Book Image

Practical Real-time Data Processing and Analytics

Book Image

Practical Real-time Data Processing and Analytics

Overview of this book

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

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...