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Rekindling a GemFire


Distributed data manager sees cache, partitioning updates

By P. J. Connolly
November 15, 2007 — 
There’s been so much talk about the ways application development has to evolve in the modern era of multicore hardware and virtualized systems that it’s easy to forget data has its own issues to consider. This gets especially tricky as companies seek to build distributed, resilient data architectures as part of a next-generation disaster recovery scenario.

GemStone Systems announced on Oct. 30 the release of GemFire Enterprise 5.1, a distributed operational data management infrastructure that allows data sharing and event distribution in environments where clustered application processes need reliable and secure access to back-end data sources.

GemStone architect Jags Ramnarayan explained that the large number of variables inherent to distributed systems significantly increase the possibility of error. Such stress-inducing points range from missed event notification to data consistency failures, all the way to application, operating system or hardware failure.

GemStone claims to have improved the redundancy of data partitioning in GemFire Enterprise 5.1 by allowing multiple levels of redundant data. The partitioned data regions can be managed with user-defined policies, allowing events to be properly ordered without requiring locks or similar steps. This, the company claims, allows all data updates to be fed through the primary partition, for a balanced memory usage profile.

Evening Out Events
Event delivery is important in a clustered data environment, and GemFire Enterprise uses distributed event notification to permit the even spread of updates across a data grid for event processing. It also allows the use of Object Query Language as a way to execute “scatter-gather” algorithms.

Users can control the way a workload is spread across the system, parallelizing data traffic to peers in an attempt to provide better throughput with multihomed nodes. So-called “lazy” connections are also possible.

The persistence implementation is designed to allow every operation to be recorded, with log files designed to roll over at a configured size. The system also pulls log files back together to reclaim disk space, which the company claims results in a 50 percent gain in throughput for synchronous persistence, and full 100 percent gain for asynchronous persistence.

GemFire Enterprise 5.1 also implements new native C++/C# client cache behaviors designed to enhance data sharing and cross-application collaboration, important in today’s distributed environments. Since objects in the cache aren’t always the same size, it’s possible for a client cache to become fragmented; the GemFire update implements heap LRU (least recently used) algorithms at the cache level to alleviate this.

GemFire clients execute queries on the server side and are subscribed to event notifications; according to the company, this keeps client-side performance from bottlenecking the cache server and impeding a data grid’s ability to scale.


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