Parse Query Plans

Richie Lee writes some C# code to parse query plans:

XPath is the bane of my life… it takes a while to find the correct value particularly as there are so many node names that are re-used yet embedded into them. So I added a simple example and a couple with more depth. Nevertheless, running the test should produce a green result. This could be used for more than just testing; DBA’s may find it useful in SQLCLR scenarios.

I’ve uploaded the code to GitHub. The repository is calledXQueryPlanPath.

9/10 would prefer F#.

Seriously, though, this is a nice start if you need to dig into execution plans programmatically.

TOP 1 Tuning

Kenneth Fisher has a good case study on tuning with the help of Rob Farley:

Here we are looking at the difference between the estimated and actual number of rows for an element of the plan. To look at this information you can either mouse over the element or right click and open the properties tab. In this case you will see that the estimated number of rows (what the optimizer thought would happen) is fairly low (117) particularly compared to what actually happened (1494900). When you see a big difference like that in a query plan there is something wrong.

This is a really nice and detailed walkthrough in which Rob plays Socrates and Kenneth your favorite of the group (Thrasymachus anyone?).

Faking Reads And Writes

Kendra Little shows us how to how to fake reads and writes:

Trainers and speakers need the code they write to be predictable, re-runnable, and as fast as possible. Faking writes can be useful for speakers and teachers who want to be able to generate some statistics in SQL Server’s index dynamic management views or get some query execution plans into cache. The “faking” bit makes the code re-runnable, and usually a bit faster. For writes, it also reduces the risk of filling up your transaction log.

I didn’t invent either of the techniques used below. Both patterns are very common and generic, and so simple that no origin is known.

This isn’t applicable to everyone, but if you’re giving a presentation and want to simulate data access, these are good techniques.

Query Store And Recompile

Grant Fritchey shows that Query Store commands kinda-sorta overpower recompilation hints:

Now, no matter what value I pass it, the RECOMPILE hint is effectively ignored. I have the same plan every time (in my case, the London plan). Ah, but is the RECOMPILE hint ignored? In fact, no, it isn’t. If I capture the extended event sql_statement_recompile, I can see every single time I execute this procedure that it’s going through a full recompile… which is then tossed and the plan I chose is forced upon the optimizer. Every time.

This tells me that if you were using OPTION(RECOMPILE) to get around nasty parameter sniffing problems before, and if you go to Query Store, and if you force a particular plan to get around said nasty parameter sniffing problems, then you probably want to update the query to get rid of that OPTION(RECOMPILE) in order to prevent unnecessary plan compilation work.

View Tuning

Randolph West had to tune a query taking 10-100x too long:

A stored procedure with a single @ProductID parameter would allow us to add WHERE ProductID = @ProductID to both derived tables, which would make the query really fast. In my testing, it ran in under 100ms.

The problem is that this would require numerous code changes to the existing system. Many of the queries that rely on the view also use additional JOINs and WHERE conditions, meaning that a stored procedure is impractical.

Enter the table-valued user-defined function, or TVF.

I’m glad that the TVF worked out for him, but personally, I’m not a big fan of functions.  In this case, though, it does the trick and leaves everyone happy.

Wait Stats

Grant Fritchey gives an introduction to wait stats:

Now, you have a meaningful list of wait statistics that will tell you exactly why, if not where, your server is running slow. Unfortunately, these waits still need to be interpreted. If you read further on Paul’s blog, you’ll see he has a number of waits and their causes documented. That’s your best bet to start understanding what’s happening on your system (although, I hear, Paul might be creating a more complete database of wait stats. I’ll update this blog post should that become available).

Wait stats are fantastic tools for figuring out your server resource limitations given the server’s historic query workload.  They’re the first place to look when experiencing server pains.


Sheldon Hull walks through some scenarios in which OPTION(RECOMPILE) might be useful:

Only want to use in specific scenarios. Basically, from various sources, I’ve always heard that these explicit query hints should typically be designed for edge cases or specific scenarios that are tested, documented, and known to scale appropriately. Anytime you introduce hints, you are taking control from the query analyzer and indicating you know best…. This might be the case, but test test test!

OPTION(RECOMPILE) is like dynamite:  use it to blow up big problems, but understand beforehand what’s going to happen.

Finding High-CPU Queries

Dennes Torres has a script to check CPU-intense queries:

Fortunately we can find queries with high CPU time using sys.dm_exec_query_stats DMV. This DMV, created in SQL Server 2008, keeps performance statistics for cached query plans, allowing us to find the queries and query plans that are most harming our system.

Glenn Berry’s fantastic set of diagnostic queries also includes a couple for finding CPU consumers.


Sometimes, Michael J. Swart says, it’s better to just do it:

Okay, this is getting out of hand. The query shouldn’t have to be this complicated.
Luckily I work with a guy named Chris. He’s amazing at what he does. He questions everything without being a nitpicker (there’s a difference). He read through the Mythbusters post and followed all the links in the comments. He asked whether gbn’s JFDI pattern wasn’t better here. So I implemented it just to see what that looked like:

I’ve ended up doing the same thing in a similar scenario.  But as Aaron Bertrand notes in the comments, test your results because performance could end up being even worse than before.

Cached Times

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.


May 2017
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