Overview

Distributed data caching allows system designers to optimize data access by situating the data close to the business logic - wherever that application logic might be located. GigaSpaces provides rich caching functionality using distributed memory resources – a GigaSpaces space functions as a cache instance, and clusters of spaces allow deployment of difference cache topologies.

The Need for Distributed Caching

Today's business systems must accommodate growing numbers of end-users accessing an ever-expanding set of business applications. Many IT organizations face the challenge of delivering the scalability and responsiveness needed for near-real-time applications.

Trying to solve the problem simply by adding more hardware is an expensive and often ineffective response. Hardware is almost always the brute force tool, but often a better alternative is a calibrated software solution. Distributed caching is one such solution. But while caching solutions provide scalability and data accessibility, they often struggle to meet the requirements of highly transactional applications, resulting in limited performance. In addition, Many applications require ways to utilize available memory resources, in order to boost performance.

GigaSpaces Cache Usage Scenarios

Different applications may have different caching requirements. Some applications require on-demand loading from a remote cache, due to limited memory; others might use the cache for read-mostly purposes; transactional applications need a cache that handles both write and read operations and maintains consistency.

In order to address these different requirements, GigaSpaces Cache is policy-driven. Most of the policies do not affect the actual application code, but rather affect the way each space/cache instance interacts with other instances. GigaSpaces cache policies support the following usage scenarios, among others.

Expand this...

Benefits of a JavaSpaces Cache

The JavaSpaces model was designed to address the broader issues in the distributed information domain, including caching, messaging, data sharing, data processing and distribution. This is all done using a single technology and a simple set of APIs. Following are the main highlights of JavaSpaces-based cache architecture.

Expand this...

Section Contents

• Data Grid Topologies — GigaSpaces Data Grid topologies.
  • All Data Grid Topologies — Deploying common topologies: replicated (sync/async), partitioned (with/without backup), ownership cluster.
  • Cache Topologies Comparison Table — A table comparing main supported topologies with different features provided.
  • Clusters Over WAN — Clustering, replication and partitioned replication between physically remote distributed sites.
  • Partitioned Space Topology — The partitioned topology provides the ability to form a space that has unlimited data storage capacity.
  • Space Local View — The space local view proxy maintains a subset of the master space's data, allowing the client to read distributed data without any remote operations.
• Managing Data Grid Instances Manually
  • Locating Clustered Space Instances — Using SpaceFinder to search for clustered space/cache instances in the network.
  • Running a Space Instance — How to run a space/cache instance in different modes, using SpaceFinder or CacheFinder.