This insanely cool new performance-related update is one of nicest features in SQL Server 2019, and certainly one I’ve been looking forward to for a long time.
If you’ve done any work around performance tuning and user-defined scalar functions, I’m pretty sure you’ll love this.
In an attempt to try a different approach, here’s a three-minute video explanation of how the different physical join operators in SQL Server work and why you would choose one over the other.
I’ve written a few blog posts on join operators befores, so if this video wet your appetite, here’s some recommended reading:
I’d love to hear what you think of the short video format! Please leave feedback in the comments below or on Twitter.
Performance tuning the other day, I was stumped by a query plan I was looking at. Even though I had constructed a covering index, I was still getting a Key Lookup operator in my query plan. What I usually do when that happens is to check the operator’s properties to see what its output columns are, so I can include those columns in my covering index.
Here’s the interesting thing: there weren’t any output columns. What happened?
The “include actual execution plan” feature in SQL Server Management Studio is an invaluable tool for performance tuning. It returns the actual execution plan used for each statement, including actual row counts, tempdb spills and a lot of other information you need to do performance tuning.
But sometimes you want to run a series of statements or procedures where you only want the execution plan for some of the statements. Here’s how:
The SQL Server query optimizer can find interesting ways to tackle seemingly simple operations that can be hard to optimize. Consider the following query on a table with two indexes, one on (a), the other on (b):
SELECT a, b FROM #data WHERE a<=10 OR b<=10000;
The basic problem is that we would really want to use both indexes in a single query.
In this post, we’re going to take a look at a few examples of how this type of query would be optimized, as well as how statistics can affect the query plan, and finally, we’ll take a look at a slightly rare plan operator called “Merge Join (Concatenation)”.
Here’s a quick tip: When you’re evaluating query strategies, you may want to consider how your query will scale when the volume in your database goes up. This does not neccessarily mean that you have to start filling your tables with gigabytes on gigabytes of data.
Although it’s rotten practice, values like dates, times, numbers, etc are often stored in columns with the wrong data type. Sometimes, it’s because the data model needs to allow for multiple types of data in the same column (sometimes even on the same row in the form of a formula or text syntax), but sometimes, it’s just plain and simple lazy.
With this type of setup, it’s only a matter of time before you run into conversion errors like this one:
Msg 245, Level 16, State 1, Line 29 Conversion failed when converting the varchar value 'xyz' to data type int.
To make things worse, these errors can be tricky to pinpoint, because they can appear to come and go without any real recognizable pattern. In this post, I’ll take a look at how this happens, how to look for these errors, and ultimately how to fix them if you can’t change the database schema.
The spool operator is one of those icons that will show up in a query plan now and then. In this post, we’ll take a closer look at what it does in a query plan, how it affects performance, and what you can do to substitute or eliminate it.
Here’s a strange insight that I gained when building a test case where I needed some randomized values. In order to generate random values, you can use the NEWID() function, which creates a uniqueidentifier value for each row. But NEWID() comes with a strange behaviour, that some (including me) will consider a bug, while others (including the SQL Server development team) consider it to be “by design”.
There are three different perspectives when you look at a T-SQL statement; the way it’s written, the way it’s evaluated, and the way it’s executed. They’re all important to understand, for different reasons.