INFINISPAN DATA GRID PLATFORM PDF

Comment 7. This two-part series aims to introduce the reader to Infinispan , a new open source, LGPL licensed data grid platform. The first part will focus on an overview of the scope and capabilities of Infinispan, along with usage examples and a brief tour of the APIs. An upcoming second part will take a deep-dive into the architecture, more advanced APIs and extending Infinispan.

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Comment 7. This two-part series aims to introduce the reader to Infinispan , a new open source, LGPL licensed data grid platform. The first part will focus on an overview of the scope and capabilities of Infinispan, along with usage examples and a brief tour of the APIs.

An upcoming second part will take a deep-dive into the architecture, more advanced APIs and extending Infinispan. I first publicly announced the project in April , and a string of alpha and beta releases quickly followed. While there are several differences between JBoss Cache and Infinispan, the most significant difference is scope.

JBoss Cache focused on being a clustered caching library while Infinispan is a data grid platform, complete with GUI management tooling, and the potential to scale to thousands of nodes. At the same time, Infinispan also fulfills the requirements of a clustered caching library, and even performs exceptionally as a standalone, non-clustered cache. To help existing JBoss Cache users, Infinispan provides for an easy migration path.

Have a look at this FAQ. High-performance custom marshaling layer to provide fast, low-overhead serialization and deserialization. A good place to start is to download the Infinispan distribution. The distribution includes a number of demo applications including a GUI-based one to visualize state moving around a grid. Download an Infinispan distribution. The GUI console should load up and you would see something like the frame below:.

Naturally, one instance is of limited use - things are more fun when you have several cache instances in a cluster. Invoking the runGuiDemo. For example, starting two more GUIs, we can see that the caches discover each other and form a cluster. You can start as many cache instances as you wish. You can now use one of the instances to generate data. You can see that this entry has been added to the cache: 8.

You will be able to retrieve this key from any of the frames in the cluster:. Alternatively you could download the distribution and get the necessary jar files there, for inclusion in your project. From that point on, you will be able to create instances of the cache and use it:. We maintain an online, interactive tutorial to walk you through the basic steps of creating a cache and using it.

For more examples, we recommend this as a good starting point, and it is probably a very good idea to have the Infinispan API documentation handy while you do this. Exploring the API in this manner is a great way to get up to speed using Infinispan really fast. This can be a useful starting point if you are considering Infinispan as a replacement for one of these cache systems. Information on these tools can be found here.

This is in the form of a WAR file which can be deployed in most Servlet containers. We have a more detailed guide on using the REST server. The current version allows you to visually monitor the health of your data grid and plots graphs of various metrics over time. While querying is only really scheduled for 4. Keep in mind that, as a tech preview, the querying interface and API are subject to change, but it does give you a feel for what to expect. Details about the tech preview of the Query API, along with instructions on usage and sample code, can be found here.

Synchronization and locking are kept to a minimum while delivering concurrent transaction isolation, and offering all of the other features of the platform, including a write-through CacheStore, eviction and JTA transaction compatibility.

The following clustered modes are supported:. A clustered, invalidated data grid is essentially a set of local, standalone caches which are aware of each other. When an entry is changed in any cache in the grid, the entire grid is made aware of the fact. Other nodes, if they happen to have cached the same entry as well, are aware that it is now out of date, and it will be invalidated. This low-cost invalidation message involves a multicast of the modified key s , and prompts remote caches to remove corresponding entries from their caches.

A replicated data grid is where each instance contains a replica of its neighbors. As such, any changes made to any instance is replicated across the entire cluster. This is useful if the cluster size is small and the entire cluster can benefit from having all of the state local and in-memory.

However, this operational mode does not scale in terms of memory, since adding more cluster nodes does not give you access to more addressable memory, and you are theoretically limited to the heap of a single JVM, discounting overhead. This is the default clustered operation mode in Infinispan, and makes use of a consistent hash algorithm to determine where keys should be located in the cluster.

Consistent hashing allows for cheap, fast and above all, deterministic location of keys with no need for further metadata or network traffic. The goal of distribution is to maintain enough copies of state in the cluster so as to be durable, but not too many copies so as to be scalable. As such, the number of copies of each entry maintained in the grid - the numOwners configuration attribute - is a configurable parameter that can be tuned, and represents the tradeoff between performance and scalability, and durability of data.

