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Infinispan data grid platform definitive guide

4.5 (4)

Infinispan data grid platform definitive guide

4.5 (4)

Overview of this book

This practical guide is intended for those who want to learn how to build extremely scalable applications. This book is easy to read and is aimed at Java enterprise developers with a solid knowledge of Java. However, no previous coding experience with Infinispan is required.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started

1. Getting Started

Introducing the Infinispan data grid

Infinispan and JSR 107 – Java Temporary Caching API

Getting started with Infinispan

Running the sample application

Creating your first project

Building Infinispan from source

Contributing to the project

Summary

2. Barriers to Scaling Data

2. Barriers to Scaling Data

Understanding performance and scalability

Improving performance using Infinispan

Improving scalability

Points to consider about relational databases

Scaling out your database

Summary

3. Using the APIs

3. Using the APIs

Infinispan architecture

Anatomy of an Infinispan clustered cache

Configuring an Infinispan cache

Infinispan APIs

Persisting data in Infinispan

Writing event listeners and notifications

Introducing JSR-107 – The Java Caching API

Summary

4. Infinispan Topologies

4. Infinispan Topologies

Clustering modes

Summary

5. Data Access Patterns

5. Data Access Patterns

Data access patterns

Understanding and configuring second-level cache in Hibernate with Infinispan

Implementing the cache-aside programming pattern

Reading and writing through cache

Writing behind caching

Summary

6. Case Study – The TicketMonster Application

6. Case Study – The TicketMonster Application

The JBoss developer framework

Installing and running the TicketMonster application

The TicketMonster application use cases

Architecture and design of TicketMonster

Scaling TicketMonster

Adding Infinispan to Ticket Monster

Summary

7. Understanding Transactions and Concurrency

7. Understanding Transactions and Concurrency

Transaction fundamentals

Java Transaction API

Transactional modes

Transactional models

Batch mode

Transaction recovery

Locking and concurrency control

Summary

8. Managing and Monitoring Infinispan

8. Managing and Monitoring Infinispan

An overview of monitoring and managing with Java

Interacting with Infinispan via JMX

Other management tools

Summary

9. Server Modules

9. Server Modules

Client/Server access

Introduction to server modules

Infinispan REST server

Using the Hot Rod server

Infinispan memcached server

The Infinispan WebSocket server

Introducing the command line interface (CLI)

Summary

10. Getting Started with Hibernate OGM

10. Getting Started with Hibernate OGM

Introducing Hibernate OGM

Installing and using Hibernate OGM

Summary

11. An Introduction to JGroups

11. An Introduction to JGroups

Introduction to group communication

Understanding the JGroups project

The JGroups architecture

Customizing JGroups settings on Infinispan

Summary

12. Advanced Topics

12. Advanced Topics

Cross-site replication

Integrating Infinispan with CDI

Using the Map/Reduce API

Improving the serialization process using externalizers

Summary

Index

Index

Using the Map/Reduce API

According to Gartner, from now on in-memory data grids and in-memory computing will be racing towards mainstream adoption and the market for this kind of technology is going to reach 1 billion by 2016. Thinking along these lines, Infinispan already provides a MapReduce API for distributed computing, which means that we can use Infinispan cache to process all the data stored in heap memory across all Infinispan instances in parallel.

If you're new to MapReduce, don't worry, we're going to describe it in the next section in a way that gets you up to speed quickly.

An introduction to Map/Reduce

MapReduce is a programming model introduced by Google, which allows for massive scalability across hundreds or thousands of servers in a data grid. It's a simple concept to understand for those who are familiar with distributed computing and clustered environments for data processing solutions.

Tip

You can find the paper about MapReduce in the following link:

...

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Tech Concepts

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Programming languages

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