Thread count in a distributed service

Hi,
     i needed some information on the thread-count attribute in a distributed service. i would like to know how do we decide on the thread count. would increasing the thread count in the tangosol-coherence.xml increase the performance of tangosol as well.
     looking forward to your help
     Thanks
     Jigs

To go into this a bit further...
     Each service instance has its own primary thread. This thread has the option of using its own isolated thread pool if the thread-count is greater than zero. If the thread-count is zero, then all work will be performed by the primary service thread. If the thread-count is greater than zero, then all work will be performed by the thread pool (the primary thread acts as a task coordinator).
     Note that the thread-count is per-service and per-cluster-member. Each cache service has a unique name. The CacheFactory class uses a single cache service instance for each cache type (Replicated/Distributed/etc). If you manually create additional cache services, they will each have their own isolated thread pools.
     Additionally, each Invocation service has its own thread pool. This setting is very important as the Invocation service is quite often used to execute long-running user tasks. If the thread pool is saturated with user tasks, then further calls to the Invocation service will block until user tasks complete and threads become available. Using named Invocation service instances will allow you to isolate critical tasks for better application availability.
     Note that this information is current as of Coherence 2.5, but may change in future releases as threading models have a huge impact on performance and scalability. Having said that, there are no significant changes planned at this point in time.
     Jon Purdy
     Tangosol, Inc.
     =================
     Added 2004-12-02 by JP:
     Also, as a very specific instance, the backup copy(ies) of a distributed cache are created on the service thread ... this means that while backup copies are being created on a node, no new tasks will be dispatched to that service's thread pool. For in-memory backup copies, this is actually optimal as adding an item to a HashMap/HashTable is roughly as fast as handing it off to another thread. However, if the backup-copy is configured to disk-based storage (or other high-latency resource), an asynchronous backup implementation should be used to eliminate latency. See AsyncBinaryStore for more information.

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