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

Partitioning and how it works


As the volume of incoming data increases, it can overwhelm the processing capabilities of the application resulting in increasing latencies and reduced throughput. It is rarely the case that the resources of the entire application are inadequate; instead, a careful analysis often reveals one or more bottlenecks. Addressing these bottlenecks will often resolve the problem. If the input data rate continues to increase, it may again cross the processability threshold, at which point the analysis must be repeated to find and resolve the new bottlenecks.

The modus operandi for addressing a bottleneck can take several forms, depending on the nature of the application, its configuration, the cluster environment, and other factors, for example:

  • Use a faster algorithm if available and compute resources are the constraint
  • Use more space-efficient algorithms and increase the memory allocation if excessive garbage collection (GC) calls are observed
  • Use additional cluster nodes...