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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 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

Basic Storm Trident topology


Here, in basic Storm Trident topology we will go through a word count example. More examples will be explained later in the chapter. This is the code for the example:

FixedBatchSpout spout = new FixedBatchSpout(new Fields("sentence"), 3,
new Values("this is simple example of trident topology"),
new Values("this example count same words"));
spout.setCycle(true); // Line 1
TridentTopology topology = new TridentTopology(); // Line 2
MemoryMapState.Factory stateFactory = new MemoryMapState.Factory(); // Line 3
topology.newStream("spout1", spout) // Line 4
.each(new Fields("sentence"), new Split(), new Fields("word")) // Line 5
.groupBy(new Fields("word")) // Line 6
.persistentAggregate(stateFactory, new Count(), new Fields("count")).newValuesStream() // Line 7
.filter(new DisplayOutputFilter()) // Line 8
.parallelismHint(6); // Line 9
Config config = new Config(); // Line 10
config.setNumWorkers(3); // Line 11
LocalCluster cluster = new LocalCluster(); // Line 12...
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