![]() Scalability of throughput is also a direct result of load-balancing the data across all servers, since as servers are added, each server is able to utilize its full processing power to manage a smaller and smaller percentage of the overall data set. It accomplishes the scalability of data capacity by evenly balancing the data across all servers, so four servers can naturally manage two times as much data as two servers. Lastly, partitioning supports linear scalability of both data capacity and throughput. The result is linear scalability with constant latency, regardless of the size of the cluster. locality as the result of to sticky load balancing) are spread out evenly across the rest of the cluster, and all update operations (which must be handled remotely to ensure survival of the HTTP sessions) are likewise spread out evenly across the rest of the cluster. Additionally, related to performance, each read from a database has an associated latency, and that latency increases dramatically as the database experiences increasing load.Ĭoherence*Web, on the other hand, has the same latency in a 2-server cluster as it has in a 200-server cluster, since all HTTP session read operations that cannot be handled locally (e.g. asynchronous writes) and performance compromises. In both cases, the actual reads and writes per second that a database is capable of does not scale in relation to the number of servers requesting those reads and writes, and the database quickly becomes a bottleneck, forcing availability, reliability (e.g. ![]() Further, each HTTP request causes an update of its corresponding HTTP session, so regardless of sticky load balancing, to ensure that HTTP session data is not lost when a server fails the desired writes-per-second to the database will also increase linearly with the size of the server cluster. If the HTTP session were stored in the database, each HTTP request (in the absence of sticky load-balancing) would require a read from the database, causing the desired reads-per-second from the database to increase linearly with the size of the server cluster. Session management highlights the scalability problem that typifies shared data sources: If an application could not share data across the servers, it would have to delegate that data management entirely to the shared store, which is typically the application's database. NET Framework supports session management for Microsoft. ![]() Oracle Coherence Session Provider for the Microsoft. Oracle Coherence includes native integration of Oracle Coherence*Web Session Management Module for Oracle WebLogic Server, Oracle WebLogic Portal, and Glassfish Server, enabling distributed HTTP session management across multiple applications and heterogeneous environments while also allowing more data to be stored within sessions. Coherence*Web also includes new support for Coherence*Extend deployments, which isolates an application server tier from the session management tier, and results in improved availability for the system as a whole. In addition Coherence*Web includes a new optimistic session locking model that significantly increases throughput of web applications. Oracle Coherence*Web provides dramatic performance improvement, particularly for deployments that utilize large sessions and aggressive session expiry.
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