tvalentyn commented on code in PR #26185:
URL: https://github.com/apache/beam/pull/26185#discussion_r1180718062
##########
examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb:
##########
@@ -0,0 +1,1836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "L7ZbRufePd2g"
+ },
+ "outputs": [],
+ "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."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "83TJhNxLD7-W"
+ },
+ "source": [
+ " # **Introduction to Windowing for Batch Processing in Apache
Beam**\n",
+ "\n",
+ "In this notebook, we will learn the fundamentals of **batch
processing** as we walk through a few introductory examples in Beam. The
pipelines in these examples process real-world data for air quality levels in
India between 2017 and 2022.\n",
+ "\n",
+ "After this tutorial you should have a basic understanding of the
following:\n",
+ "\n",
+ "* What is **batch vs. stream** data processing?\n",
+ "* How can I use Beam to run a **simple batch analysis job**?\n",
+ "* How can I use Beam's **windowing features** to process only
certain intervals of data at a time?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dj3ftRRqfumW"
+ },
+ "source": [
+ "To begin, run the following cell to set up Apache Beam."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "zmJ0pCmSvD0-",
+ "outputId": "9041f637-12a0-4f78-f60b-ebd3c3a1c186"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.5/14.5
MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
Review Comment:
nit: you could clean the outputs
##########
examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb:
##########
@@ -0,0 +1,1836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "L7ZbRufePd2g"
+ },
+ "outputs": [],
+ "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."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "83TJhNxLD7-W"
+ },
+ "source": [
+ " # **Introduction to Windowing for Batch Processing in Apache
Beam**\n",
+ "\n",
+ "In this notebook, we will learn the fundamentals of **batch
processing** as we walk through a few introductory examples in Beam. The
pipelines in these examples process real-world data for air quality levels in
India between 2017 and 2022.\n",
+ "\n",
+ "After this tutorial you should have a basic understanding of the
following:\n",
+ "\n",
+ "* What is **batch vs. stream** data processing?\n",
+ "* How can I use Beam to run a **simple batch analysis job**?\n",
+ "* How can I use Beam's **windowing features** to process only
certain intervals of data at a time?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dj3ftRRqfumW"
+ },
+ "source": [
+ "To begin, run the following cell to set up Apache Beam."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "zmJ0pCmSvD0-",
+ "outputId": "9041f637-12a0-4f78-f60b-ebd3c3a1c186"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.5/14.5
MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m
\u001b[32m144.1/144.1 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta
\u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.7/89.7
kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m515.5/515.5
kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6
MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.0/152.0
kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7
MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for crcmod (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for dill (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for docopt (setup.py) ...
\u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install apache-beam.\n",
+ "!pip install --quiet apache-beam"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "7sBoLahzPlJ1"
+ },
+ "outputs": [],
+ "source": [
+ "# Set the logging level to reduce verbose information\n",
+ "import logging\n",
+ "\n",
+ "logging.root.setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BB6FAwYj1dAi"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "<hr style=\"border: 1px solid #fdb515;\" />\n",
+ "\n",
+ "## Batch vs. Stream Data Processing\n",
+ "\n",
+ "What's the difference?\n",
+ "\n",
+ "**Batch processing** is when data processing and analysis happens on
a set of data that have already been stored over a period of time. \n",
+ "In other words, the input is a finite, bounded data set. Examples
include payroll and billing systems, which have to be processed weekly or
monthly.\n",
+ "\n",
+ "**Stream processing** happens *as* data flows through a system. This
results in analysis and reporting of events \n",
+ "within a short period of time or near real-time on an infinite,
unbounded data set. \n",
+ "Examples include fraud detection or intrusion detection, which
requires the continuous processing of transaction data.\n",
+ "\n",
+ "> This tutorial will focus on **batch processing** examples. \n",
+ "To learn more about stream processing in Beam, check out
[this](https://beam.apache.org/documentation/sdks/python-streaming/)."
Review Comment:
```suggestion
"To learn more about stream processing in Beam, check out the
[Python
Streaming](https://beam.apache.org/documentation/sdks/python-streaming/) page."
```
##########
examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb:
##########
@@ -0,0 +1,1836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "L7ZbRufePd2g"
+ },
+ "outputs": [],
+ "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."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "83TJhNxLD7-W"
+ },
+ "source": [
+ " # **Introduction to Windowing for Batch Processing in Apache
Beam**\n",
+ "\n",
+ "In this notebook, we will learn the fundamentals of **batch
processing** as we walk through a few introductory examples in Beam. The
pipelines in these examples process real-world data for air quality levels in
India between 2017 and 2022.\n",
+ "\n",
+ "After this tutorial you should have a basic understanding of the
following:\n",
+ "\n",
+ "* What is **batch vs. stream** data processing?\n",
+ "* How can I use Beam to run a **simple batch analysis job**?\n",
+ "* How can I use Beam's **windowing features** to process only
certain intervals of data at a time?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dj3ftRRqfumW"
+ },
+ "source": [
+ "To begin, run the following cell to set up Apache Beam."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "zmJ0pCmSvD0-",
+ "outputId": "9041f637-12a0-4f78-f60b-ebd3c3a1c186"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.5/14.5
MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m
\u001b[32m144.1/144.1 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta
\u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
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kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m515.5/515.5
kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
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MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.0/152.0
kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7
MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for crcmod (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for dill (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for docopt (setup.py) ...
