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

Python SDK type hints and coders

Python3 can provide type hints on functions, as shown in the following code:

def toKv(s: str) -> beam.typehints.KV[bytes, bytes]:
  return ("".encode("utf-8"), s.encode("utf-8"))

The previous code defines a method called toKv that takes a string (str) as input and outputs an object that's compatible with beam.typehints.KV[bytes, bytes]. When we use such a function in a simple transform such as beam.Map(toKv), Beam can infer the type of the resulting PCollection and can automatically use a special-purpose coder instead of pickle. In the case of bytes, this would be ByteArrayCoder.

Besides declaring type hints to mapping functions, we can use a decorator for DoFns, which will declare the (input or output) type hint explicitly for the whole transform:

@beam.typehints.with_input_types(
    beam.typehints.Tuple[
        str, beam.typehints...