Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

Ensuring pipeline upgradability

First, be aware that Beam (currently, as of version 2.28.0) does not offer an abstraction that would allow us to transfer a pipeline between runners including its state. The code of the pipeline can be transferred, but that means a new pipeline will be created and that any computation done on the previous runner is lost. That is due to the fact that, currently, the processing of pipeline upgrades is a runner-specific task, and details might therefore differ slightly based on which runner we choose.

That is the bad news. The good news is that the pipeline upgrade process is generally subject to the same constraints that are mostly runner independent and, therefore, the chances are high that very similar rules will apply to the majority of runners.

Let's look at the tasks that a runner must perform to upgrade a pipeline:

  1. The complete state (including the timers) of all transforms must be stored in a durable and persistent location. This...