Month: February 2016

Tara Kizer sees a difference between the stored procedure cached time and an individual SQL statement’s cached time:

The app’s plan was cached the day before. But wait a second! My assumption was that it had recompiled this morning due to the updated stats.

Also check out Aaron Morelli’s link to an older blog post on how the plan cache works.

Steve Jones compares writing a function versus using CROSS APPLY to write the same function:

The conclusion I’d take here is that CROSS APPLY ought to be a tool you keep in the front of your toolbox and use when you must execute a function for each row of a set of tables. This is one of the T-SQL  techniques that I never learned early in my career (it wasn’t available), and I haven’t used much outside of looking for execution plans, but it’s a join capability I will certainly look to use in the future.

I’m one of the biggest fans of the APPLY operator out there—my favorite talk is based on it, even.  But in this case, I’m going to say that writing “CROSS APPLY” really didn’t do anything here—times are similar enough that I’d be suspicious that the database engine is doing the same thing both times.

Gail Shaw has a follow-up with more query “tricks” that aren’t fooling anyone:

In a similar vein to last week’s blog post… I heard an interesting comment recently. “Change that Column != 2 to a Column > 2 or Column < 2 combination, it can use indexes better.”

Sounds like something that clearly needs testing!

Not shockingly, this did nothing to make the query run faster or use fewer resources.  There are ways to rewrite queries to improve performance while maintaining the same result structure (a common example being rewriting query using a cursor or WHILE loop to perform one set-based operation), but Gail’s point is vital:  test your changes and make sure that if you’re saying it will perform better, that it actually perform better.

Dan Holmes points out a quirk of estimated row counts with CLR-based functions:

That is an enormous amount of data.  What if you needed to sort that?  What if you joined this to another table or view and a spool was required.  What it it was a hash join and a memory grant was required?  The demand that this seemingly innocuous statement placed on your server could be overwhelming.

The memory grant could create system variability that is very difficult to find.  There is a thread on MSDN that I started which exposes what prompted this post.  (The plan that was causing much of the problem is at this link.)

It’s important to keep in mind the good enough “big round figures” that SQL Server uses for row estimation when stats are unavailable (e.g., linked server to Hive or a CLR function like in the post).  These estimates aren’t always correct, and there are edge cases like the one in the post in which the estimates will be radically wrong and begin to affect your server.

Aaron Bertrand uses a Visual Studio Online outage to talk about query hints:

These are all things that may have been necessary under the old estimator, but are likely just tying the optimizer’s hands under the new one. This is a query that could have, and should have, been tested in their dev / staging / QA environments under the new cardinality estimator long before they flipped the switch in production, and probably could have gone through series of tests where different combinations of those hints and options could have been removed. This is something for which that team can only blame themselves.

Also check out Aaron Morelli’s comment on the post.