\u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install apache-beam.\n",
+ "!pip install --quiet apache-beam"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "7sBoLahzPlJ1"
+ },
+ "outputs": [],
+ "source": [
+ "# Set the logging level to reduce verbose information\n",
+ "import logging\n",
+ "\n",
+ "logging.root.setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BB6FAwYj1dAi"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "<hr style=\"border: 1px solid #fdb515;\" />\n",
+ "\n",
+ "## Batch vs. Stream Data Processing\n",
+ "\n",
+ "What's the difference?\n",
+ "\n",
+ "**Batch processing** is when data processing and analysis happens on
a set of data that have already been stored over a period of time. \n",
+ "In other words, the input is a finite, bounded data set. Examples
include payroll and billing systems, which have to be processed weekly or
monthly.\n",
+ "\n",
+ "**Stream processing** happens *as* data flows through a system. This
results in analysis and reporting of events \n",
+ "within a short period of time or near real-time on an infinite,
unbounded data set. \n",
+ "Examples include fraud detection or intrusion detection, which
requires the continuous processing of transaction data.\n",
+ "\n",
+ "> This tutorial will focus on **batch processing** examples. \n",
+ "To learn more about stream processing in Beam, check out
[this](https://beam.apache.org/documentation/sdks/python-streaming/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W_63UtsoBRql"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "## Load the Data\n",
+ "\n",
+ "Let's import the example data we will be using throughout this
tutorial. The
[dataset](https://www.kaggle.com/datasets/fedesoriano/air-quality-data-in-india)
consists of **hourly air quality data (PM 2.5) in India from November 2017 to
June 2022**.\n",
+ "\n",
+ "> The World Health Organization (WHO) reports 7 million premature
deaths linked to air pollution each year. In India alone, more than 90% of the
country's population live in areas where air quality is below the WHO's
standards.\n",
+ "\n",
+ "**What does the data look like?**\n",
+ "\n",
+ "The data set has 36,192 rows and 6 columns in total recording the
following attributes:\n",
+ "\n",
+ "1. `Timestamp`: Timestamp in the format YYYY-MM-DD HH:MM:SS\n",
+ "2. `Year`: Year of the measure\n",
+ "3. `Month`: Month of the measure\n",
+ "4. `Day`: Day of the measure\n",
+ "5. `Hour`: Hour of the measure\n",
+ "6. `PM2.5`: Fine particulate matter air pollutant level in air\n",
+ "\n",
+ "**For our purposes, we will focus on only the first and last column
of the** `air_quality` **DataFrame**:\n",
+ "\n",
+ "1. `Timestamp`: Timestamp in the format YYYY-MM-DD HH:MM:SS\n",
+ "2. `PM 2.5`: Fine particulate matter air pollutant level in air\n",
+ "\n",
+ "Run the following cell to load the data into our file directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "GTteBUZ-7e2s",
+ "outputId": "3af9cdb0-c248-4c6d-96f6-c3739fb66014"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Copying gs://batch-processing-example/air-quality-india.csv...\n",
+ "/ [1 files][ 1.4 MiB/ 1.4 MiB]
\n",
+ "Operation completed over 1 objects/1.4 MiB.
\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Copy the dataset file into the local file system from Google Cloud
Storage.\n",
+ "!mkdir -p data\n",
+ "!gsutil cp gs://batch-processing-example/air-quality-india.csv data/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1NcmPl7C43lY"
+ },
+ "source": [
+ "#### Data Preparation\n",
+ "\n",
+ "Before we load the data into a Beam pipeline, let's use Pandas to
select two columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";,
+ "height": 206
+ },
+ "id": "dq-k7hwRf4MA",
+ "outputId": "7d70a959-5278-453e-9315-f5ed06821744"
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Timestamp</th>\n",
+ " <th>Year</th>\n",
+ " <th>Month</th>\n",
+ " <th>Day</th>\n",
+ " <th>Hour</th>\n",
+ " <th>PM2.5</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2017-11-07 12:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>12</td>\n",
+ " <td>64.51</td>\n",
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+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2017-11-07 13:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>13</td>\n",
+ " <td>69.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2017-11-07 14:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>14</td>\n",
+ " <td>92.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2017-11-07 15:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>15</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2017-11-07 16:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
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+ " google.colab.kernel.accessAllowed ? 'block' :
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+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element =
document.querySelector('#df-ca65f108-edf8-4e2b-8152-6df025dc7ff8');\n",
+ " const dataTable =\n",
+ " await
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+ " [key],
{});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the '
+\n",
+ " '<a target=\"_blank\"
href=https://colab.research.google.com/notebooks/data_table.ipynb>data table
notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable,
element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
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+ " </script>\n",
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+ " "
+ ],
+ "text/plain": [
+ " Timestamp Year Month Day Hour PM2.5\n",
+ "0 2017-11-07 12:00:00 2017 11 7 12 64.51\n",
+ "1 2017-11-07 13:00:00 2017 11 7 13 69.95\n",
+ "2 2017-11-07 14:00:00 2017 11 7 14 92.79\n",
+ "3 2017-11-07 15:00:00 2017 11 7 15 109.66\n",
+ "4 2017-11-07 16:00:00 2017 11 7 16 116.50"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the data into a Python Pandas DataFrame.\n",
+ "import pandas as pd\n",
+ "\n",
+ "air_quality = pd.read_csv(\"data/air-quality-india.csv\")\n",
+ "air_quality.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>PM2.5</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Timestamp</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-11-07 12:00:00</th>\n",
+ " <td>64.51</td>\n",
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+ " <tr>\n",
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+ " }\n",
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document.querySelector('#df-db4299d8-d441-4fc1-af7d-8d1c199492ed');\n",
+ " const dataTable =\n",
+ " await
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+ " [key],
{});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the '
+\n",
+ " '<a target=\"_blank\"
href=https://colab.research.google.com/notebooks/data_table.ipynb>data table
notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable,
element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ " PM2.5\n",
+ "Timestamp \n",
+ "2017-11-07 12:00:00 64.51\n",
+ "2017-11-07 13:00:00 69.95\n",
+ "2017-11-07 14:00:00 92.79\n",
+ "2017-11-07 15:00:00 109.66\n",
+ "2017-11-07 16:00:00 116.50"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import csv\n",
+ "\n",
+ "#Select only the two features of the DataFrame we're interested
in.\n",
+ "airq = air_quality.loc[:, [\"Timestamp\",
\"PM2.5\"]].set_index(\"Timestamp\")\n",
+ "saved_new = pd.DataFrame(airq)\n",
+ "saved_new.to_csv(\"data/air_quality.csv\")\n",
+ "airq.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VRFkb_sLDUCD"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "# 1. Average Air Quality Index (AQI)\n",
+ "\n",
+ "Before we explore more advanced batch processing with different types
of windowing, we will start with a simple batch analysis example.\n",
+ "\n",
+ "Our **objective** is to analyze the *entire* dataset to find the
**average PM2.5 air quality index** in India across the entire 11/2017-6/2022
period.\n",
+ "\n",
+ "> This examples uses the `GlobalWindow`, which is a single window
that covers the entire PCollection. All pipelines use the
[`GlobalWindow`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.window.html#apache_beam.transforms.window.GlobalWindow)
by default. In many cases, especially for batch pipelines, this is what we
