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Building Big Data Pipelines with Apache Beam

You're reading from   Building Big Data Pipelines with Apache Beam Use a single programming model for both batch and stream data processing

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
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Author (1):
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Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
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Table of Contents (13) Chapters Close

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam FREE CHAPTER 3. Chapter 2: Implementing, Testing, and Deploying Basic Pipelines 4. Chapter 3: Implementing Pipelines Using Stateful Processing 5. Section 2 Apache Beam: Toward Improving Usability
6. Chapter 4: Structuring Code for Reusability 7. Chapter 5: Using SQL for Pipeline Implementation 8. Chapter 6: Using Your Preferred Language with Portability 9. Section 3 Apache Beam: Advanced Concepts
10. Chapter 7: Extending Apache Beam's I/O Connectors 11. Chapter 8: Understanding How Runners Execute Pipelines 12. Other Books You May Enjoy

Task 19 – Implementing RPCParDo in the Python SDK

This task will be a reimplementation of Task 8 from Chapter 3, Implementing Pipelines using Stateful Processing. We will use a stateful DoFn to batch the elements for a defined maximal amount of time. As always, we will restate the problem again for clarity.

Problem definition

Use a given RPC service to augment data in the input stream using batched RPCs with batches whose size are about K elements. Also, resolve the batch after, at most, time T to avoid (possibly) an infinitely long waiting time for elements in small batches.

As in the previous task, we will skip the discussion of the problem's decomposition as we discussed that when we implemented Task 8. Instead, we will jump directly into its implementation using the Python SDK.

Solution implementation

The implementation can be found in chapter6/src/main/python/rpc_par_do.py. It can be broken down into the RPCParDoStateful transform, which is declared...

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