High Performance in-memory computing with Apache Ignite.pdf

This book covers a verity of topics, including in-memory data grid, highly available service grid, 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: You have a high volume of ACID transactions in your system. You have database bottleneck in your application and want to solve the problem. You want to develop and deploy Microservices in a distributed fashion. 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.. You want to share Spark RDD directly in-memory (without storing the state into the 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 a scalable and fault-tolerant fashion. You want to use distributed computations in parallel fashion to gain high performance, low latency, and linear scalability. You want to accelerate applications performance without changing code. What you will learn: In-memory data fabrics use-cases and how it can help you to develop near real-time applications. In-memory data fabrics detail architecture. Caching strategies and how to use In-memory caching to improve the performance of the applications. SQL grid for in-memory caches. How to accelerates the performance of your existing Hadoop ecosystem without changing any code. Sharing Spark RDD states between different Spark applications for improving performance. Processing events & streaming data, integrate Apache Ignite with other frameworks like Storm, Camel, etc. Using distributed computing for building low-latency software. Developing distributed Microservices in fault-tolerant fashion. For