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

Introducing the primitive PTransform object – stateless ParDo

As we have already noted, the ParDo PTransform is the most basic primitive transform that we can use to do a variety of useful work. The name is an abbreviation of parallel do, and that is what it does. As already noted, there are multiple versions of this PTransform with different requirements and different behaviors. But, in essence, the basics of stateless ParDo remain valid for the other cases as well.

The essential parts of a ParDo object are illustrated in the following figure:

Figure 3.2 – A DoFn object life cycle

The first thing we notice is that the stream is split into chunks called bundles. The size of bundles or other runtime parameters are runner-specific – that is, each runner can choose its preferred way of assigning elements in a stream into bundles. The important thing to remember is that bundles are considered atomic units of work. The processing of a bundle...