want since we want to analyze all the data that we have."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";,
+ "height": 34
+ },
+ "id": "v06NFe9sDYXc",
+ "outputId": "f65eae63-0424-4ac0-8609-78e98ac21bd0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof
window.interactive_beam_jquery == 'undefined') {\n var jqueryScript =
document.createElement('script');\n jqueryScript.src =
'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n
jqueryScript.type = 'text/javascript';\n jqueryScript.onload =
function() {\n var datatableScript =
document.createElement('script');\n datatableScript.src =
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datatableScript.type = 'text/javascript';\n datatableScript.onload =
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document.head.appendChild(datatableScript);\n };\n
document.head.appendChild(jqueryScript);\n } else {\n
window.interactive
_beam_jquery(document).ready(function($){\n \n });\n
}"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "49.308428658266905\n"
+ ]
+ }
+ ],
+ "source": [
+ "import apache_beam as beam\n",
+ "\n",
+ "def parse_file(element):\n",
+ " file = csv.reader([element], quotechar='\"', delimiter=',',\n",
+ " quoting=csv.QUOTE_ALL, skipinitialspace=True)\n",
+ " for line in file:\n",
+ " return line\n",
+ "\n",
+ "with beam.Pipeline() as pipeline:\n",
+ " (\n",
+ " pipeline\n",
+ " | 'Read input file' >>
beam.io.ReadFromText(\"data/air_quality.csv\",\n",
+ "
skip_header_lines=1)\n",
+ " | 'Parse file' >> beam.Map(parse_file)\n",
+ " | 'Get PM' >> beam.Map(lambda x: float(x[1])) # only process
PM2.5\n",
+ " | 'Get mean value' >> beam.combiners.Mean.Globally()\n",
+ " | beam.Map(print))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "GmHEE1G5Y1z-",
+ "outputId": "248ee3d7-43af-4b53-9832-8da0eb7ac974"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "49.30842865826703"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# To verify, the above mean value matches what Pandas produces\n",
+ "airq[\"PM2.5\"].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b3gGxC6w6qXx"
+ },
+ "source": [
+ "**Congratulations!** You just created a simple aggregation processing
pipeline in batch using `GlobalWindow`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vRameihqDJ8l"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "# 2. Advanced Processing in Batch with Windowing\n",
+ "\n",
+ "Sometimes, we want to
[aggregate](https://beam.apache.org/documentation/transforms/python/overview/#aggregation)
data, like `GroupByKey` or `Combine`, only at certain intervals, like hourly
or daily, instead of processing the entire `PCollection` of data only once.\n",
+ "\n",
+ "In this case, our **objective** is to determine the **fluctuations of
air quality *every 30 days*.\n",
+ "\n",
+ "**_Windows_** in Beam allow us to process only certain data intervals
at a time.\n",
+ "In this notebook, we will go through different ways of windowing our
pipeline.\n",
+ "\n",
+ "We have already been introduced to the default GlobalWindow (see
above) that covers the entire PCollection. Now we will dive into **fixed time
windows, sliding time windows, and session windows**.\n",
+ "\n",
+ "> [Another windowing
tutorial](https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/tour-of-beam/windowing.ipynb)
with a toy dataset is recommended to go through."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gj0_S5Ka3-zb"
+ },
+ "source": [
+ "### First, we need to convert timestamps to Unix time\n",
+ "\n",
+ "Apache Beam requires us to provide the timestamp as Unix time. Let us
write the simple time conversion function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "nKBYsxFg4SIa"
+ },
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "# This function is modifiable and can convert integers to time
formats like unix\n",
+ "# Without this function and .strptime, you may run into comparison
issues!\n",
+ "def to_unix_time(time_str: str, time_format='%Y-%m-%d %H:%M:%S') ->
int:\n",
+ " \"\"\"Converts a time string into Unix time.\"\"\"\n",
+ " time_tuple = time.strptime(time_str, time_format)\n",
+ " return int(time.mktime(time_tuple))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "_mPge0KdRx20",
+ "outputId": "43475bbe-548a-4817-ed0b-534cebbe70ce"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1634220000"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to_unix_time('2021-10-14 14:00:00')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lL0_QONF1aMH"
+ },
+ "source": [
+ "### Second, let us define some helper functions to develop our
pipeline\n",
+ "\n",
+ "In this code, we have a transform (`PrintWindowInfo`) to help us
analyze an element alongside its window information, and we have another
transform\n",
+ "(`PrintWindowInfo`) to help us analyze how many elements landed into
each window.\n",
+ "We use a custom
[`DoFn`](https://beam.apache.org/documentation/transforms/python/elementwise/pardo)\n",
+ "to access that information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "KtPL-echb2xv"
+ },
+ "outputs": [],
+ "source": [
+ "#@title Helper functions to develop our pipeline\n",
+ "\n",
+ "def human_readable_window(window) -> str:\n",
+ " \"\"\"Formats a window object into a human readable
string.\"\"\"\n",
+ " if isinstance(window, beam.window.GlobalWindow):\n",
+ " return str(window)\n",
+ " return f'{window.start.to_utc_datetime()} -
{window.end.to_utc_datetime()}'\n",
+ "\n",
+ "class PrintElementInfo(beam.DoFn):\n",
+ " \"\"\"Prints an element with its Window information for
debugging.\"\"\"\n",
+ " def process(self, element, timestamp=beam.DoFn.TimestampParam,
window=beam.DoFn.WindowParam):\n",
+ " print(f'[{human_readable_window(window)}]
{timestamp.to_utc_datetime()} -- {element}')\n",
+ " yield element\n",
+ "\n",
+ "@beam.ptransform_fn\n",
+ "def PrintWindowInfo(pcollection):\n",
Review Comment:
beam.LogElements is supposed to be able to print elements and windwing info,
but might have some rough edges.. cc: @svetakvsundhar in case you are
interested to check if it works later. Doesn't have to block this.
##########
examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb:
##########
@@ -0,0 +1,1836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/learn_beam_windowing_by_doing.ipynb\";
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\"; alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cellView": "form",
+ "id": "L7ZbRufePd2g"
+ },
+ "outputs": [],
+ "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."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "83TJhNxLD7-W"
+ },
+ "source": [
+ " # **Introduction to Windowing for Batch Processing in Apache
Beam**\n",
+ "\n",
+ "In this notebook, we will learn the fundamentals of **batch
processing** as we walk through a few introductory examples in Beam. The
pipelines in these examples process real-world data for air quality levels in
India between 2017 and 2022.\n",
+ "\n",
+ "After this tutorial you should have a basic understanding of the
following:\n",
+ "\n",
+ "* What is **batch vs. stream** data processing?\n",
+ "* How can I use Beam to run a **simple batch analysis job**?\n",
+ "* How can I use Beam's **windowing features** to process only
certain intervals of data at a time?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dj3ftRRqfumW"
+ },
+ "source": [
+ "To begin, run the following cell to set up Apache Beam."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "zmJ0pCmSvD0-",
+ "outputId": "9041f637-12a0-4f78-f60b-ebd3c3a1c186"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.5/14.5
MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m
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\u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.7/89.7
kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m515.5/515.5
kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6
MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.0/152.0
kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K
\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7
MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for crcmod (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for dill (setup.py) ...
\u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for docopt (setup.py) ...
