![]() ![]() Additionally, you can have SQL Server Analysis Services convert the null value to a designated value by configuring three properties: the UnknownMember and UnknownMemberName properties for the dimension, and the NullProcessing property for the dimension's key attribute. You can modify the default settings or convert null values in your extract, transform, and load process (if any) of the underlying relational data warehouse. If SQL Server Analysis Services encounters a null value during processing, by default, it converts this null to a zero for numeric columns or to an empty string for string columns. However, there is some information that SSD usage can speed it up on the hardware level - I haven’t tested it yet and I don’t usually use the non-addictive aggregations though.When SQL Server Analysis Services processes a dimension, all the distinct values from the underlying columns in the tables, or views in the data source view, populate the attributes in the dimension. When costumers complain about performance, I’m adding a new measure group, but it slows your processing time down. ![]() ![]() Personally, I prefer the MDX way, it’s more flexible and easier to use. There are two ways: create a new table/view without the NULL values or set it in the partition section. I don’t know the reasoning, but DistinctCount doesn’t support the Preserve option in the NullProcessing property. DistinctCount is forced to be created in the separate measure group anyway. If performance matters to you, then you have to create the measure physically, for sure.īeware: if you are using a fact table containing the NULL values on the DistinctCount column, you have to filter them out. It becomes slow in some rough cases due to using the Set within Sum function which you have to avoid for smooth performance. The only disadvantage, I see, is performance. > 0, 1, NULL ) )Īs you see, you may dynamically add any conditions you want without any changes in the cube.ĭisadvantages. Furthermore, I have already mentioned that in my previous post. However, I recommend using SUM+IIF combination for an improved performance. The first idea is that usually you come up with something like the following code: Count( NonEmpty(. Tip: if you already have an attribution, add an extra one with the same key, filed and set AttributeHierarchyVisible to false, in order to use it for the MDX calculations only (I’ll explain the reasoning behind it in another blog post). You have to have an attribution you‘ll go through the count and any addictive measure with the required relationships. In my opinion, there are two best practices for the DistinctCount measures. But what about filtering by products if there is no product information in the header table? So, we just create a fact table with the headers of invoices. Let’s say we want to count the number of invoices. Sometimes we can avoid using the DistinctCount aggregation: simply replace it with Count aggregation. But, it is not as easy as it seems at the first glance. It means that in order to add a new value you have to check whether the set is not containing the value already. The DistinctCount usually is used for the non-numeric strings, like user, client, day or whatever you want to count. The same way SSAS handles Count, MAX, MIN meanwhile the DistinctCount is different. You do not need to know the full set of values from the already processed rows. For example, your aggregation is SUM, your current value is 11 and you want to add a new row, containing value 2, what means that the re-aggregated value will be 13. What does it mean?įirst of all, you cannot simply replace with a new value just by knowing the previous one. The DistinctCount aggregation is a semi-addictive aggregation. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |