Computed Column Performance

Paul White has a great article on when computed columns perform poorly:

A major cause of poor performance is a simple failure to use an indexed or persisted computed column value as expected. I have lost count of the number of questions I have had over the years asking why the optimizer would choose a terrible execution plan when an obviously better plan using an indexed or persisted computed column exists.

The precise cause in each case varies, but is almost always either a faulty cost-based decision (because scalars are assigned a low fixed cost); or a failure to match an expanded expression back to a persisted computed column or index.

The match-back failures are especially interesting to me, because they often involve complex interactions with orthogonal engine features. Equally often, the failure to “match back” leaves an expression (rather than a column) in a position in the internal query tree that prevents an important optimization rule from matching. In either case, the outcome is the same: a sub-optimal execution plan.

Definitely read the whole thing if you’re thinking about setting trace flag 176 on.

Related Posts

Nested Loops, Hash, Or Merge: Which Is Best?

Grant Fritchey dodges the important questions: First response, also a joke, was the question at the title of this post: What is the preferred operator when joining tables: Hash Match, Nested Loops or Merge? While my immediate response to this question is, yes. Meaning, they’re all preferred, situationally. I decided to expand on that a […]

Read More

Parallelism Strategies For Grouping Operations

Itzik Ben-Gan continues his series on grouping data in SQL Server by looking at how these operations can go parallel: Besides needing to choose between various grouping and aggregation strategies (preordered Stream Aggregate, Sort + Stream Aggregate, Hash Aggregate), SQL Server also needs to choose whether to go with a serial or a parallel plan. […]

Read More

Categories