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Category: MDX

Reducing Query Timeout on DAX and MDX Queries

Published 2025-02-04 by Kevin Feasel

Chris Webb shuts it down:

The recent announcement of Surge Protection gives Fabric/Power BI capacity admins a way to restrict the impact of background operations on a capacity, preventing them from causing throttling. However, at the time of writing, Surge Protection does not prevent users that are running expensive DAX or MDX queries – which are interactive operations – from causing problems on your capacity. Indeed, right now, there is no direct way to stop runaway queries from consuming a lot of CUs, although there is something you can do which will help a lot: reducing the query timeout.

Read on for information about why Surge Protection doesn’t currently work with DAX and MDX queries, and how you can change the query timeout. This is kind of interesting considering that, outside of the Microsoft Fabric world, we typically move the query timeout higher rather than lower, to deal with long-running queries.

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MDX vs DAX for Bulk Data Extraction from Power BI

Published 2025-01-27 by Kevin Feasel

Chris Webb performs a test but gives us a warning first:

This is a post I’ve avoided writing for many years, and before I carry on let me make one thing clear:

Doing bulk extracts of data from a Power BI semantic model is a **really** bad idea

My colleague Matthew Roche wrote a great post on this topic a couple of years ago that is still relevant: using Power BI (or Analysis Services) as a data source for other systems, including other Power BI Import mode semantic models, is an anti-pattern. Power BI is optimised for small, analytical queries that return the amount of data that can be visualised on a single page. It is not optimised for queries that return millions of rows.

After the warning, Chris still gives a performance breakdown for extracting data from a semantic model in Excel, using automated MDX and DAX.

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MDX For Beginners

Published 2016-03-01 by Kevin Feasel

Dan King continues his series on MDX:

So obviously we brought back internet Sales for all years captured into the cube.  So now we need to understand the WHERE clause and how to use it properly in MDX.

The “WHERE” clause in MDX is used to define another slicer and limit the attributes from the defined slicer. The slicer used in the “WHERE” clause cannot be the same slicer used when defining your rows hence why I use the date dimension.  The MDX query below depicts the WHERE clause and syntax:

I liked Dan’s introductory-level presentation on learning MDX, and this blog series is following along those same lines.

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MDX On DirectQuery

Published 2015-11-19 by Kevin Feasel

Chris Webb takes a look at MDX querying in SQL Server 2016 Tabular models:

There were a lot of limitations when using DirectQuery in SSAS Tabular 2012/4, but for me the showstopper was the fact that it only worked if you were running DAX queries against your model. Historically the only major client tool that generated DAX queries to get data was Power View, and Power View was/is too limited for serious use, so that alone meant that none of my customers were interested in using DirectQuery. Although we now have Power BI Desktop and PowerBI.com, which also generate DAX queries, the fact remains that the vast majority of business users will still prefer to use Excel PivotTables as their primary client tool – and Excel PivotTables generate MDX queries. So, support for MDX queries in DirectQuery mode in SSAS 2016 means that Excel users will now be able to query a Tabular model in DirectQuery mode. This, plus the performance improvements made to the SQL generated in DirectQuery mode, means that it’s now a feature worth considering in scenarios where you have too much data for SSAS Tabular’s native in-memory engine to handle or where you need to see real-time results.

Good stuff.  Read the whole post, especially if (unlike me) you know a thing or two about MDX.

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