![]() ![]() Process Incremental doesn’t drop aggregations and indexes. ![]() A Process Incremental internally creates a temporary partition, processes it with the target fact data, and then merges it with the existing partition. But you can only do a Process Incremental if there are new records for the partition (no updates or deletes), as a Process Incremental never deletes or updates existing members, it only adds new members. There is one more option to consider, a Process Incremental, which can be used in place of all the above steps. As another option, instead of doing a Process Index, you can do a Process Default, which will evaluate the state of all the partitions: for one’s that had a Process Full it will ignore them for one’s that did not process, it may or may not touch them (it will if they were part of a dimension and the aggregations were dropped, in which case it will rebuild those aggregations and indexes via a Process Index but it won’t reprocess the data) for one’s that had a Process Data it will build out the aggregations and indexes (Process Index). However, the best practice is to do a Process Data and Process Index separately instead of a Process Full, because: it is a bit faster, it reduces the stress on the server, it makes data available to end-users sooner (while a Process Index is happening, users can still query cube), and you can just run Process Index if Process Data completes but Process Index bombs. Note instead of doing step #2 and step #3 separately you could just do a Process Full (which does a Process Data and Process Index behind the scenes). Process Index for the rest of the partitions (the partitions that have not changed). Note that Process Data processes data only without building aggregations or indexes.ģ. Of course, the smaller the partition, the better, so try to use daily partitions instead of monthly or use monthly partitions instead of yearly. Process Data the partitions that have changed data (which are usually the most recent partitions). This means after a Process Update you need to do a Process Index on the partitions (see step #3).Ģ. ![]() The cube is still available for queries, albeit with lower performance (with Process Update flexible aggregations and indexes on related partitions will be dropped). But if members were deleted or if member relationships changed (e.g., a Customer moved from Redmond to Seattle), then some of the aggregation data and bitmap indexes on the partitions are dropped. If only new members were added, then the partitions are not affected. Depending on the nature of the changes in the dimension table, Process Update can affect dependent partitions. Process Update all the dimensions that could have had data changed. Regarding the best possible processing strategy, I suggest the following steps:ġ. Note that partitioning requires the enterprise version of SQL Server 2008 ( view version differences). This can greatly reduce the total cube processing time. The biggest benefit of partitioning is that it allows you to process multiple partitions in parallel on a server that has multiple processors. So can using a different processing strategy than a Process Full. Partitioning the cube can help to reduce the processing time. This is especially true as more and more companies want cube processing during the day instead of the usual off-hours time when no one is using the cube. As your SSAS cube gets bigger, cube processing time will become a problem. ![]()
0 Comments
Leave a Reply. |