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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

Trident operations


As we discussed earlier, Trident operations are Storm bolt implementation. We have a vast range of operations available in Trident. They can perform complex operations and aggregate with cache in memory. The following are operations available with Trident.

Functions

The following are characteristics of functions:

  • Class has to extend BaseFunction
  • This is a partition of the local operation that means no network transfer is involved and is applied to each batch partition independently
  • It takes a set of inputs and emits zero or more output
  • In output, it emits an output tuple including the original input tuple

Here is the example:

class PerformDiffFunction extends BaseFunction {
  @Override
  public void execute(TridentTuple tuple, TridentCollector collector) {
    int number1 = tuple.getInteger(0);
    int number2 = tuple.getInteger(1); if(number2>number1){
      collector.emit(new Values(number2-number1));
    }
  }
}

Input:

[1,2]
[3,4]
[7,3]

Output:

[1,2,1]
[3,4,1]

map and flatMap...

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