bzablocki commented on code in PR #27284:
URL: https://github.com/apache/beam/pull/27284#discussion_r1246486332
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,424 @@
+{
Review Comment:
That would be great! Is this something @robertwb can arrange?
##########
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:
In the current implementation we need to specify `type: chain` if we don't
want to pass the `input` parameter to every transform. So we have 2 options:
1) pipeline with 'input' specification in every transform:
```
pipeline:
- type: ReadFromText
name: InputText
file_pattern: data/kinglear.txt
- type: PyFlatMap
name: FindWords
input: InputText
fn: |
import re
lambda line: re.findall(r"[a-zA-Z]+", line)
- type: PyMap
name: PairWordsWith1
input: FindWords
fn: 'lambda word: (word, 1)'
```
2) Specify 'type: chain' and specify transforms under the 'transforms' key
```
pipeline:
type: chain
transforms:
- type: ReadFromText
name: InputText
file_pattern: data/kinglear.txt
- type: PyFlatMap
name: FindWords
fn: |
import re
lambda line: re.findall(r"[a-zA-Z]+", line)
- type: PyMap
name: PairWordsWith1
fn: 'lambda word: (word, 1)'
```
3) The third option is to change the implementation to default to 'type:
chain' when there is no type specified, but that is for another PR. I imagine
it would look like this:
```
pipeline:
- type: ReadFromText
name: InputText
file_pattern: data/kinglear.txt
- type: PyFlatMap
name: FindWords
fn: |
import re
lambda line: re.findall(r"[a-zA-Z]+", line)
- type: PyMap
name: PairWordsWith1
fn: 'lambda word: (word, 1)'
```
What do you think @amotley and @svetakvsundhar ?
##########
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 rewrote the text around the `type:chain`, please take another look.
##########
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"
+ ],
+ "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-01\"\n",
+ " file_name_suffix: \".txt\"\n",
+ "'''\n",
+ "save_to_file(pipeline, 'pipeline-01-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-01-chain.yaml` (or
`pipeline-01.yaml`) file as an input:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('python -m apache_beam.yaml.main
--pipeline_spec_file=pipeline-01-chain.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's verify the results and see the content of the output file."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('head data/result-pipeline-01-00000-of-00001.txt')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "If everything went well, you should see only spam messages from our input
dataset. Congratulations, onto the next example!\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Count words in spam messages, select top 10 popular words and write
results to a file\n",
+ "\n",
+ "We'd like to write a pipeline which counts words occurring in spam
messages, selects the most popular ones and writes the result to a file.\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "pipeline = '''\n",
+ "pipeline:\n",
+ " type: chain\n",
+ "\n",
+ " transforms:\n",
+ " # Read input data. Each line from the csv file is a String.\n",
+ " - type: ReadFromText\n",
+ " name: SmsData\n",
+ " file_pattern: data/SMSSpamCollection.csv\n",
+ "\n",
+ " # Split each line into an array, where the first element is message
label (ham or spam) and the second is the content of the message.\n",
+ " - type: PyMap\n",
+ " name: SplitLine\n",
+ " fn: 'lambda line: line.split(\"\\\\t\")'\n",
+ "\n",
+ " # Select only the rows that contain spam messages, based on the
label.\n",
+ " - type: PyFilter\n",
+ " name: SpamMessages\n",
+ " keep: 'lambda row: row[0] == \"spam\"'\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[1])\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: PairLoweredWordsWith1\n",
+ " fn: 'lambda word: (word.lower(), 1)'\n",
+ "\n",
+ " # Using SumPerKey transform, we'll calculate the occurence of each
word.\n",
+ " - type: SumPerKey\n",
+ " name: GroupAndSum\n",
+ "\n",
+ " # Select 10 most popular words. Input format to this step is a tuple
(word: count),\n",
+ " # so we provide the count (row[1]) as the key to compare the
numbers\n",
+ " - type: TopNLargest\n",
+ " name: Largest\n",
+ " n: 10\n",
+ " key: 'lambda row: row[1]'\n",
+ "\n",
+ " # Save results to a text file.\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.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's run the pipeline:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('python -m apache_beam.yaml.main
--pipeline_spec_file=pipeline-02.yaml')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "To view the output:"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "run('head data/result-pipeline-02-00000-of-00001.txt')"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Summary\n",
Review Comment:
Sure, that's a good idea. It should be easy easy to do
##########
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."
Review Comment:
Do we have any already? Or should I leave a todo note here?
##########
examples/notebooks/get-started/try-apache-beam-yaml.ipynb:
##########
@@ -0,0 +1,424 @@
+{
Review Comment:
Good idea. I'll reach out to rszper@.
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