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

Apache Mesos API


Mesos has an API so that developers can build custom frameworks to run on top of the infrastructure. As you can imagine, Mesos implements the actor model for message passing, because the complexity increases without a non-blocking communication. This also leverages protocol buffers.

Scheduler HTTP API

Support for the new HTTP API was introduced in the Mesos version 0.24. The Mesos Master has the /api/v1/scheduler endpoint which the scheduler communicates.

For detailed information about the scheduler HTTP API, go to:

http://mesos.apache.org/documentation/latest/scheduler-http-api/

Requests

The master accepts the following request calls.

SUBSCRIBE

To enable communication with the master, the scheduler sends a SUBSCRIBE message via HTTP POST. The response contains the subscription confirmation and the framework ID to continue the conversation.

The SUBSCRIBE JSON request structure is:

POST /api/v1/scheduler HTTP/1.1 { 
  "type" : "SUBSCRIBE", 
  "force" : true, 
  ...