Book Image

Fast Data Processing Systems with SMACK Stack

By : Raúl Estrada
Book Image

Fast Data Processing Systems with SMACK Stack

By: Raúl Estrada

Overview of this book

SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing. We’ll start off with an introduction to SMACK and show you when to use it. First you’ll get to grips with functional thinking and problem solving using Scala. Next you’ll come to understand the Akka architecture. Then you’ll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, you’ll learn how to perform linear scalability in databases with Apache Cassandra. You’ll grasp the high throughput distributed messaging systems using Apache Kafka. We’ll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspect of SMACK using a few case studies. By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing.
Table of Contents (15 chapters)
Fast Data Processing Systems with SMACK Stack
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface

Kafka and Akka


The connector is available at Maven Central for Scala 2.11 in the coordinates:

libraryDependencies += "com.typesafe.akka" %% "akka-stream-kafka" % "0.11-M4"

If you remember, a producer publishes messages to Kafka topics. The message itself contains information about what topic and partition to publish, so one can publish to different topics with the same producer. The underlying implementation uses the KafkaProducer.

When creating a consumer stream we need to pass ProducerSettings defining:

  • Kafka cluster bootstrap servers
  • Serializers for the keys and values
  • Tuning parameters

Here we have a ProducerSettings example:

import akka.kafka._ 
import akka.kafka.scaladsl._ 
import org.apache.kafka.common.serialization.StringSerializer 
import org.apache.kafka.common.serialization.ByteArraySerializer 
 
val producerSettings = ProducerSettings(system, new ByteArraySerializer, new StringSerializer).withBootstrapServers("localhost:9092") 

The easiest way to publish...