\u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install apache-beam.\n",
+ "!pip install --quiet apache-beam"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "7sBoLahzPlJ1"
+ },
+ "outputs": [],
+ "source": [
+ "# Set the logging level to reduce verbose information\n",
+ "import logging\n",
+ "\n",
+ "logging.root.setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BB6FAwYj1dAi"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "<hr style=\"border: 1px solid #fdb515;\" />\n",
+ "\n",
+ "## Batch vs. Stream Data Processing\n",
+ "\n",
+ "What's the difference?\n",
+ "\n",
+ "**Batch processing** is when data processing and analysis happens on
a set of data that have already been stored over a period of time. \n",
+ "In other words, the input is a finite, bounded data set. Examples
include payroll and billing systems, which have to be processed weekly or
monthly.\n",
+ "\n",
+ "**Stream processing** happens *as* data flows through a system. This
results in analysis and reporting of events \n",
+ "within a short period of time or near real-time on an infinite,
unbounded data set. \n",
+ "Examples include fraud detection or intrusion detection, which
requires the continuous processing of transaction data.\n",
+ "\n",
+ "> This tutorial will focus on **batch processing** examples. \n",
+ "To learn more about stream processing in Beam, check out
[this](https://beam.apache.org/documentation/sdks/python-streaming/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W_63UtsoBRql"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "## Load the Data\n",
+ "\n",
+ "Let's import the example data we will be using throughout this
tutorial. The
[dataset](https://www.kaggle.com/datasets/fedesoriano/air-quality-data-in-india)
consists of **hourly air quality data (PM 2.5) in India from November 2017 to
June 2022**.\n",
+ "\n",
+ "> The World Health Organization (WHO) reports 7 million premature
deaths linked to air pollution each year. In India alone, more than 90% of the
country's population live in areas where air quality is below the WHO's
standards.\n",
+ "\n",
+ "**What does the data look like?**\n",
+ "\n",
+ "The data set has 36,192 rows and 6 columns in total recording the
following attributes:\n",
+ "\n",
+ "1. `Timestamp`: Timestamp in the format YYYY-MM-DD HH:MM:SS\n",
+ "2. `Year`: Year of the measure\n",
+ "3. `Month`: Month of the measure\n",
+ "4. `Day`: Day of the measure\n",
+ "5. `Hour`: Hour of the measure\n",
+ "6. `PM2.5`: Fine particulate matter air pollutant level in air\n",
+ "\n",
+ "**For our purposes, we will focus on only the first and last column
of the** `air_quality` **DataFrame**:\n",
+ "\n",
+ "1. `Timestamp`: Timestamp in the format YYYY-MM-DD HH:MM:SS\n",
+ "2. `PM 2.5`: Fine particulate matter air pollutant level in air\n",
+ "\n",
+ "Run the following cell to load the data into our file directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "GTteBUZ-7e2s",
+ "outputId": "3af9cdb0-c248-4c6d-96f6-c3739fb66014"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Copying gs://batch-processing-example/air-quality-india.csv...\n",
+ "/ [1 files][ 1.4 MiB/ 1.4 MiB]
\n",
+ "Operation completed over 1 objects/1.4 MiB.
\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Copy the dataset file into the local file system from Google Cloud
Storage.\n",
+ "!mkdir -p data\n",
+ "!gsutil cp gs://batch-processing-example/air-quality-india.csv data/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1NcmPl7C43lY"
+ },
+ "source": [
+ "#### Data Preparation\n",
+ "\n",
+ "Before we load the data into a Beam pipeline, let's use Pandas to
select two columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";,
+ "height": 206
+ },
+ "id": "dq-k7hwRf4MA",
+ "outputId": "7d70a959-5278-453e-9315-f5ed06821744"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div id=\"df-ca65f108-edf8-4e2b-8152-6df025dc7ff8\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Timestamp</th>\n",
+ " <th>Year</th>\n",
+ " <th>Month</th>\n",
+ " <th>Day</th>\n",
+ " <th>Hour</th>\n",
+ " <th>PM2.5</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2017-11-07 12:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>12</td>\n",
+ " <td>64.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2017-11-07 13:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>13</td>\n",
+ " <td>69.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2017-11-07 14:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>14</td>\n",
+ " <td>92.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2017-11-07 15:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>15</td>\n",
+ " <td>109.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2017-11-07 16:00:00</td>\n",
+ " <td>2017</td>\n",
+ " <td>11</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>116.50</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\"
onclick=\"convertToInteractive('df-ca65f108-edf8-4e2b-8152-6df025dc7ff8')\"\n",
+ " title=\"Convert this dataframe to an interactive
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+ " const element =
document.querySelector('#df-ca65f108-edf8-4e2b-8152-6df025dc7ff8');\n",
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+ " await
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+ " [key],
{});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the '
+\n",
+ " '<a target=\"_blank\"
href=https://colab.research.google.com/notebooks/data_table.ipynb>data table
notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable,
element);\n",
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+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ " Timestamp Year Month Day Hour PM2.5\n",
+ "0 2017-11-07 12:00:00 2017 11 7 12 64.51\n",
+ "1 2017-11-07 13:00:00 2017 11 7 13 69.95\n",
+ "2 2017-11-07 14:00:00 2017 11 7 14 92.79\n",
+ "3 2017-11-07 15:00:00 2017 11 7 15 109.66\n",
+ "4 2017-11-07 16:00:00 2017 11 7 16 116.50"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the data into a Python Pandas DataFrame.\n",
+ "import pandas as pd\n",
+ "\n",
+ "air_quality = pd.read_csv(\"data/air-quality-india.csv\")\n",
+ "air_quality.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
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+ "height": 237
+ },
+ "id": "WNXrvP-wDIkA",
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+ " <th></th>\n",
