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Building Big Data Pipelines with Apache Beam

You're reading from  Building Big Data Pipelines with Apache Beam

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Pages 342 pages
Edition 1st Edition
Languages
Author (1):
Jan Lukavský Jan Lukavský
Profile icon Jan Lukavský

Table of Contents (13) Chapters

Preface Section 1 Apache Beam: Essentials
Chapter 1: Introduction to Data Processing with Apache Beam Chapter 2: Implementing, Testing, and Deploying Basic Pipelines Chapter 3: Implementing Pipelines Using Stateful Processing Section 2 Apache Beam: Toward Improving Usability
Chapter 4: Structuring Code for Reusability Chapter 5: Using SQL for Pipeline Implementation Chapter 6: Using Your Preferred Language with Portability Section 3 Apache Beam: Advanced Concepts
Chapter 7: Extending Apache Beam's I/O Connectors Chapter 8: Understanding How Runners Execute Pipelines Other Books You May Enjoy

Understanding windowing semantics in depth

In Chapter 1, Introducing Data Processing with Apache Beam, we introduced the basic types of window functions. To recap, we defined the following:

  • Fixed windows
  • Sliding windows
  • Global window
  • Session windows

We also defined two basic types of windows: key-aligned and key-unaligned. The first three types (fixed, sliding, and global) are key-aligned, and session windows are key-unaligned (as in session windows, each window can start and end at different times for different keys). However, what we skipped in Chapter 1, Introduction to Data Processing with Apache Beam, was the fact that we can define completely custom windowing logic.

The Window.into transform accepts a generic WindowFn instance, which defines the following main methods:

  1. The assignWindows method, which assigns elements into a set of window labels.
  2. The isNonMerging method, which tells the runner whether the WindowFn instance defines merging...
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