My first acquaintance with high load systems was at the beginning of 2007. I started working on a real-world project in 2009. From that moment on, I've spent most of my office time with Cassandra, Hadoop, and numerous CEP tools. Our first Hadoop project (in 2011-2012) with a cluster of 54 nodes often disappoints me with its long startup time. I have never been satisfied with the performance of our applications and I'm always looking for something new to boost the performance of our information systems. During this time, I have tried HazelCast, Ehcache, and Oracle Coherence as in-memory caches to gain the performance of applications. I was usually disappointed in the complexity of using this library for functional limitations.

When I first encountered Apache Ignite, I was amazed. It was the platform that I'd been waiting on for a long time: a simple Spring-based framework with a lot of awesome features such as database caching, big data acceleration, streaming, and compute/service grids.

The goal of the book is to provide a guide for those who really need to implement an in-memory platform in their projects. At the same time, the idea behind the book is not writing a manual. Although the Apache Ignite platform is very big and growing day by day, we concentrate only on the features of the platform (from our point of view) that can really help to improve the performance of applications.

This book covers a variety of topics, including in-memory data grids, highly available service grids, streaming (event processing for IoT and fast data), and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those who have the following use cases:

1. You have a high volume of ACID transactions in your system.
2. You have database bottleneck in your application and want to solve the problem.
3. You want to develop and deploy microservices in a distributed fashion.
4. You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs.
5. You want to share Spark RDD directly in-memory (without storing the state to disk), which can dramatically increase the performance of the Spark jobs.
6. You are planning to migrate to microservices and the web session clustering is the problem for you.
7. You are planning to process continuous never-ending streams and complex events of data in a scalable and fault-tolerant fashion.
8. You want to use distributed computations in parallel fashion to gain high performance, low latency, and linear scalability.
9. You want to accelerate applications performance without changing code.

You can check out the full table of contents on Leanpub. The print version will be also available soon. A book sample in PDF format can be downloaded here. The source code of the book is available here.

The paperback version of the book is now available at Lulu.com & Amazon.com.