Reverse Engineering The Stream Aggregate Algorithm

Itzik Ben-Gan has started a series of articles on optimizing queries which use grouping and aggregating with a reverse-engineering of the stream aggregate algorithm:

As you may already know, when SQL Server optimizes a query, it evaluates multiple candidate plans, and eventually picks the one with the lowest estimated cost. The estimated plan cost is the sum of all the operators’ estimated costs. In turn, each operator’s estimated cost is the sum of the estimated I/O cost and estimated CPU cost. The cost unit is meaningless in its own right. Its relevance is in the comparison that the optimizer makes between candidate plans. That is, the costing formulas were designed with the goal that, between candidate plans, the one with the lowest cost will (hopefully) represent the one that will finish more quickly. A terribly complex task to do accurately!

The more the costing formulas adequately take into account the factors that truly affect the algorithm’s performance and scaling, the more accurate they are, and the more likely that given accurate cardinality estimates, the optimizer will choose the optimal plan. At any rate, if you want to understand why the optimizer chooses one algorithm versus another you need to understand two main things: one is how the algorithms work and scale, and another is SQL Server’s costing model.

So back to the plan in Figure 1; let’s try and understand how the costs are computed. As a policy, Microsoft will not reveal the internal costing formulas that they use. When I was a kid I was fascinated with taking things apart. Watches, radios, cassette tapes (yes, I’m that old), you name it. I wanted to know how things were made. Similarly, I see value in reverse engineering the formulas since if I manage to predict the cost reasonably accurately, it probably means that I understand the algorithm well. During the process you get to learn a lot.

Our query ingests 1,000,000 rows. Even with this number of rows, the I/O cost seems to be negligible compared to the CPU cost, so it is probably safe to ignore it.

As for the CPU cost, you want to try and figure out which factors affect it and in what way.

I give this my highest recommendation.

Related Posts

Aggregate Pushdown with GROUP BY

Paul White takes us through several performance improvements around aggregate pushdown: SQL Server 2016 introduced serial batch mode processing and aggregate pushdown. When pushdown is successful, aggregation is performed within the Columnstore Scan operator itself, possibly operating directly on compressed data, and taking advantage of SIMD CPU instructions. The performance improvements possible with aggregate pushdown can be very […]

Read More

Power Query Container Size and Performance

Chris Web looks into what changing the Power BI Dataflow container size does for us: Currently there is no way to change this 256MB in Power BI Desktop or Excel although someone has already posted a suggestion on the Ideas site to allow us to change it. How much of an impact does this actually […]

Read More

Categories

April 2018
MTWTFSS
« Mar May »
 1
2345678
9101112131415
16171819202122
23242526272829
30