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

Specifying the PCollection Coder object and the TypeDescriptor object

The PCollection name is an abbreviation of parallel collection, which suggests that it is a collection of elements that are somewhat distributed among multiple workers. And that is exactly what a parallel collection is. To be able to communicate with the individual elements of this collection, each element needs a serialized representation. That is, we need a piece of code that takes a raw (in-memory) object and produces a byte representation that can be sent over the wire. After receiving on the remote side, we need another piece of code that will take this byte representation and recreate the original in-memory object (or rather, a copied version of the original). And that is exactly what coders are for.

We have already used Coder in our test cases. Recall how we constructed our TestStream object:

TestStream.create(StringUtf8Coder.of())

The reason we need to specify a Coder object here is that every PCollection...