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

Running Apache Beam WordCount on Apache Apex


As the next step toward running on Apex, you can also run your pipeline on a local Apex cluster, for a testing scenario that is slightly more similar to production:

mvn compile exec:java \    -P apex-runner \    -D exec.mainClass=org.apache.beam.examples.WordCount \    -Dexec.args="--inputFile=gs://apache-beam-samples/shakespeare/* --output=/tmp/output-apex/ --runner=ApexRunner --embeddedExecution=true" 

Again, you should find output files in /tmp/output-apex. The number of files may differ, but their overall contents will be the same. Unless you request particular sharding, it is up to the Beam runner to decide the parallelism of the write step.

Now, we should run this on a real YARN cluster; if you are not already in an environment with a cluster available, it is easy to set one up with Google Cloud Dataproc or AWS EMR. To do so, there is no special treatment needed.

Now, let's spin up a Dataproc cluster and run this via those instructions:

mvn compile...