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Overview GemFire is a high performance distributed data management infrastructure that sits between application cluster and back-end data sources. With GemFire, data can be managed in-memory, which makes the access faster.
Spring Data provides an easy configuration and access to GemFire from Spring application. Maven Dependencies To make use of the Spring Data GemFire support, we first need to add the following dependency in our pom. GemFire Basic Features 3. Cache The cache in the GemFire provides the essential data management services as well as manages the connectivity to other peers. The cache configuration cache. Regions Data regions are a logical grouping within a cache for a single data set.
Simply put, a region lets us store data in multiple VMs in the system without consideration to which node the data is stored within the cluster. Regions are classified into three broad categories: Replicated region holds the complete set of data on each node. It gives a high read performance.
A copy of the data is stored on one of the other nodes. It provides a good write performance. There is no connectivity with other nodes within the cluster. This is very similar to SQL in syntax.
Data Serialization To manage the data serialization-deserialization, GemFire provides options other than Java serialization that gives a higher performance, provides greater flexibility for data storage and data transfer, also support for different languages.
PDX is a cross-language data format that provides a faster serialization and deserialization, by storing the data in the named field which can be accessed directly without the need of fully deserializing the object. Function Execution In GemFire, a function can reside on a server and can be invoked from a client application or another server without the need to send the function code itself. The caller can direct a data-dependent function to operate on a particular data set or can lead an independent data function to work on a particular server, member or member group.
Continuous Querying With continuous querying, the clients subscribe to server side events by using SQL-type query filtering. The server sends all the events that modify the query results. The syntax for a continuous query is similar to basic queries written in OQL. Spring Data GemFire Support 4.
Here mcast-port is set to zero, which indicates that this GemFire node is disabled for multicast discovery and distribution. Entity Mapping The library provides support to map objects to be stored in GemFire grid. Function Execution Support We also have annotation support available — to simplify working with GemFire function execution.
There are two concerns to address when we make use of functions, the implementation, and the execution. The complete code for this article is available over on GitHub.
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Getting Started with Pivotal GemFire
Running the Tutorial
Introduction to Cache Management
A Guide to GemFire with Spring Data