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Category: Query Tuning

Understanding Query Execution Time Statistics

Published 2021-06-15 by Kevin Feasel

Esat Erkec takes us through SET STATISTICS TIME ON:

The SET STATISTICS TIME ON statement returns a text report and this report includes how long it is taken by the query compilation and execution time of a query. To enable this option for any query we need to execute the SET STATISTICS TIME ON command before the execution of the query so that the execution time report will appear in the message of the query result panel until we turn off this option. All values of the report ​​are shown in milliseconds type and its syntax like as below:

Read on to see how you can use it, as well as things to keep in mind as you do.

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When Expressions Beat Local Variables

Published 2021-06-03 by Kevin Feasel

Erik Darling talks about a pet peeve of mine:

I see people writing stored procedures that use local variables either because they read that all functions in a where clause are dangerous, or because they need to perform a calculation on one of the parameters that get passed in.

In nearly all cases, you’re better off… not doing that.

Here are a couple examples of why.

I think a lot of this stems from advice to avoid non-SARGable predicates, but miss the idea that expressions of the sort Erik shows are perfectly cromulent.

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Dealing with Key Lookups

Published 2021-06-01 by Kevin Feasel

Jared Poche shows a good technique for removing key lookups:

A key lookup is an operation that occurs when a query has used a nonclustered index on a given table, but needs to access more columns to complete the query. It may need to check columns not in that index for additional filters, or it may just need to return that column as part of its result set.

In the simple query above, we’re retrieving 100 rows from the seek against a nonclustered index, then performing a key lookup against the clustered index. There is a nested loops operator between the two and understanding how that operates is important; for each row we receive from the first table, we perform the second operation once. So, in this query we are seeking 100 rows from the nonclustered index, then performing the key lookup 100 times. We go through the index once for each row we return, and you can see the cost of the key lookup operator is 99% of the query.

Read on for more information, including how to eliminate key lookups.

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Percentages in Execution Plans

Published 2021-05-25 by Kevin Feasel

Hugo Kornelis shares some information with us:

A lot of information you see when looking at execution plan is shown as a percentage. And those percentages are often the first thing that draws our attention. So it’s really important to know what those values are. And yet, I had forgotten to include coverage for percentages in my training videos. And you’ll also not find those percentages mentioned in the Execution Plan Reference. Why? Simple. They don’t exist.

It is here that I imagine a short silence. Followed by a huge outburst. “What? Of course they exist! I look at them every day!”

Read on to see what Hugo means, and also check out the video training course.

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Temporal Table Performance Scenarios

Published 2021-05-21 by Kevin Feasel

Hugo Kornelis continues a series on temporal table performance:

Welcome to part eighteen of the plansplaining series. Like the previous posts, this one too focuses on temporal tables and their effect on the execution plan. After looking at data modifications in temporal tables and at querying with a most basic temporal form of temporal query, let’s look at the more advanced variations for temporal querying.

We’re still looking at getting data from a single query only in this post. We’ll look at joins in the next post.

Click through for these scenarios.

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Tracking Query Compile Time

Published 2021-05-12 by Kevin Feasel

Grant Fritchey doesn’t have time to wait:

A question that came up recently was how to track the query compile time. It’s actually a pretty interesting question because, there aren’t that many ways to tell how long it took to compile the query, and they don’t necessarily agree. For most of us, most of the time, compile time for a given query doesn’t matter. However, I love telling the story of the query I had on an old system that could run in 90ms, but took 5 minutes to compile. In short, sometimes compile time matters.

Read on to learn several ways to determine how long it took that query to compile.

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Row Goals and Query Optimizer Estimates

Published 2021-05-03 by Kevin Feasel

Hugo Kornelis explains how row goals can change query optimizer behavior:

The most simple is when the query literally tells SQL Server that you don’t want to have all rows returned. Everyone knows the TOP clause, which is most commonly used for this. For ANSI portability, and because it adds a few options, you should also be aware of the FETCH and OFFSET modifiers to the ORDER BY clause, that have a similar functionality and are specifically designed to support paging. And there is of course the SET ROWCOUNT option, though I sincerely hope nobody actually uses that. All of these options literally tell SQL Server that we don’t want all results, only a part of them. The execution plan that would produce the entire set the fastest might not necessarily be the fastest way to get the few rows we actually want, so it’s a good thing that the optimizer has a way to come up with a different execution plan for these cases.

But there are plenty of other ways you might get a row goal, so check them out.

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Another Batch of Common Query Plan Patterns

Published 2021-04-23 by Kevin Feasel

Erik Darling continues pulling one-star query plan patterns. First up is common subexpression spools:

The first time I heard the term “common subexpression spool” my brain went numb for a week.

It’s not a particularly mellifluous phrase, but it is helpful to understand what it is.

One easy way to think about it is a temporary cache for the result of a query, like a temp table inside your execution plan:

Then we look at optimizer choices around sorting:

Sometimes these Sorts are harmless, and sometimes they’re not. There are many situational things about the queries, indexes, available resources, and query plan appropriateness that will lead you to treating things differently.

Parameter sniffing, cardinality estimate accuracy, query concurrency, and physical memory are all potential reasons for these choices going great or going grog.

Read both of the whole things.

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