Pivotal
GemFire

Scale Out Your NoSQL Applications

Data-driven applications at web scale

  • Benefits
  • Features
  • Use Cases

Perform at Real-Time Speed

Create NoSQL applications that operate in real-time speed at any scale, thanks to distributed in-memory technology. Elastically stretch your scale when your applications need to serve expected or viral peaks in demand. Ensure optimal usage of system resources to meet that demand. Scale to a multi-site global grid.


Stay Consistent & Resilient at Scale

Run transactional applications at any scale. Your data stays consistent across distributed nodes. Grid-optimized queries and powerful database operations allow you to create low latency complex data operations in your application. Node failover and cluster self healing ensure resiliency of your application for mission-critical requirements.


Build Powerful Data-Centric Applications

Take advantage of web-scale functionality without changing your code. Or persist and operate on JavaScript Object Notation (JSON), user-defined objects and complex graphs in transactional applications. Leverage an asynchronous event framework with continuous query for low latency detection algorithms. Enhance the scale of applications written in Java, C++, C# or using Representational State Transfer (REST).




Case Study: China Railway

3.5 billion Chinese travelers. Just one ticketing system.

Read Case Study

GemFire Deployment Topologies

GemFire is flexible and can be deployed in any of these configurations.



Features

In-Memory Data Grid

Pivotal GemFire® stores all operational data compressed and in-memory to avoid disk I/O time lags. Nodes operate in a cluster, optimizing data distribution and processing, to ensure the highest speed and balanced utilization of system resources. Pivotal GemFire scales elastically and linearly – adding nodes increases capacity predictably.


ACID, Low Latency Database Operations

Data is durable through in-grid replication and persistent write-optimized disk stores. Multiple checks ensure the transactional consistency of data. To minimize latency, grid-aware database queries and operations are routed to nodes holding relevant data for processing. Triggers and event notifications provide real-time reaction capabilities as data comes into the system.


High Availability, Resilience & Global Scale

Pivotal GemFire clusters provide automatic fail-over to other nodes in the cluster in case of failures. To keep data accessible, while ensuring data stays consistent, grids resiliently rebalance and reform if nodes leave or join the cluster. Clusters of Pivotal GemFire can be connected via wide area network (WAN) to implement multi-site, global-scale and disaster recovery deployments.


NoSQL Data Management in Multiple Languages

Pivotal GemFire supports user defined object models in complex graphs, as well as documents in JSON format. Data can be accessed in native clients for Java, C++ and C#, as well as applications supporting REST calls. APIs implemented include Java:Hashmap, Spring Data GemFire and Memcached.


Powerful Data Application Features

Pivotal GemFire allows you to query data using Object Query Language (OQL). Custom procedures written in Java are stored and executed in relevant nodes, where pertinent data is stored. A reliable asycnhronous database event framework provides publish and subscribe capabilities, call back functions for custom processing, along with support for continuous query.


Easy Administration of Distributed Data Grid

To optimize performance and system resource utilization, Pivotal GemFire is built to automate many administrative tasks – including self-healing of clusters when nodes join and distribution of data across nodes. Pivotal Gemire tools include a cluster status dashboard, offline performance analysis and command line interfaces to support automation scripting.



Use Cases

Enterprise-Wide Cache

Pivotal GemFire is often employed to provide enterprise-wide caching of application data, spanning multiple clouds or data centers.

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Elastic In-Memory Computing

This use case involves computation-intensive processing of large volumes of data with extremely high output and response requirements in seconds or less. Examples include market-risk analysis, web-scale partner electronic pricing and inventory analysis.

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Real-Time Transactional Applications at Scale

These are applications that need to provide real-time responses and maintain consistency of data while processing many parallel transactions, often with high availability. Examples include reservation systems and post-trade processing.

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Elastic Stream Processing

When data from incoming variable high-velocity streams absolutely must be stored, perform advanced processing including real-time analytics and matching.

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Case Study

Newedge Delivers Enhanced Post-Trading Platform Using Pivotal


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