Book Image

Learning Apache Apex

By : Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles
Book Image

Learning Apache Apex

By: Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles

Overview of this book

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

WordCount in Apache Beam


Now that we have introduced the big picture and concepts of Beam, we'll walk through the basic example of Beam WordCount in Java, run it first in a testing runner, and later on Apex. Since you have already seen this on Apex elsewhere in the book, we will jump right into the Beam code.

Here is the entirety of the example we call minimal word count:

PipelineOptions options = ... Pipeline pipeline = Pipeline.create(options);pipeline    .begin()    // Read some data (parallel connector that ships with Beam)    .apply(TextIO.read().from("gs://apache-beam-samples/shakespeare/*"))\    // Split into "words" (elementwise transform)    .apply(FlatMapElements        .into(TypeDescriptors.strings())        .via((String word) -> Arrays.asList(word.split("[^\\p{L}]+"))))    // Drop empty strings (elementwise transform)    .apply(Filter.by((String word) -> !word.isEmpty()))    // Count per words (per key aggregation)    .apply(Count.<String>perElement())    // Format...