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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

Connecting Kafka to Spark Streaming


The following section walks you through a program that reads the streaming data off the Kafka topic and counts the words. The aspects that will be captured in the following code are as follows:

  • Kafka-Spark Streaming integration
  • Creating and consuming from DStreams in Spark
  • See the streaming application reading from an infinite unbounded stream to generate results

Let's take a look at the following code:

package com.example.spark; 
Import files: 
import java.util.Collection; 
import java.util.HashMap; 
import java.util.Iterator; 
import java.util.Map; 
import java.util.regex.Pattern; 
 
import org.apache.spark.SparkConf; 
import org.apache.spark.api.java.function.Function; 
import org.apache.spark.streaming.Duration; 
import org.apache.spark.streaming.api.java.JavaDStream; 
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream; 
import org.apache.spark.streaming.api.java.JavaStreamingContext; 
import org.apache.spark.streaming.kafka.KafkaUtils...
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