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

Distributed systems need to be resilient


As discussed in Chapter 2, Getting Started with Application Development, Apex applications run on a cluster as a distributed set of processes. Distribution enables scalability, and with the correct architecture, adding more resources to a compute cluster (such as YARN) allows applications to scale horizontally (refer to Chapter 4, Scalability, Low Latency, and Performance). At the same time, growing number of processes and machines also increases the likelihood of failure. Hardware or software failures cannot be avoided.

In order to prevent failure resulting in downtime or incorrect results, the system has to be resilient. Fault-tolerance mechanisms should cover high availability (HA) as well as provide with processing correctness guarantee to the user. For a production-quality and business-critical system, these aspects should be important evaluation criteria. A stream data processing platform should provide fault-tolerance along with scalability...