+ " <th>PM2.5</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Timestamp</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2017-11-07 12:00:00</th>\n",
+ " <td>64.51</td>\n",
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+ " <tr>\n",
+ " <th>2017-11-07 13:00:00</th>\n",
+ " <td>69.95</td>\n",
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+ " <td>92.79</td>\n",
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+ " }\n",
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document.querySelector('#df-db4299d8-d441-4fc1-af7d-8d1c199492ed');\n",
+ " const dataTable =\n",
+ " await
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+ " [key],
{});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the '
+\n",
+ " '<a target=\"_blank\"
href=https://colab.research.google.com/notebooks/data_table.ipynb>data table
notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable,
element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ " PM2.5\n",
+ "Timestamp \n",
+ "2017-11-07 12:00:00 64.51\n",
+ "2017-11-07 13:00:00 69.95\n",
+ "2017-11-07 14:00:00 92.79\n",
+ "2017-11-07 15:00:00 109.66\n",
+ "2017-11-07 16:00:00 116.50"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import csv\n",
+ "\n",
+ "#Select only the two features of the DataFrame we're interested
in.\n",
+ "airq = air_quality.loc[:, [\"Timestamp\",
\"PM2.5\"]].set_index(\"Timestamp\")\n",
+ "saved_new = pd.DataFrame(airq)\n",
+ "saved_new.to_csv(\"data/air_quality.csv\")\n",
+ "airq.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VRFkb_sLDUCD"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "# 1. Average Air Quality Index (AQI)\n",
+ "\n",
+ "Before we explore more advanced batch processing with different types
of windowing, we will start with a simple batch analysis example.\n",
+ "\n",
+ "Our **objective** is to analyze the *entire* dataset to find the
**average PM2.5 air quality index** in India across the entire 11/2017-6/2022
period.\n",
+ "\n",
+ "> This examples uses the `GlobalWindow`, which is a single window
that covers the entire PCollection. All pipelines use the
[`GlobalWindow`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.window.html#apache_beam.transforms.window.GlobalWindow)
by default. In many cases, especially for batch pipelines, this is what we
want since we want to analyze all the data that we have."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";,
+ "height": 34
+ },
+ "id": "v06NFe9sDYXc",
+ "outputId": "f65eae63-0424-4ac0-8609-78e98ac21bd0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof
window.interactive_beam_jquery == 'undefined') {\n var jqueryScript =
document.createElement('script');\n jqueryScript.src =
'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "49.308428658266905\n"
+ ]
+ }
+ ],
+ "source": [
+ "import apache_beam as beam\n",
+ "\n",
+ "def parse_file(element):\n",
+ " file = csv.reader([element], quotechar='\"', delimiter=',',\n",
+ " quoting=csv.QUOTE_ALL, skipinitialspace=True)\n",
+ " for line in file:\n",
+ " return line\n",
+ "\n",
+ "with beam.Pipeline() as pipeline:\n",
+ " (\n",
+ " pipeline\n",
+ " | 'Read input file' >>
beam.io.ReadFromText(\"data/air_quality.csv\",\n",
+ "
skip_header_lines=1)\n",
+ " | 'Parse file' >> beam.Map(parse_file)\n",
+ " | 'Get PM' >> beam.Map(lambda x: float(x[1])) # only process
PM2.5\n",
+ " | 'Get mean value' >> beam.combiners.Mean.Globally()\n",
+ " | beam.Map(print))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "GmHEE1G5Y1z-",
+ "outputId": "248ee3d7-43af-4b53-9832-8da0eb7ac974"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "49.30842865826703"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# To verify, the above mean value matches what Pandas produces\n",
+ "airq[\"PM2.5\"].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b3gGxC6w6qXx"
+ },
+ "source": [
+ "**Congratulations!** You just created a simple aggregation processing
pipeline in batch using `GlobalWindow`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vRameihqDJ8l"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "# 2. Advanced Processing in Batch with Windowing\n",
+ "\n",
+ "Sometimes, we want to
[aggregate](https://beam.apache.org/documentation/transforms/python/overview/#aggregation)
data, like `GroupByKey` or `Combine`, only at certain intervals, like hourly
or daily, instead of processing the entire `PCollection` of data only once.\n",
+ "\n",
+ "In this case, our **objective** is to determine the **fluctuations of
air quality *every 30 days*.\n",
+ "\n",
+ "**_Windows_** in Beam allow us to process only certain data intervals
at a time.\n",
+ "In this notebook, we will go through different ways of windowing our
pipeline.\n",
+ "\n",
+ "We have already been introduced to the default GlobalWindow (see
above) that covers the entire PCollection. Now we will dive into **fixed time
windows, sliding time windows, and session windows**.\n",
+ "\n",
+ "> [Another windowing
tutorial](https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/tour-of-beam/windowing.ipynb)
with a toy dataset is recommended to go through."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gj0_S5Ka3-zb"
+ },
+ "source": [
+ "### First, we need to convert timestamps to Unix time\n",
+ "\n",
+ "Apache Beam requires us to provide the timestamp as Unix time. Let us
write the simple time conversion function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "nKBYsxFg4SIa"
+ },
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "# This function is modifiable and can convert integers to time
formats like unix\n",
+ "# Without this function and .strptime, you may run into comparison
issues!\n",
+ "def to_unix_time(time_str: str, time_format='%Y-%m-%d %H:%M:%S') ->
int:\n",
+ " \"\"\"Converts a time string into Unix time.\"\"\"\n",
+ " time_tuple = time.strptime(time_str, time_format)\n",
+ " return int(time.mktime(time_tuple))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "_mPge0KdRx20",
+ "outputId": "43475bbe-548a-4817-ed0b-534cebbe70ce"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1634220000"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "to_unix_time('2021-10-14 14:00:00')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lL0_QONF1aMH"
+ },
+ "source": [
+ "### Second, let us define some helper functions to develop our
pipeline\n",
+ "\n",
+ "In this code, we have a transform (`PrintWindowInfo`) to help us
