Distributed Computing Scalable Architecture with GigaSpaces Elastic
Caching Edition

Open Interfacing Layer

Supports multiple languages and frameworks

Achieve easy migration, reduced learning curves, and faster time to market by leveraging existing skillsets such as Spring, Java, JPA, .Net, and C++.

Virtualized Deployment Infrastructure

Any environment, anytime, anywhere – traditional data center, public/private cloud, or hybrid

Isolate the runtime environment, physical address, and platform type from your data grid. The system takes care of provisioning your data grid instances onto the best available resources, and self-adjusts to maintain utilization levels as machine availability changes over time.

In-Memory Data Grid

Provides high performance, dynamic linear scalability, and always-on availability

Query Processor: Enables applications to query data using simple as well as complex SQL-like queries.
Memory Manager: Sets upper limits on memory usage in your cluster nodes, with automatic handling of overflow
Cluster & Replication Layer:
- Flexible topologies enable you to arrange distributed cache in partitions, replicated units, or a combination of both.
- Data can be replicated over geographically distributed deployments.
- Entire cluster can be transparently accessed from any machine.
Discovery & Communication
- Enables cluster members to automatically discover other members; unicast and multicast discovery support.

Easy Application Server Clustering with GigaSpaces XAP Elastic Caching Edition

Overcome Relational database built-in performance limitations

Store your data in memory (in-memory data grid as system of record)
Replicate & partition your data
Write-behind to the DB

The GigaSpaces In-Memory Data Grid (IMGD) supports the follwing deployment topologies:

All the topologies support co-locating business logic with any of the IMDG instances, enabling fast data processing and eliminating serialization and network overhead once data is accessed.

Fully replicated: Each member contains all of the data. Replication between nodes is done synchronously or asynchronously
Partitioned: Each node contains a different subset of the data
Partitioned: Each node contains a different subset of the data.Regardless of the IMDG cluster deployment topology, a client can run a near-cache (called local cache/view).