @@
+{
+ "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 a text file from Cloud Storage to your local file
system. We are using this file to test your pipeline."
+ ]
+ },
+ {
+ "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": 134,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> pip install --quiet apache-beam\n",
+ "\n",
+ ">> mkdir -p data\n",
+ "\n",
+ ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt
data/kinglear.txt\n",
+ "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\r\n",
+ "/ [1 files][153.6 KiB/153.6 KiB]
\r\n",
+ "Operation completed over 1 objects/153.6 KiB.
\r\n",
+ "\n",
+ ">> gsutil cp
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection
data/SMSSpamCollection.csv\n",
+ "Copying
gs://apache-beam-samples/SMSSpamCollection/SMSSpamCollection...\r\n",
+ "/ [1 files][466.7 KiB/466.7 KiB]
\r\n",
+ "Operation completed over 1 objects/466.7 KiB.
\r\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "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": 135,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/kinglear.txt\n",
+ "\tKING LEAR\r\n",
+ "\r\n",
+ "\r\n",
+ "\tDRAMATIS PERSONAE\r\n",
+ "\r\n",
+ "\r\n",
+ "LEAR\tking of Britain (KING LEAR:)\r\n",
+ "\r\n",
+ "KING OF FRANCE:\r\n",
+ "\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": 136,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ ">> head data/SMSSpamCollection.csv\n",
+ "ham\tGo until jurong point, crazy.. Available only in bugis n great
world la e buffet... Cine there got amore wat...\r\n",
+ "ham\tOk lar... Joking wif u oni...\r\n",
+ "spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May
2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply
08452810075over18's\r\n",
+ "ham\tU dun say so early hor... U c already then say...\r\n",
+ "ham\tNah I don't think he goes to usf, he lives around here though\r\n",
+ "spam\tFreeMsg Hey there darling it's been 3 week's now and no word
back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50
to rcv\r\n",
+ "ham\tEven my brother is not like to speak with me. They treat me like
aids patent.\r\n",
+ "ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu
Vettam)' has been set as your callertune for all Callers. Press *9 to copy your
friends Callertune\r\n",
+ "spam\tWINNER!! As a valued network customer you have been selected to
receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid
12 hours only.\r\n",
+ "spam\tHad your mobile 11 months or more? U R entitled to Update to the
latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on
08002986030\r\n",
+ "\n",
+ ">> wc -l data/SMSSpamCollection.csv\n",
+ "5574 data/SMSSpamCollection.csv\r\n",
+ "\n"
+ ]
+ }
+ ],
+ "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."
Review Comment:
Or maybe .yaml?
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