Erik Darling lays out an argument why execution time isn’t always a great measure for finding bad queries:

Most scripts (even ones I’ve worked on!) that look at the plan cache, have had the ability to sort it by different metrics: CPU, reads, writes, duration, etc.

A lot of people are very interested in long running queries — and I am too!

Heck, they’re how I make money. Blogging pays like crap.

But there’s a slight problem with only looking at query duration.

Read on for an example. Erik loses money on every blog post but he makes up for it in volume.

Paul White takes us through several performance improvements around aggregate pushdown:

SQL Server 2016 introduced serial batch mode processing and aggregate pushdown. When pushdown is successful, aggregation is performed within the Columnstore Scan operator itself, possibly operating directly on compressed data, and taking advantage of SIMD CPU instructions.

The performance improvements possible with aggregate pushdown can be very substantial. The documentation lists some of the conditions required to achieve pushdown, but there are cases where the lack of ‘locally aggregated rows’ cannot be fully explained from those details alone.

This article covers additional factors that affect aggregate pushdown for GROUP BY queries only. Scalar aggregate pushdown (aggregation without a GROUP BY clause), filter pushdown, and expression pushdown may be covered in a future post.

Read the whole thing.

Brent Ozar shares a list of load testing tools for SQL Server:

One thing I need you to understand first: you have to provide the database and the queries. Almost all of the tools in this post, except the last one, are designed to help you run queries, but they don’t include the queries. The whole idea with load testing is that you’re trying to mimic your own workloads. If you’re just trying to test a server with generic workloads, start with my post, “How to Check Performance on a New SQL Server.”

Click through for a list of tools. I’d also throw in Pigdog from Mark Wilkinson (one of my co-workers). This helped replicate a few issues in SQL Server 2017 around tempdb performance.

Erik Darling takes us through a limitation to SQL Server 2019 scalar function inlining:

There’s a lot of excitement (alright, maybe I’m sort of in a bubble with these things) about SQL Server 2019 being able to inline most scalar UDFs.

But there’s a sort of weird catch with them. It’s documented, but still.

If you use GETDATE in the function, it can’t be inlined.

GETDATE() and its bretheren are non-deterministic so I figured that would be an issue. Check out the documentation for the other limitations.

Monica Rathbun explains what a key lookup is in SQL Server:

One of the easiest things to fix when performance tuning queries are Key Lookups or RID Lookups. The key lookup operator occurs when the query optimizer performs an index seek against a specific table and that index does not have all of the columns needed to fulfill the result set. SQL Server is forced to go back to the clustered index using the Primary Key and retrieve the remaining columns it needs to satisfy the request. A RID lookup is the same operation but is performed on a table with no clustered index, otherwise known as a heap.  It uses a row id instead of a primary key to do the lookup.

As you can see these can very expensive and can result in substantial performance hits in both I/O and CPU. Imagine a query that runs thousands of times per minute that includes one or more key lookups. This can result in tremendous overhead which is generated by these extra reads it effects the overall engine performance.

Monica’s absolutely right: key lookups can take a decent query and make it into a performance hog.