WebSphere eXtreme Scale

Features and benefits

WebSphere eXtreme Scale provides the technology to enhance business applications, including web commerce, supply chain, financial, trading and on-line gaming, to form new, innovative classes of business applications by extending the data-caching concept with advanced features. These application enhancements can be realized by utilizing WebSphere eXtreme Scale in the following flexible scenarios and configurations:

• Key-based replication: Now, WebSphere eXtreme scale supports key-based replication scenarios, which are also referred to as multimaster or Availability Partition (AP) replication scenarios.
• Non-Java client supportC/C++, .NET: WebSphere eXtreme Scale allows non-native, non-Java client access to its caching features via REST APIs, implemented on ADO.NET Data Services V1 specification.
• Improved reporting and monitoring: WebSphere eXtreme Scale is instrumented to work with DB2 performance monitoring tools. Its statistics are enhanced to be queriable to better work with other monitoring consoles, and a native administrative console is now included.
• Simple Data and Database Cache: Applications can improve performance and throughput using the WebSphere eXtreme Scale configuration as a local cache. Caching the data in memory attacks what is often the largest part of a transactions processing time, waiting for the data to arrive. WebSphere eXtreme Scale enhances this traditional data-caching scenario even further by offering fail-over capability.
• Client/Grid with Near Cache: Having the data in memory is good. When dealing with large volumes of data, applications can perform even better. A Java Virtual Machine (JVM) can have a local eXtreme Scale grid which sits in front of a remote grid serving as a "near cache" for a subset of the data, allowing a client to leverage a very large remote cache to offload backend processing or to speed access to cached results. The near cache is in the same JVM as the application and provides local, in-process access to data. It contains a subset of all the data in the grid and is checked first when a record is requested. If the record is not in the near cache, then it is retrieved from the grid and put into the near cache. The response time is reduced the next time the same record is accessed. The faster response times for the records you access often leads to faster response time for the user. The near cache is also updated when data writes go to the grid. Applications can use the distributed locking services provided by the remote grid to coordinate access to shared data across clients.
• Side cache/Java Persistence API (JPA) cache plug-in: When applications need information that is used frequently but does not change often, like a users profile, a side cache offers significant performance benefits. WebSphere eXtreme Scale can be used as a side cache to store objects that have been retrieved from the backend. The application checks the side cache first to see if it contains a record. If theres a cache miss, then the data is retrieved from the backend and inserted into the cache.
• Real-Time Data and Event Mining: When working with real-time data flows, the first challenge is filtering and organizing the data so the applications can use it. A partitioned eXtreme Scale configuration can subscribe to events and apply them to partitioned data thus supporting linear scalability and predictable response times for these applications.
• Map/Reduce support: WebSphere eXtreme Scale clients can invoke agents that run against massive amounts of data on multiple nodes in parallel. Clients can then aggregate and further process the results stored in the grid by the nodes. This collocation of data and application logic helps in massive analytical and transaction processing operations.
• Moving application logic and the data in to the same address space provides zero latency access to data managed by the grid. This allows low latency applications to achieve sub millisecond response times for read/write transactions enabling a new class of applications that leverage elastic replicated in memory data grids instead of a traditional central database for extremely low latency response times and full elastic scaling with no bottlenecks.

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