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Fast Data Processing Systems with SMACK Stack

You're reading from  Fast Data Processing Systems with SMACK Stack

Product type Book
Published in Dec 2016
Publisher Packt
ISBN-13 9781786467201
Pages 376 pages
Edition 1st Edition
Languages
Author (1):
Raúl Estrada Raúl Estrada
Profile icon Raúl Estrada

Table of Contents (15) Chapters

Fast Data Processing Systems with SMACK Stack
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. An Introduction to SMACK 2. The Model - Scala and Akka 3. The Engine - Apache Spark 4. The Storage - Apache Cassandra 5. The Broker - Apache Kafka 6. The Manager - Apache Mesos 7. Study Case 1 - Spark and Cassandra 8. Study Case 2 - Connectors 9. Study Case 3 - Mesos and Docker

Architecture


Kafka was created at LinkedIn. To start with LinkedIn used Java Message Service (JMS). But when they needed more power, that is, a scalable architecture, the LinkedIn development team decided to build the project that today we know as Kafka. In 2011, Kafka was open sourced as theApache Project. Due to size constraints, in this section we'll leave the reader with some reflections on why the architecture is designed in the way it is.

The Figure 5-6, A topic with 3 partitions, shows a topic with three partitions. We can see the five Kafka components: Zookeeper, broker, topic, producer and consumer.

Figure 5-6. A topic with 3 partitions

The Kafka project goals are:

  • An API: To support the custom implementation of producers and consumers
  • Low overhead: Low network latency and low storage overhead with message persistence on disk
  • High throughput: Publishing and subscribing of millions of messages, supporting data feeds in real time
  • Distributed: Highly scalable architecture to handle low...
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