Book Image

Apache Ignite Quick Start Guide

By : Sujoy Acharya
Book Image

Apache Ignite Quick Start Guide

By: Sujoy Acharya

Overview of this book

Apache Ignite is a distributed in-memory platform designed to scale and process large volume of data. It can be integrated with microservices as well as monolithic systems, and can be used as a scalable, highly available and performant deployment platform for microservices. This book will teach you to use Apache Ignite for building a high-performance, scalable, highly available system architecture with data integrity. The book takes you through the basics of Apache Ignite and in-memory technologies. You will learn about installation and clustering Ignite nodes, caching topologies, and various caching strategies, such as cache aside, read and write through, and write behind. Next, you will delve into detailed aspects of Ignite’s data grid: web session clustering and querying data. You will learn how to process large volumes of data using compute grid and Ignite’s map-reduce and executor service. You will learn about the memory architecture of Apache Ignite and monitoring memory and caches. You will use Ignite for complex event processing, event streaming, and the time-series predictions of opportunities and threats. Additionally, you will go through off-heap and on-heap caching, swapping, and native and Spring framework integration with Apache Ignite. By the end of this book, you will be confident with all the features of Apache Ignite 2.x that can be used to build a high-performance system architecture.
Table of Contents (9 chapters)

CAP theorem and Apache Ignite

Apache Ignite supports distributed transactional cache operations, and at the same time it is highly available. Supporting both ACID transactions and high availability is a big ask for any distributed data store. Distributed data stores follow the CAP theorem. Computer scientist Eric Brewer proposed the CAP theorem, and it says that a distributed data store cannot offer more than two of the following three capabilities:

  • Consistency: You will always get the latest and greatest data. Suppose you have two nodes, A and B, and someone is updating a document/record in node B and you are reading that same record from node A. You should get the latest update made to the record in node B.
  • Availability: You should always get a response; it may not be the latest data, but it should not throw an error.
  • Partition tolerance: This means that if you remove the network...