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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

Do It Yourself


We will build a use case using filters, group by, and aggregators. The use case finds the top 10 devices that generate the maximum data in a batch. Here is the pseudo code:

  • Write a data generator that will publish an event with fields such as phone number, bytes in and bytes out
  • The data generator will publish events in Kafka
  • Write a topology program:
    • To get the events from Kafka
    • Apply filter to exclude phone number to take part in top 10
    • Split event on the basis of comma
    • Perform group by operation to bring same phone numbers together
    • Perform aggregate and sum out bytes in and bytes out together
    • Now, apply assembly with the FirstN function which requires the field name and number elements to be calculated
    • And finally display it on the console

You will find the code in the code bundle for reference.

Program:

package com.book.chapter8.diy;

In the following code snippet, we have import files:

import org.apache.storm.Config; 
import org.apache.storm.LocalCluster; 
import org.apache.storm.kafka...
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