analyze an element alongside its window information, and we have another
transform\n",
+ "(`PrintWindowInfo`) to help us analyze how many elements landed into
each window.\n",
+ "We use a custom
[`DoFn`](https://beam.apache.org/documentation/transforms/python/elementwise/pardo)\n",
+ "to access that information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "KtPL-echb2xv"
+ },
+ "outputs": [],
+ "source": [
+ "#@title Helper functions to develop our pipeline\n",
+ "\n",
+ "def human_readable_window(window) -> str:\n",
+ " \"\"\"Formats a window object into a human readable
string.\"\"\"\n",
+ " if isinstance(window, beam.window.GlobalWindow):\n",
+ " return str(window)\n",
+ " return f'{window.start.to_utc_datetime()} -
{window.end.to_utc_datetime()}'\n",
+ "\n",
+ "class PrintElementInfo(beam.DoFn):\n",
+ " \"\"\"Prints an element with its Window information for
debugging.\"\"\"\n",
+ " def process(self, element, timestamp=beam.DoFn.TimestampParam,
window=beam.DoFn.WindowParam):\n",
+ " print(f'[{human_readable_window(window)}]
{timestamp.to_utc_datetime()} -- {element}')\n",
+ " yield element\n",
+ "\n",
+ "@beam.ptransform_fn\n",
+ "def PrintWindowInfo(pcollection):\n",
+ " \"\"\"Prints the Window information with AQI in that window for
debugging.\"\"\"\n",
+ " class PrintAQI(beam.DoFn):\n",
+ " def process(self, mean_elements,
window=beam.DoFn.WindowParam):\n",
+ " print(f'>> Window [{human_readable_window(window)}], AQI:
{mean_elements}')\n",
+ " yield mean_elements\n",
+ "\n",
+ " return (\n",
+ " pcollection\n",
+ " | 'Count elements per window' >>
beam.combiners.Mean.Globally().without_defaults()\n",
+ " | 'Print counts info' >> beam.ParDo(PrintAQI())\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dtQbcRU6XYCr"
+ },
+ "source": [
+ "Note: when you run below code, pay attention to how the human
readable windows varies for each window type.\n",
+ "\n",
+ "To illustrate how windowing works, we will use the below toy data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "oOLx4IsZXoTO"
+ },
+ "outputs": [],
+ "source": [
+ "# a toy data\n",
+ "air_toy_data = [\n",
+ " ['2021-10-14 14:00:00', '43.27'],\n",
+ " ['2021-10-14 15:00:00', '44.17'],\n",
+ " ['2021-10-14 16:00:00', '48.77'],\n",
+ " ['2021-10-14 17:00:00', '55.57'],\n",
+ " ['2021-10-14 18:00:00', '56.95'],\n",
+ " ['2021-10-21 09:00:00', '36.77'],\n",
+ " ['2021-10-21 10:00:00', '34.87'],\n",
+ " ['2021-11-17 01:00:00', '62.64'],\n",
+ " ['2021-11-17 02:00:00', '65.28'],\n",
+ " ['2021-11-17 03:00:00', '65.53'],\n",
+ " ['2021-11-17 04:00:00', '70.18'],\n",
+ " ['2021-12-11 21:00:00', '69.07'],\n",
+ " ['2022-01-02 21:00:00', '76.56'],\n",
+ " ['2022-01-02 22:00:00', '78.77'],\n",
+ " ['2022-01-02 23:00:00', '73.16'],\n",
+ " ['2022-01-03 03:00:00', '74.05'],\n",
+ " ['2022-01-03 19:00:00', '100.28'],\n",
+ " ['2022-01-03 22:00:00', '80.92'],\n",
+ " ['2022-01-04 05:00:00', '80.48'],\n",
+ " ['2022-01-04 07:00:00', '84.0'],\n",
+ " ['2022-01-04 18:00:00', '95.49'],\n",
+ " ['2022-01-05 00:00:00', '69.01'],\n",
+ " ['2022-01-05 07:00:00', '76.85'],]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_QoStuYV-uku"
+ },
+ "source": [
+ "### Fixed time windows\n",
+ "\n",
+ "`FixedWindows` allow us to create fixed-sized windows with evenly
spaced intervals.\n",
+ "We only need to specify the _window size_ in seconds.\n",
+ "\n",
+ "In Python, we can use
[`timedelta`](https://docs.python.org/3/library/datetime.html#timedelta-objects)\n",
+ "to help us do the conversion of minutes, hours, or days for us.\n",
+ "\n",
+ "We then use the
[`WindowInto`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html?highlight=windowinto#apache_beam.transforms.core.WindowInto)\n",
+ "transform to apply the kind of window we want."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "Q7Q4yVzh8XWO",
+ "outputId": "076752f2-1b40-4d07-9419-88757adc99be"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 14:00:00
-- 43.27\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 15:00:00
-- 44.17\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 16:00:00
-- 48.77\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 17:00:00
-- 55.57\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 18:00:00
-- 56.95\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-21 09:00:00
-- 36.77\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-21 10:00:00
-- 34.87\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 01:00:00
-- 62.64\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 02:00:00
-- 65.28\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 03:00:00
-- 65.53\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 04:00:00
-- 70.18\n",
+ "[2021-11-29 00:00:00 - 2021-12-29 00:00:00] 2021-12-11 21:00:00
-- 69.07\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 21:00:00
-- 76.56\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 22:00:00
-- 78.77\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 23:00:00
-- 73.16\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 03:00:00
-- 74.05\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 19:00:00
-- 100.28\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 22:00:00
-- 80.92\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 05:00:00
-- 80.48\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 07:00:00
-- 84.0\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 18:00:00
-- 95.49\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-05 00:00:00
-- 69.01\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-05 07:00:00
-- 76.85\n",
+ ">> Window [2021-09-30 00:00:00 - 2021-10-30 00:00:00], AQI:
45.76714285714286\n",
+ ">> Window [2021-10-30 00:00:00 - 2021-11-29 00:00:00], AQI:
65.9075\n",
+ ">> Window [2021-11-29 00:00:00 - 2021-12-29 00:00:00], AQI:
69.07\n",
+ ">> Window [2021-12-29 00:00:00 - 2022-01-28 00:00:00], AQI:
80.86999999999999\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "import apache_beam as beam\n",
+ "from datetime import timedelta\n",
+ "\n",
+ "\n",
+ "# We first set the window size to around 1 month.\n",
+ "window_size = timedelta(days=30).total_seconds() # in seconds\n",
+ "\n",
+ "\n",
+ "# Let us set up the windowed pipeline and compute AQI every 30
days\n",
+ "with beam.Pipeline() as pipeline:\n",
+ " (\n",
+ " pipeline\n",
+ " | beam.Create(air_toy_data)\n",
+ " | 'With timestamps' >> beam.MapTuple(\n",
+ " lambda timestamp, element:\n",
+ " beam.window.TimestampedValue(float(element),
to_unix_time(timestamp))\n",
+ " )\n",
+ " | 'Fixed windows' >>
beam.WindowInto(beam.window.FixedWindows(window_size))\n",
+ " | 'Print element info' >> beam.ParDo(PrintElementInfo())\n",
+ " | 'Print window info' >> PrintWindowInfo()\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N-bqgv5K_INW"
+ },
+ "source": [
+ "### Sliding time windows\n",
+ "\n",
+ "[`Sliding
windows`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.window.html#apache_beam.transforms.window.SlidingWindows)\n",
+ "allow us to compute AQI every 30 days but each window should cover
the last 15 days.\n",
+ "We can specify the _window size_ in seconds just like with
`FixedWindows` to define the window size. We also need to specify a _window
period_ in seconds, which is how often we want to emit each window."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "Lrj1mb6Q_LW7",
+ "outputId": "4418b5bd-6dc0-44a3-ca1a-d231ec9af9e1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "window_size: 2592000.0 seconds\n",
+ "window_period: 1296000.0 seconds\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 14:00:00
-- 43.27\n",
+ "[2021-09-15 00:00:00 - 2021-10-15 00:00:00] 2021-10-14 14:00:00
-- 43.27\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 15:00:00
-- 44.17\n",
+ "[2021-09-15 00:00:00 - 2021-10-15 00:00:00] 2021-10-14 15:00:00
-- 44.17\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 16:00:00
-- 48.77\n",
+ "[2021-09-15 00:00:00 - 2021-10-15 00:00:00] 2021-10-14 16:00:00
-- 48.77\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 17:00:00
-- 55.57\n",
+ "[2021-09-15 00:00:00 - 2021-10-15 00:00:00] 2021-10-14 17:00:00
-- 55.57\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-14 18:00:00
-- 56.95\n",
+ "[2021-09-15 00:00:00 - 2021-10-15 00:00:00] 2021-10-14 18:00:00
-- 56.95\n",
+ "[2021-10-15 00:00:00 - 2021-11-14 00:00:00] 2021-10-21 09:00:00
-- 36.77\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-21 09:00:00
-- 36.77\n",
+ "[2021-10-15 00:00:00 - 2021-11-14 00:00:00] 2021-10-21 10:00:00
-- 34.87\n",
+ "[2021-09-30 00:00:00 - 2021-10-30 00:00:00] 2021-10-21 10:00:00
-- 34.87\n",
+ "[2021-11-14 00:00:00 - 2021-12-14 00:00:00] 2021-11-17 01:00:00
-- 62.64\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 01:00:00
-- 62.64\n",
+ "[2021-11-14 00:00:00 - 2021-12-14 00:00:00] 2021-11-17 02:00:00
-- 65.28\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 02:00:00
-- 65.28\n",
+ "[2021-11-14 00:00:00 - 2021-12-14 00:00:00] 2021-11-17 03:00:00
-- 65.53\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 03:00:00
-- 65.53\n",
+ "[2021-11-14 00:00:00 - 2021-12-14 00:00:00] 2021-11-17 04:00:00
-- 70.18\n",
+ "[2021-10-30 00:00:00 - 2021-11-29 00:00:00] 2021-11-17 04:00:00
-- 70.18\n",
+ "[2021-11-29 00:00:00 - 2021-12-29 00:00:00] 2021-12-11 21:00:00
-- 69.07\n",
+ "[2021-11-14 00:00:00 - 2021-12-14 00:00:00] 2021-12-11 21:00:00
-- 69.07\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 21:00:00
-- 76.56\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-02 21:00:00
-- 76.56\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 22:00:00
-- 78.77\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-02 22:00:00
-- 78.77\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-02 23:00:00
-- 73.16\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-02 23:00:00
-- 73.16\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 03:00:00
-- 74.05\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-03 03:00:00
-- 74.05\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 19:00:00
-- 100.28\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-03 19:00:00
-- 100.28\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-03 22:00:00
-- 80.92\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-03 22:00:00
-- 80.92\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 05:00:00
-- 80.48\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-04 05:00:00
-- 80.48\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 07:00:00
-- 84.0\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-04 07:00:00
-- 84.0\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-04 18:00:00
-- 95.49\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-04 18:00:00
-- 95.49\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-05 00:00:00
-- 69.01\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-05 00:00:00
-- 69.01\n",
+ "[2021-12-29 00:00:00 - 2022-01-28 00:00:00] 2022-01-05 07:00:00
-- 76.85\n",
+ "[2021-12-14 00:00:00 - 2022-01-13 00:00:00] 2022-01-05 07:00:00
-- 76.85\n",
+ ">> Window [2021-09-30 00:00:00 - 2021-10-30 00:00:00], AQI:
45.76714285714286\n",
+ ">> Window [2021-09-15 00:00:00 - 2021-10-15 00:00:00], AQI:
49.746\n",
+ ">> Window [2021-10-15 00:00:00 - 2021-11-14 00:00:00], AQI:
35.82\n",
+ ">> Window [2021-11-14 00:00:00 - 2021-12-14 00:00:00], AQI:
66.53999999999999\n",
+ ">> Window [2021-10-30 00:00:00 - 2021-11-29 00:00:00], AQI:
65.9075\n",
+ ">> Window [2021-11-29 00:00:00 - 2021-12-29 00:00:00], AQI:
69.07\n",
+ ">> Window [2021-12-29 00:00:00 - 2022-01-28 00:00:00], AQI:
80.86999999999999\n",
+ ">> Window [2021-12-14 00:00:00 - 2022-01-13 00:00:00], AQI:
80.86999999999999\n"
+ ]
+ }
+ ],
+ "source": [
+ "import apache_beam as beam\n",
+ "from datetime import timedelta\n",
+ "\n",
+ "# Sliding windows of 30 days and emit one every 15 days.\n",
+ "window_size = timedelta(days=30).total_seconds() # in seconds\n",
+ "window_period = timedelta(days=15).total_seconds() # in seconds\n",
+ "print(f'window_size: {window_size} seconds')\n",
+ "print(f'window_period: {window_period} seconds')\n",
+ "\n",
+ "with beam.Pipeline() as pipeline:\n",
+ " (\n",
+ " pipeline\n",
+ " | 'Air Quality' >> beam.Create(air_toy_data)\n",
+ " | 'With timestamps' >> beam.MapTuple(\n",
+ " lambda timestamp, element:\n",
+ " beam.window.TimestampedValue(float(element),
to_unix_time(timestamp))\n",
+ " )\n",
+ " | 'Sliding windows' >> beam.WindowInto(\n",
+ " beam.window.SlidingWindows(window_size, window_period)\n",
+ " )\n",
+ " | 'Print element info' >> beam.ParDo(PrintElementInfo())\n",
+ " | 'Print window info' >> PrintWindowInfo()\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y_SbevXv_MDk"
+ },
+ "source": [
+ "### Session time windows\n",
+ "\n",
+ "Maybe we don't want regular windows, but instead, have the windows
reflect periods where activity happened.
