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

Resource allocation


Mesos has a resource allocation module that contains the policy Mesos master uses to determine the quantity of resource offers made to each framework. As developers, we can customize the module to implement our own allocation policy, for example, we can manipulate the priority and weight of resources, to meet the business requirements. We can also develop custom allocation modules.

One objective of the resource allocation module is to ensure fair resource distribution among the frameworks. The efficiency of a cluster manager lies in the choice of the correct sharing policy algorithm.

For example, Hadoop is governed by the max-min fairness allocation algorithm, in which resource requirements are distributed equitably among competitors. The effectiveness of this algorithm is proven in homogeneous environments. Unfortunately, fast data requires heterogeneous environments.

The distribution of resources between frameworks with heterogeneous demands for resources brings an interesting...