Regardless of how large the cluster is though, the number of copies is fixed. This means that such a setup scales linearly as nodes are added to the cluster. Further, capacity added is capacity realized, since adding more nodes means more usable memory can be addressed. For example, discounting for overhead, JVMs in such a cluster, with a heap size of 1GB each and setting numOwners 2 would give you GB of addressable memory in the entire system! With a distributed data grid, there is no guarantee that the instance you speak to locally holds the entry you are looking for.

The system may have to make a remote call to another cache node to retrieve the requested entry. While this remote lookup happens transparently, it has a cost associated with it. To minimize this cost in the event of repeated lookups on the same key, L1 caching can be enabled. L1 caching causes the requesting node to cache the retrieved entry locally and listen for changes to the key on the wire.

L1-cached entries are given an internal expiry to control memory usage. Enabling L1 will improve performance for repeated reads of non-local keys, but will increase memory consumption to some degree. Infinispan can be used for a number of purposes. Traditional cache usage - to front databases or other expensive, non-scalable data stores - is one. Use as a toolkit to cluster a container, framework or server by distributing on-the-fly state and allowing for failover is another common use case.

Such usage allows framework or server developers to create clustered offerings where state management is delegated to Infinispan and clients connected to such backends can gracefully fail over to another instance if one were to experience a failure. Increasingly, though, use as a primary data store in itself is gaining popularity, especially for unstructured or semi-structured data.

Due to the low-latency, high-concurrency and highly scalable nature of in-memory data grids, they have become popular in many applications that require the ability to scale on-demand, or to have fast, low-latency access to data.

Infinispan fits well with the NoSQL movement, which is gaining momentum, as well as cloud-deployments where traditional data stores are problematic. We know of several open source and proprietary products considering Infinispan as a part of their offering, and here are some that have reached a certain degree of maturity that may be of interest.

As of version 3. More details on this cache provider can be found here. More details can be found here. A formal user guide is in the process of being written. Expect this to be available soon, but in the meanwhile the wiki should be your primary source for information.

The wiki serves as a launchpad for more information on Infinispan, from design documents to FAQs , API docs to configuration references , tutorials to tips on contributing to the project. We use JIRA as a project issue tracker.

And of course you should follow the project on Twitter! Like our logo? Check out these cool desktop wallpapers the good people at JBoss. Have a look at this page , which details the resources available to anyone interested in participating in the project, along with information on how to get in touch with the development team.

Performance Zone. Over a million developers have joined DZone. Let's be friends:. Introducing the Infinispan Data Grid Platform. DZone 's Guide to. Free Resource. Like 0. Join the DZone community and get the full member experience. Join For Free. What is Infinispan? Current release: 4. Cache extends java. Customize your configuration as you wish.

Configuring your Cache The online configuration guide is your friend here. All elements in this file are optional with sensible defaults selected for any element omitted. Please refer to the configuration reference for details. HTTPConnection hostname conn. Querying the grid While querying is only really scheduled for 4.

Operational modes Here we discuss the different operational modes in more detail. The following clustered modes are supported: Invalidated data grid A clustered, invalidated data grid is essentially a set of local, standalone caches which are aware of each other.

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Introducing the Infinispan Data Grid Platform

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Article: "Introducing the Infinispan Data Grid Platform"

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Infinispan Data Grid Platform Definitive Guide

In-memory data grids IMDG have recently been gaining a lot of attention, and the market for this technology is steadily increasing. This book is a practical guide that covers all Infinispan features in a progressive manner with the help of real-world and ready-to-use examples. You will learn how to install Infinispan and create a perfect development environment. Finally, we will present a sample application that you can download from the JBoss Developer Framework. Skip to main content. Start your free trial. What You Will Learn Install Infinispan and create the perfect development environment Get acquainted with topologies and strategies for different situations and data access patterns Familiarize yourself with scaling techniques and concepts including distributed transactions, ACID, and database sharding Monitor your Infinispan instances by using RHQ or JMX clients Control and manage transactions using JTA and use the available APIs to manipulate your cache data Create an application called TicketMonster and learn how to implement Infinispan Learn about JGroups and how to use it with Infinispan for reliable communication Learn the concepts behind big data and how to work with Infinispan MapReduce API Show and hide more.

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