Basic query optimalization in DAX

What can we do to make queries as fast as possible? Here is some tips 🙂

In my simple test I want to see the sales for year 2009. To make sure my queries does not hit any cache I’m clearing it using XMLA. In my first query I will try to use the ROW function and sum up the internet sales

EVALUATE
ROW (
"Sales", CALCULATE (
SUM ( 'Internet Sales'[Sales Amount] ),
'Date'[Calendar Year] = 2008
)
)

When watching the profiler I can see that this query took 156 milliseconds. The reason why this takes quite long is that we are one the row level.

Lets switch to table level.

EVALUATE
FILTER (
SUMMARIZE (
'Date',
'Date'[Calendar Year],
"Sales", SUM ( 'Internet Sales'[Sales Amount] )
),
'Date'[Calendar Year] = 2008
)

This query took 78 milliseconds. Twice as fast as the first query.

What if I move the calculation using ADDCOLUMNS outside the SUMMARIZE?

EVALUATE
ADDCOLUMNS (
FILTER (
SUMMARIZE ( 'Date', 'Date'[Calendar Year] ),
'Date'[Calendar Year] = 2008
),
"Sales", CALCULATE ( SUM ( 'Internet Sales'[Sales Amount] ) )
)

This query took 62 milliseconds

Do you have an even better optimalization tip? Feel free to drop me an mail

How to use “RANKX”

If you want to rank your result in DAX you can use the RANKX function.

Below is a sample

DEFINE
MEASURE 'Internet Sales'[Rank] =
RANKX (
ALL ( 'Product'[Product Name] ),
SUMX (
RELATEDTABLE ( 'Internet Sales' ),
'Internet Sales'[Internet Total Sales]
)
)
EVALUATE
FILTER (
ADDCOLUMNS (
VALUES ( 'Product'[Product Name] ),
"Product Rank", 'Internet Sales'[Rank],
"Sales amount", 'Internet Sales'[Internet Total Sales]
),
[Sales amount] <> 0
)
ORDER BY 'Internet Sales'[Rank]

Please note the filter that removes the empty rows.

Here is the resultset

Rank