Category: Performance Tuning

Max Vernon runs a performance test of CAST versus CONVERT:

This post is a follow-up to my prior post inspecting the performance of PARSE vs CAST & CONVERT, where we see that PARSE is an order of magnitude slower than CONVERT. In this post, we’ll check if there is a similar difference between using CAST or CONVERT. But just to be clear, CONVERT offers a lot more functionality than CAST; this post will not help you decide which of these functions to use for a specific use-case – I leave that to the reader to decide for themselves.

Max gets slightly different numbers but under the covers they both call the same CONVERT() function. The difference in numbers is noise: both of them have standard deviations of ~200ms, so a t-test can’t distinguish the two. The big choice is whether you’d rather have ANSI standard code (if so, use CAST()) or if you’d prefer additional functionality around dates and times (like CONVERT() offers).

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Bert Wagner takes us through a workflow for troubleshooting performance issues in SQL Server using execution plans:

With the query pasted and formatted in my SSMS editor window, I like retrieving the estimated execution plan first, and then pasting the query into a second editor window and executing the query with the “Include Actual Execution Plan” option turned on. For bonus points, I’ll split the SSMS window vertically so I can start looking at the estimated execution plan while the query runs and returns the actual execution plan: I like this combination because I (almost) immediately receive my estimated execution plan and can start looking for problems. Once the query on the right finishes executing and I get the actual plan with all of its lovely run-time stats, I usually switch to that looking at that one.

Even if your approach is quite different, it’s good to compare and contrast.

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Brent Ozar reviews a Microsoft Research paper:

In the Microsoft Research paper Plan Stitch: Harnessing the Best of Many Plans by Bailu Ding, Sudipto Das, Wentao Wu, Surajit Chaudhuri, and Vivek Narasayya (2018), the authors propose something really cool: watching an execution plan change over time, splitting it into parts, and then combining parts to form a SuperPlan™. (They don’t call it a SuperPlan™ – it just seems appropriate, right?)

That looks like an interesting paper, and Brent has a few more if you agree.

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Niko Neugebauer looks at TPC-H query testing in both row mode and batch mode:

I executed every single query enough times so that the execution would be run totally In-Memory (64GB of RAM is enough because we have our data compressed, as I mentioned earlier in the SETUP part). This would allow me to mimic a busy system that has enough resources to process the reading workload. Since the Batch Execution Mode is focusing on the CPU improvements, I decided to sample not only the total elapsed time, but the CPU times so that we can do some judgements of the CPU bandwidth variation. Lowering the CPU consumption fo the high-demanding CPU queries is a key in order to improve the overall system parallelism (well, watch-out for the memory, of-course).

Each successful execution was sampled at least 3 (and in some query cases over 5 times) and then the result would be averaged, so that we can have a higher confidence.

Niko has some interesting findings, some good for SQL Server and some not so good.

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Nick Craver has a long post on how Stack Overflow does application caching:

For everyone who hates caching, this is the section for you! Yes, I’m totally playing both sides.

Given the above and how drastic the wins are, why wouldn’t we cache something? Well, because every single decision has trade-offs. Every. Single. One. It could be as simple as time spent or opportunity cost, but there’s still a trade-off.

This is a long but very useful post.

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