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 – GroupByKey

As we have seen, a GroupByKey transform works in the way illustrated in the following figure:

Figure 2.14 – GroupByKey

As in the case of Combine PTransform objects, the input stream must be keyed. This is a way of saying that the PCollection must have elements of the KV type. This is generally true for any stateful operations. The reason for this is that having a state (which cannot be partitioned) divided into smaller, independent sub-states means that it cannot scale and would therefore lead to scalability issues. Therefore, Beam explicitly prohibits this and enforces the use of keyed PCollections for the input of each stateful operation.

The GroupByKey transform then takes this keyed stream (in Figure 2.14, the key is represented as the shape of the stream element) and creates something that can be viewed as a sub-stream for each key. We can then process elements with a different...