Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
Arrow right icon
View More author details
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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime