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-----Original Message----- We have a cube which is partitioned by month, and receives new data files once a day. Data is loaded into a "holding" table, a temporary partition is build based on that holding table, and then the temporary partition is merged into the current month. This process had been working just fine for the past year or more. Recently, we started having problems with query performance for particular queries, and decided to switch to usage-based optimizations (replaced the current 500 aggregations with 68, and got a higher percentage). This has worked out just fine for the query performance, but now we've started having problems while merging the temporary partition into the current month. Normally, the merge step has been taking between 10 & 20 minutes to perform per day. Now what's happening is that most of the time, the merge step never finishes. If I watch the OLAP data folder, I can see the temporary partition files being created, I can see the "'S" files being created & growing as they normally would during the merge, but at the point in time where they would normally disappear (replacing the current month's partition), they don't. The Analysis Services service keeps using 50-100% of the CPU, but nothing ever happens (I've waited as long as 32 hours). None of the files in the OLAP data folder are updated beyond the 10 minutes after the merge started, and the OLAP service is doing something the entire time. I've tried rebuilding the partitions, deleting the cube (and ensuring that all files were deleted from the OLAP data folder), and rebuilding from a script, but the same thing keeps happening. Worse yet, it will sometimes work correctly. Usually after I've rebuilt the current month, I can get 1 or 2 files to process, but then the same problem comes back. I've spent my last 2 weekends rebuilding this cube, and it's getting old real fast. It's a fairly large cube, taking 10 hours/per month to process, and currently containing 4 months. Has anyone seen this type of behavior? Any help would be greatly appreciated. . |
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