[`Sessions`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.window.html#apache_beam.transforms.window.Sessions)\n",
+ "allow us to create those windows.\n",
+ "We only need to specify a _gap size_ in seconds, which is the maximum
number of seconds of inactivity to close a session window. In this case, if no
event happens within 10 days, the current session windown closes and is emitted
and a new session windown is created whenenver the next event happens."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
+ },
+ "id": "zgNc-c3THB7G",
+ "outputId": "d8d5fed1-8748-4845-cbc3-0b898ce4bcd8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "gap_size: 864000.0 seconds\n",
+ "[2021-10-14 14:00:00 - 2021-10-24 14:00:00] 2021-10-14 14:00:00
-- 43.27\n",
+ "[2021-10-14 15:00:00 - 2021-10-24 15:00:00] 2021-10-14 15:00:00
-- 44.17\n",
+ "[2021-10-14 16:00:00 - 2021-10-24 16:00:00] 2021-10-14 16:00:00
-- 48.77\n",
+ "[2021-10-14 17:00:00 - 2021-10-24 17:00:00] 2021-10-14 17:00:00
-- 55.57\n",
+ "[2021-10-14 18:00:00 - 2021-10-24 18:00:00] 2021-10-14 18:00:00
-- 56.95\n",
+ "[2021-10-21 09:00:00 - 2021-10-31 09:00:00] 2021-10-21 09:00:00
-- 36.77\n",
+ "[2021-10-21 10:00:00 - 2021-10-31 10:00:00] 2021-10-21 10:00:00
-- 34.87\n",
+ "[2021-11-17 01:00:00 - 2021-11-27 01:00:00] 2021-11-17 01:00:00
-- 62.64\n",
+ "[2021-11-17 02:00:00 - 2021-11-27 02:00:00] 2021-11-17 02:00:00
-- 65.28\n",
+ "[2021-11-17 03:00:00 - 2021-11-27 03:00:00] 2021-11-17 03:00:00
-- 65.53\n",
+ "[2021-11-17 04:00:00 - 2021-11-27 04:00:00] 2021-11-17 04:00:00
-- 70.18\n",
+ "[2021-12-11 21:00:00 - 2021-12-21 21:00:00] 2021-12-11 21:00:00
-- 69.07\n",
+ "[2022-01-02 21:00:00 - 2022-01-12 21:00:00] 2022-01-02 21:00:00
-- 76.56\n",
+ "[2022-01-02 22:00:00 - 2022-01-12 22:00:00] 2022-01-02 22:00:00
-- 78.77\n",
+ "[2022-01-02 23:00:00 - 2022-01-12 23:00:00] 2022-01-02 23:00:00
-- 73.16\n",
+ "[2022-01-03 03:00:00 - 2022-01-13 03:00:00] 2022-01-03 03:00:00
-- 74.05\n",
+ "[2022-01-03 19:00:00 - 2022-01-13 19:00:00] 2022-01-03 19:00:00
-- 100.28\n",
+ "[2022-01-03 22:00:00 - 2022-01-13 22:00:00] 2022-01-03 22:00:00
-- 80.92\n",
+ "[2022-01-04 05:00:00 - 2022-01-14 05:00:00] 2022-01-04 05:00:00
-- 80.48\n",
+ "[2022-01-04 07:00:00 - 2022-01-14 07:00:00] 2022-01-04 07:00:00
-- 84.0\n",
+ "[2022-01-04 18:00:00 - 2022-01-14 18:00:00] 2022-01-04 18:00:00
-- 95.49\n",
+ "[2022-01-05 00:00:00 - 2022-01-15 00:00:00] 2022-01-05 00:00:00
-- 69.01\n",
+ "[2022-01-05 07:00:00 - 2022-01-15 07:00:00] 2022-01-05 07:00:00
-- 76.85\n",
+ ">> Window [2021-10-14 14:00:00 - 2021-10-31 10:00:00], AQI:
45.76714285714286\n",
+ ">> Window [2021-11-17 01:00:00 - 2021-11-27 04:00:00], AQI:
65.9075\n",
+ ">> Window [2021-12-11 21:00:00 - 2021-12-21 21:00:00], AQI:
69.07\n",
+ ">> Window [2022-01-02 21:00:00 - 2022-01-15 07:00:00], AQI:
80.86999999999999\n"
+ ]
+ }
+ ],
+ "source": [
+ "import apache_beam as beam\n",
+ "from datetime import timedelta\n",
+ "\n",
+ "# Sessions divided by 10 days.\n",
+ "gap_size = timedelta(days=10).total_seconds() # in seconds\n",
+ "print(f'gap_size: {gap_size} seconds')\n",
+ "\n",
+ "with beam.Pipeline() as pipeline:\n",
+ " (\n",
+ " pipeline\n",
+ " | 'Air Quality' >> beam.Create(air_toy_data)\n",
+ " | 'With timestamps' >> beam.MapTuple(\n",
+ " lambda timestamp, element:\n",
+ " beam.window.TimestampedValue(float(element),
to_unix_time(timestamp))\n",
+ " )\n",
+ " | 'Session windows' >>
beam.WindowInto(beam.window.Sessions(gap_size))\n",
+ " | 'Print element info' >> beam.ParDo(PrintElementInfo())\n",
+ " | 'Print window info' >> PrintWindowInfo()\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XcX0t6hya85F"
+ },
+ "source": [
+ "Note how the above windows are irregular."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EBSlyeBibNL0"
+ },
+ "source": [
+ "<hr style=\"border: 5px solid #003262;\" />\n",
+ "\n",
+ "# 3. Put All Together\n",
+ "\n",
+ "Section 2 uses the toy data to go through three different windowing
types. Now it is time to analyze the real data (`data/air_quality.csv`).\n",
+ "\n",
+ "Can you build one Beam pipeline to compute all AQI values for these
windows?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "plMjLuh-lKzr"
+ },
+ "outputs": [],
+ "source": [
+ "#@title Edit This Code Cell\n",
+ "..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "cellView": "form",
+ "id": "t6vBMax0bubN"
+ },
+ "outputs": [],
+ "source": [
+ "#@title Expand to see the answer\n",
+ "import apache_beam as beam\n",
+ "import csv\n",
+ "import time\n",
+ "import apache_beam as beam\n",
+ "from datetime import timedelta\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "def to_unix_time(time_str: str, time_format='%Y-%m-%d %H:%M:%S') ->
int:\n",
+ " \"\"\"Converts a time string into Unix time.\"\"\"\n",
+ " time_tuple = time.strptime(time_str, time_format)\n",
+ " return int(time.mktime(time_tuple))\n",
+ "\n",
+ "def human_readable_window(window) -> str:\n",
+ " \"\"\"Formats a window object into a human readable
string.\"\"\"\n",
+ " if isinstance(window, beam.window.GlobalWindow):\n",
+ " return str(window)\n",
+ " return f'{window.start.to_utc_datetime()} -
{window.end.to_utc_datetime()}'\n",
+ "\n",
+ "@beam.ptransform_fn\n",
+ "def OutputWindowInfo(pcollection):\n",
+ " \"\"\"Output the Window information with AQI in that
window.\"\"\"\n",
+ " class GetAQI(beam.DoFn):\n",
+ " def process(self, mean_elements,
window=beam.DoFn.WindowParam):\n",
+ " yield human_readable_window(window), mean_elements\n",
+ " return (\n",
+ " pcollection\n",
+ " | 'Count elements per window' >>
beam.combiners.Mean.Globally().without_defaults()\n",
+ " | 'Output counts info' >> beam.ParDo(GetAQI())\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def parse_file(element):\n",
+ " file = csv.reader([element], quotechar='\"', delimiter=',',\n",
+ " quoting=csv.QUOTE_ALL, skipinitialspace=True)\n",
+ " for line in file:\n",
+ " return line\n",
+ "\n",
+ "p = beam.Pipeline()\n",
+ "\n",
+ "# get the data\n",
+ "read_text = (p | 'Read input file' >>
beam.io.ReadFromText(\"data/air_quality.csv\",\n",
+ "
skip_header_lines=1)\n",
+ "| 'Parse file' >> beam.Map(parse_file)\n",
Review Comment:
nit: This formatting looks a bit strange in this section, since typically
there is an indent in the text below opened brackets.
Also multiple long lines throghout the notebook.
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