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See more details on The Apache Ignite Book . Spring Data  provides a unified and easy way to access the different kinds of persistence store, both relational database systems, and NoSQL data stores. It is on top of JPA, adding another layer of abstraction and defining a standard-based design to support persistence Layer in a Spring context. Apache Ignite  IgniteRepository  implements Spring Data CrudRepository interface and extends basic capabilities of the  CrudRepository , which in turns supports: Basic CRUD operations on a repository for a specific type. Access to the Apache Ignite SQL grid via Spring Data API. With Spring Data's repositories, you only need to write an interface with finder methods to query the objects. All the CRUD method for manipulating the objects will be delivered automatically. As an example: @RepositoryConfig(cacheName = "DogCache") public interface DogRepository extends IgniteRepository<Dog, Long> { List<Do...

Portions of this article were taken from the book  High-Performance In-Memory Computing With Apache Ignite . If it got you interested, check out the rest of the book for more helpful information. Before reading, be sure to check out  Part 1 ! Apache Ignite provides a vanilla distributed in-memory file system called Ignite File System (IGFS) with similar functionality to Hadoop HDFS. This is one of the unique features of Apache Ignite that helps accelerate Big Data computing. IGFS implements the Hadoop file system API and is designed to support Hadoop v1 and Yarn Hadoop v2. Ignite IGFS can transparently plug into Hadoop or Spark deployment. One of the greatest benefits of the IGFS is that it does away with Hadoop NamedNode in the Hadoop deployment; it seamlessly utilizes Ignite’s in-memory database under the hood to provide completely automatic scaling and failover without any additional shared storage. IGFS uses memory instead of disk to produce a distributed, fault-tole...

Recently Apache Ignite 2.0 introduce a beta version of the in-memory machine learning grid, which is a distributed machine learning library built on top of the Apache IMDG. This beta release of ML library can perform local and distributed vector, decompositions and matrix algebra operations. The data structure can be stored in Java heap, off-heap or distributed Ignite caches. At this moment, the Apache Ignite ML grid doesn't support any prediction or recommendation analysis. In this short post, we are going to download the new Apache Ignite 2.0 release, build the example and run them. 1. Download and unpack the Apache Ignite 2.0 distribution. Download the Apache Ignite 2.0 binary release version from the following link . Unpack the distribution somewhere in your workstation (e.g /home/ignite/2.0 ) and set the IGNITE_HOME path to the directory. 2. Start the Apache Ignite remote node Run the following command in the terminal window. ignite.sh examples/config/example-ignite....

Yesterday I have got the first paperback version of the book High performance in-memory computing with Apache Ignite  The book is available at Lulu.com & Amazon bookstore. Product details Paperback:  360 pages Language:  English ISBN-10:  1365732355 ISBN-13:  978-1365732355 Product Dimensions:  8.3 x 0.8 x 11 inches Shipping Weight:  1.8 pounds Happy reading!!

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. 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 m...

In this article, author of the book "High Performance in-memory computing with Apache Ignite" , discussing the design of modern application architecture with Apache Ignite. Part of this article taken from the book . Let’s take a quick look at an architecture of a traditional system. The traditional application architecture uses data stores which have synchronous read-write operations. This is useful for data consistency and data durability, but it is very easy to get bottleneck if there are a lot of transactions waiting in the queue. Consider the following traditional architecture as shown below. High-volume transaction processing. In-memory data grid adds an additional layer within an environment, which uses the Random-Access Memory (RAM) of the server to store most of all data required by the applications. In-memory data grid sits between the application servers and the data store. In-memory data grid uses a cache of frequently accessed data by the client in th...

The book High Performance in-memory computing with Apache Ignite has been completed and available at LeanPub . Table of contents: Introduction What is Apache Ignite Who uses Apache Ignite Why Ignite instead of others Our Hope Chapter one: Installation and the first Ignite application Pre-requirities Installation Run multiple instances of Ignite in a single host Configure a multi-node cluster in different host Rest client to manipulate with Ignite Java client SQL client Conclusion What's Next Chapter two: Architecture overview Functional overview ClusterTopology Client and Server Embedded with the application Server in separate JVM (real cluster topology) Client and Server in separate JVM on single host Caching Topology Partitioned caching topology Replicated caching topology Local mode Caching strategy Cache-aside Read-through and Write-through Write behind Data model CAP theorem and where does Ignite stand in? Clustering Cluster group Da...