An Easy Way for SSAS Batch Processing

Batch processing is available in Analysis Services. Which means we can send multiple processing commands to the server in just a single SQL Server Job. With SSAS batch processing we can control which objects to be processed and in what order in a batch.  As batch processing reduces the amount of time taken to commit changes it offers better data availability. We can easily generate XMLA codes  for batch processing through SSMS (SQL Server Management Studio). You might see lots of discussions about this in other websites and lots of them are saying you need to right click on the objects one by one and generate the scripts. Then put all scripts together in another XMLA script. But it is such a pain when you have lots of objects that should be selected one after another to generate the batch processing XMLA. Sadly, it is not the end of the story. You need put all scripts together by copying and pasting the scripts several times. Today I want to show you a very easy to the job which saves lots of your time.

I’m using “Adventure Works 2012 Multidimensional” as an example and I’m going to batch process some dimensions.

  • Connect to the SSAS server from Management Studio
  • Expand the database
  • Expand dimensions

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Role-playing Dimension in SSAS Tabular Models

First of all I’d like to explain what a Role-playing dimension actually means. Then I’ll express the way you can implement it in a SSAS tabular model.

When you link a dimension to a fact table several times for logically distinctive roles you’re using a role-playing dimension.

The key points are:

1.       You are linking a fact table to a dimension multiple times. The relationships are defined by linking multiple foreign keys in the fact table to a single key in the dimension table.

2.       Each linkage represents a single role or concept

The most popular role-playing dimensions are DimDate and DimTime. Do you want to see how to implement Role Playing Dimensions in Power BI, Click here and here.

NOTE: The sample is from Microsoft “AdventureWorksDW” for SQL Server 2012 and might be different from your own data warehouse design.

For instance, in a sales system that you have something like FactInternetSales fact table which has several links, or relationships, to a DimDate or DimAddress for distinct concepts like “Order Date”, “Ship Date” and “Due Date”.

As you see, all of the above columns obviously represent different meanings of date. In the data warehouse design you’ll see something like this:

role-playing dimension 01

Although this is absolutely OK in the relational database layer, but, this sort of relationship is NOT permitted in the tabular model, so what should we do?

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How To Implement a Composite Key In SSAS Tabular Model

As you might know SSAS tabular models do not support composite keys so you always must have just one column to make a unique row through the whole table. This is such a pain especially when you are new to the tabular models and don’t have that much detail information about it. So when you import some tables with existing relationships based on composite keys, the Table Import Wizard will ignore those relationships.

So what should we do to solve the problem?

The solution is to combine the values of the composite keys. 

Here is how you can do the job?

·         Creating a view on top of the source tables:

1.  If you’re using SQL Server 2012 and above you can use the “concat” function to combine the values. The function combines several expressions regardless of their data types. So you can use it like this select CONCAT (1, 1.22100001,‘First’) SQL2012 and the result would be something like this

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2.  If you’re using earlier versions of SQL Server then you need to mind the data types. So for the above sample the SQL code would be select cast(1 as char(1)) + cast(1.22100001 as char(10))+‘First’ SQL2008 . As we expect the result is the same.

·         Adding a new computed column to all tables involved in SQL Server before importing the tables to the tabular model

·         Adding a new calculated column to all tables involved after importing the tables to the tabular model

As a quick note, you’ll need to remove the existing relationships imported from SQL Server and create the new relationship based on the combined keys.

Easy peasy!