Coherence Query Performance in Large Clusters

Large clusters (measured in terms of the number of storage-enabled members participating in the largest cache services) may introduce challenges when issuing queries. There is no particular cluster size threshold for this, rather a gradually increasing tendency for issues to arise.


The most obvious challenges are that a client's perceived query latency will be determined by the slowest responder (more likely to be a factor in larger clusters) as well as the fact that adding additional cache servers will not increase query throughput if the query processing is not compute-bound (which would generally be the case for most indexed queries).


If the data set can take advantage of the partition affinity features of Coherence, then the application can use a PartitionedFilter to target a query to a single server (using partition affinity to ensure that all data is in a single partition).


If this can not be done, then avoiding an excessive number of cache server JVMs will help, as will ensuring that each cache server has sufficient CPU resources available and is also properly configured to minimize GC pauses (the most common cause of a slow-responding cache server).

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The primary contributors to this blog are comprised of the Exalogic and Cloud Application Foundation contingent of Oracle's Fusion Middleware Architecture Team, fondly known as the A-Team. As part of the Oracle development organization, The A-Team supports some of Oracle's largest and most strategic customers worldwide. Our mission is to provide deep technical expertise to support various Oracle field organizations and customers deploying Oracle Fusion Middleware related products. And to collect real world feedback to continuously improve the products we support. In this blog, our experts and guest experts will focus on Exalogic, WebLogic, Coherence, Tuxedo/mainframe migration, Enterprise Manager and JDK/JRockIT performance tuning. It is our way to share some of our experiences with Oracle community. We hope our followers took away something of value from our experiences. Thank you for visiting and please come back soon.

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