Distributed tuning job count
Enables distributed parallel tuning, which can significantly increase
the performance of the tuning tool. A value of
n causes the
tuning tool to distribute tuning work among
n parallel jobs.
These jobs are distributed among a set of machines. Use the
WorkerPool parameter to
provide a distributed worker cluster.
Note that distributed tuning is most effective when the worker machines have similar performance. Distributed tuning doesn't attempt to normalize performance by server, so it can incorrectly attribute a boost in performance to a parameter change when the associated setting is tried on a worker that is significantly faster than the others.
One important note about integer-valued parameters: while the maximum value that can be stored in a signed integer is , we use a MAXINT value of 2,000,000,000. Attempting to set an integer parameter to a value larger than this maximum will produce an error.
For examples of how to query or modify parameter values from our different APIs, refer to our Parameter Examples.