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I have a MOLAP cube with a customer dimension containing 1.3 million records. 500,000 are used in the 50 mil. row fact table, the rest are "potential" customers having demographic data. Quite often reports will include the dimension won't do any filtering on it. The resulting set will be a managable size, but using crossjoins and even nonemptycrossjoins takes much longer than a SQL query would, if it even finishes. As an example, I am working on a query that will produce a flattened sales comparison matrix for Reporting Services: Store Customer ProductGrp Period1 Period2 Change Store1 Customer1 ProductGrp1 2 5 3 Store1 Customer2 ProductGrp1 6 5 -1 Store2 Customer1 ProductGrp1 0 5 5 I can write a SQL version for the Cube's source database that runs in a few minutes, but its counterpart in MDX using a NonEmptyCrossJoin is rediculously expensive in time and resources. Would maxing out creation of aggregations help? Any other suggestions? The separate msmdvldm process isn't used in the current cube, containing only the 500,000 relevant customers. Thanks, Tom McLeod |
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