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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Input sources and output stores


Spark Streaming supports three kinds of input sources:

  • Basic sources: Sources directly available in the StreamingContext API. Examples: file systems, socket connections, and Akka actors.

  • Advanced sources: Sources like Kafka, Flume, Kinesis, Twitter, and so on, which are available through extra utility classes.

  • Custom sources: Requires implementing a user-defined receiver.

Multiple receivers can be created in the same application to receive data from different sources. It is important to allocate enough resources (cores and memory) for enabling receivers and tasks to execute simultaneously. For example, if you start your application with one core, it will be taken by the receiver and no tasks will be executed because of a lack of available cores.

Basic sources

There are four basic sources available in Spark StreamingContext as shown in the following table:

Source

Description

TCP stream

For streaming data via TCP/IP by specifying a hostname and a port number...

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