Much like dimensions, each partition also has several properties that should be carefully examined and configured appropriately. #2: Define the Slice Property for Every Partition Personally, I prefer the second option, particularly for environments where I don’t have direct access to make schema changes in the relational source. Alternatively, you could also bind each partition’s definition to a different query.
You could define a separate view in the relational data source for each partition, each view retrieving only portion of fact table’s data. You have a couple of options for populating measure groups partitioned by multiple hierarchies. So go ahead and partition by multiple hierarchies when possible to match the pattern of data retrieval. Most cubes will have far fewer partitions, however. Theoretically, there is a limit to the number of partitions per cube≲ billion. Many people feel that they will end up with too many partitions if they partition data on any dimension other than time or date dimension. Scanning 15GB of data will invariably be slower than scanning a 1GB file. A query examining bike sales for 2009 will only have to read a single 1GB file. Now let’s split the data into 15 partitions of 1GB eachone for a combination of each year and category. If we store all data in a single 15GB partition, every query will have to examine this 15GB data file (presuming data is not found in an MSAS storage engine cache and no useful aggregations exist for resolving the query). Suppose we have data for five years and for three categories (bikes, accessories and clothing). Furthermore, if you partition your data according to the typical query patterns, you will see a far superior query performance than if your measure group had a single large partition.įor example, let’s suppose your measure group is partitioned by year and by product category. It is often more efficient to process five partitions of 20 million rows, in parallel, as opposed to processing a single partition with 100 million rows. Realize that during processing, MSAS has to read an entire partition’s data on a single thread. Some applications attempt to use much larger partitions, perhaps because data is partitioned only by month or by day.
Microsoft recommends limiting partition sizes so they contain up to 20 million rows or have a file size up to 1GB. #1: Abide by Prescribed Limits for Partition Sizes
The following sections offer recommendations for working with partitions and aggregations. Learn More Buy Recommended Practices with Partitions and Aggregations Recommended Practices with Partitions and Aggregations The EXCEPT query will show you the NULL value, and then I could see that it needed to be excluded from the distinct count on the fact table.Microsoft SQL Server 2008 Analysis Services Unleashed Key not in (Select Key from DimensionTable where KEY is not NULL) This is in fact a bad query because NOT IN doesn’t work where there a null values. Key not in (Select Key from DimensionTable) I was initially checking that my dimension contained a key that was required in a fact table using a query like this: If it does, these can be omitted from the measure group via a query in the partition definition (assuming the distinct count measure is in its own measure group)” “Errors in the OLAP storage engine: The sort order specified for distinct count records is incorrect.Ĭheck that the distinctcount column does not contain nulls. I had this issue, and your item (2) regarding NULL values was the answer. This doesn’t really make sense, because the point of a distinct count is that you expect there to be duplicate items that you only want to count once.
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