You’ll end up erasing all the things and redo all your parameterization, like format, type, aggregations … which is not really handy especially when you’re not the primary developer of the cube to do some performance tuning. If you start by creating all measures and then change some of them to be distinct count measures, no new measure group will be created. This article will look at a calculation and how SSAS can help centralize the logic in cubes and tabular models. This will automatically create a new measure group for the same fact table. Be sure to change their aggregation type accordingly, as for numeric measures the default aggregation type is the “sum” function.Ģ) Then, drag into the pane all the other measures of the fact table. Then drag and drop the fields from the table that you want to become distinct count measures into the measure groups metadata pane. To create distinct count measure groups, you need to proceed in two simple steps :ġ) First, start with a fresh fact table that has still no measure group associated. This allow to manage partitioning and aggregations more accurately in a manner that suites better those kind of measures. In fact, you have to perform the steps in the correct order or you ll find yourself stuck and need to redo the thing from the beginning…Īccording to the msdn, a SSAS fact table can only have one measure group associated except for the distinct count measures that can be separated from other measures and have their own measure group. You can find more tips and tricks at my blog,. You can use a hash to distribute the users. For example, you may want to see total sales of a product as it accumulates over time, or for inventory models the total on hand at a given time. Getting a distinct count for another dimension. Thats why the distinct count is created separately and you need to create partitions with the same number of rows. Some times ago (who says years ?), I found myself stuck with this one, as it is not really straightforward into the SSAS BIDS cube designer how to achieve it. 06-13-2016 02:49 PM A common Measure that you’ll probably find useful in PowerPivot or SSAS Tabular Models is finding running totals. I even think that most of you may already know about it…. This is no rocket science, just a little tip that every one would solve after spending 5 minutes on it. A standalone count of basket ids is around a minute and some more complex drilldown queries can take 2.5- 5 mins.Having blogged last week here about the advantages of the new storage file format found in the Denali’s CTP3 for those kind of measures when they are of the string data type, remember me that maybe this little feature may need a post. Physical RAM is OK at 128GB on the server. I don't think I can do many to many, because whilst a Customer can have multiple baskets, a basket cannot have multiple customers (please correct me if wrong). I have been running usage based optimisations on aggregations which helps bit by bit, but nothing hugely impacting. I wonder if anyone has any tips on where to look to boost performance. The number of distinct basketID s in my dataset is nearly 32 million. There can then be multiple rows of the same basketID / customerID together with another ID for the item in the basket. each row contains a basketID and a customerID. The performance is therefore incredibly slow as a physical distinct count, particularly when I introduce dimension hierarchies. 1 I have a cube where there is a single fact table about online customer orders. ![]() The number of distinct basketIDs in my dataset is nearly 32 million. There can then be multiple rows of the same basketID/ customerID together with another ID for the item in the basket. ![]() This creates a measure group from a dimension, in other words, dimension DimUsers now acts as a dimension as well as a measure. each row contains a basketID and a customerID. Distinct Count: Next, I created a new measure from the measures pane, configured usage to count of rows, and source table to DimUser. I have a cube where there is a single fact table about online customer orders.
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