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Practical Real-time Data Processing and Analytics

You're reading from  Practical Real-time Data Processing and Analytics

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Pages 360 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introducing Real-Time Analytics 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Packaging structure of Spark Streaming


In this section, we will discuss the various APIs and operations exposed by Spark Streaming.

Spark Streaming APIs

All Spark Streaming classes are packaged in the org.apache.spark.streaming.* package. Spark Streaming defines two core classes which also provide access to all Spark Streaming functionality, such as StreamingContext.scala and DStream.scala.

Let's examine the following functions and roles performed by these classes:

  • org.apache.spark.streaming.StreamingContext: This is an entry point to Spark Streaming functionality. It defines methods for creating the objects of DStream.scala and also for starting and stopping the Spark Streaming jobs.
  • org.apache.spark.streaming.dstream.DStream.scala: DStreams, or discretized streams, provide the basic abstraction for Spark Streaming. They provide the sequence of RDDs created from the live data for transforming the existing DStreams. This class defines the global operations that can be performed on all DStreams...
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