Skip to main content

Book: High performance in-memory computing with Apache Ignite has been published

The book "High performance in-memory computing with apache Ignite" has been released and
available at http://leanpub.com/ignite

Print copy of the book is available at Lulu.com & Amazon bookstore.

Support independent publishing: Buy this book on Lulu.
UP1: NOW the book is available for purchase from the Russian federation through PayPal (Ignore the yellow warning).


The goal of the book is to provide a guide for those who really need to implement the In-memory platform in their projects. At the same time, the idea behind the book is not writing a manual.

This book wraps all the topics like in-memory data grid, highly available service grid, streaming and in-memory computing use cases from high-performance computing to get the performance gain. The book will be particularly useful for those, who have the following use cases:

  • You have database bottleneck in your application and want to solve the problem.
  • You have a high volume of ACID transactions in your system.
  • You want to develop and deploy microservices in distributed fashion.
  • You have existing Hadoop ecosystem (OLAP) and want to improve the performance of the Map/Reduce jobs without making any changes in your existing Map/Reduce jobs.
  • 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.
  • You are planning to migrate to microservices and the web session clustering is the problem for you.
  • You are planning to process continuous never-ending streams and complex events of data in scalable and fault-tolerant fashion.
  • You want to use distributed computations in parallel fashion to gain high performance, low latency, and linear scalability.
  • You heard about Off-heap memory but don't know how to use it in your application.
For every topic, a complete application is delivered, which will help the audience to quick start with the topic. The book is a project-based guide, where each chapter focuses on the complete implementation of a real-world scenario, the commonly occurring challenges in each scenario has also discussed, along with tips and tricks and best practices on how to overcome them. Every chapter is independent and a complete project.

Who is this book for

Target audience of this book will be IT architect, team leaders, a programmer with minimum programming knowledge, who want to get the maximum performance from their applications.

No excessive knowledge is required, though it would be good to be familiar with JAVA and Spring framework. The book is also useful for any reader, who already familiar with Oracle Coherence, Hazelcast, Infinispan or memcached.

See the full table of contents of the book here.

Happy Reading.

Comments

Popular posts from this blog

8 things every developer should know about the Apache Ignite caching

Any technology, no matter how advanced it is, will not be able to solve your problems if you implement it improperly. Caching, precisely when it comes to the use of a distributed caching, can only accelerate your application with the proper use and configurations of it. From this point of view, Apache Ignite is no different, and there are a few steps to consider before using it in the production environment. In this article, we describe various technics that can help you to plan and adequately use of Apache Ignite as cutting-edge caching technology. Do proper capacity planning before using Ignite cluster. Do paperwork for understanding the size of the cache, number of CPUs or how many JVMs will be required. Let’s assume that you are using Hibernate as an ORM in 10 application servers and wish to use Ignite as an L2 cache. Calculate the total memory usages and the number of Ignite nodes you have to need for maintaining your SLA. An incorrect number of the Ignite nodes can become a b...

Apache Ignite Baseline Topology by Examples

Ignite Baseline Topology or BLT represents a set of server nodes in the cluster that persists data on disk. Where, N1-2 and N5 server nodes are the member of the Ignite clusters with native persistence which enable data to persist on disk. N3-4 and N6 server nodes are the member of the Ignite cluster but not a part of the baseline topology. The nodes from the baseline topology are a regular server node, that store's data in memory and on the disk, and also participates in computing tasks. Ignite clusters can have different nodes that are not a part of the baseline topology such as: Server nodes that are not used Ignite native persistence to persist data on disk. Usually, they store data in memory or persists data to a 3rd party database or NoSQL. In the above equitation, node N3 or N4 might be one of them. Client nodes that are not stored shared data. To better understand the baseline topology concept, let’s start at the beginning and try to understand its goal and what ...

Benchmarking high performance java collection framework

I am an ultimate fan of java high performance framework or library. Java native collection framework always works with primitive wrapper class such as Integer, Float e.t.c. Boxing and unboxing of wrapper class to primitive data type always decrease the java execution performance. Most of us, always looking for such a library or framework to works with primitive data type in collections for increasing performance of Java application. Most of the time i uses javolution framework to get better performance, however, this holiday i have read about a few new java collections frameworks and decided to do some homework benchmarking to find out, how much they could better than Java native collection framework. I have examine two new java collection framework, one of them are fastutil and another one are HPPC. For benchmarking i have used java JMH with mode Throughput. For benchmarking i took similar collection for java ArrayList, HashSet and HasMap from two above described frameworks. Col...