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Apache Ignite Quick Start Guide

Apache Ignite Quick Start Guide

By : Acharya
3 (1)
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Apache Ignite Quick Start Guide

Apache Ignite Quick Start Guide

3 (1)
By: 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)
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Caching topology

The Apache Ignite caching API provides three caching modes to distribute cache elements:

  • Local
  • Partitioned
  • Replicated

Local

The local mode is similar to JVM caching using a HashMap. Data is local to the node and not distributed to any other nodes of the cluster. The following are the benefits of the local mode:

  • Fastest data access as there is no need for network operations
  • Fastest data modification

The main drawback of this approach is if there is more than one JVM that needs to access the same data, then more than one local cache will be created. A local cache is useful for read-only data access where the JVM doesn't need to update any cache elements. Still, a local cache is way better than hashmap...

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