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Building Data Streaming Applications with Apache Kafka

You're reading from   Building Data Streaming Applications with Apache Kafka Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787283985
Length 278 pages
Edition 1st Edition
Tools
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Authors (2):
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Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Messaging Systems FREE CHAPTER 2. Introducing Kafka the Distributed Messaging Platform 3. Deep Dive into Kafka Producers 4. Deep Dive into Kafka Consumers 5. Building Spark Streaming Applications with Kafka 6. Building Storm Applications with Kafka 7. Using Kafka with Confluent Platform 8. Building ETL Pipelines Using Kafka 9. Building Streaming Applications Using Kafka Streams 10. Kafka Cluster Deployment 11. Using Kafka in Big Data Applications 12. Securing Kafka 13. Streaming Application Design Considerations

Scala Kafka consumer

This is the Scala version of the previous program and will work the same as the previous snippet. Kafka allows you to write consumer in many languages including Scala.

import org.apache.kafka.clients.consumer._
import org.apache.kafka.common.TopicPartition
import org.apache.log4j.Logger
import java.util._


object DemoConsumer {
private val log: Logger = Logger.getLogger(classOf[DemoConsumer])

@throws[Exception]
def main(args: Array[String]) {
val topic: String = "test1"
val topicList: List[String] = new ArrayList[String]
topicList.add(topic)
val consumerProperties: Properties = new Properties
consumerProperties.put("bootstrap.servers", "10.200.99.197:6667")
consumerProperties.put("group.id", "Demo_Group")
consumerProperties.put("key.deserializer", "org.apache.kafka.common.serialization...
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