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Category: Query Tuning

Parsing SQL Server Query Stats in Powershell

Published 2024-09-24 by Kevin Feasel

Andy Levy builds a cmdlet:

A couple weeks ago, DrData asked

With SET STATISTICS IO ON, is there really no way to show the TOTALS at the bottom? There are some nice tools like StatisticsParser but it sure would be nice to see the totals of all values right at the bottom, especially when there are many tables.

The task at hand in the office was a need to do the same thing, but with SET STATISTICS TIME ON. So I got to thinking…I can PowerShell my way through this, right?

Andy, being The Man, shows us exactly how to do this.

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ISNULL vs COALESCE in SQL Server

Published 2024-09-13 by Kevin Feasel

Erik Darling has a video for us:

A Difference Between ISNULL And COALESCE You Might Care About In SQL Server

There’s nothing for me to snip as the graf. I don’t often link to videos without any sort of text accompaniment, but it’s been too long since I’ve linked to Erik and this was an interesting topic.

Bonus points for using “case expression” instead of the more common but technically incorrect “case statement.”

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Implicit Conversions in SQL Server

Published 2024-09-12 by Kevin Feasel

Vlad Drumea explains what it means implicitly to convert:

If you’re here, you most likely know what a data type conversion is, but, in short, it’s the operation of converting a value from one data type to another.

There are two types of conversions in SQL Server:

  • explicit – which are done by explicitly applying the CAST and CONVERT functions on a column, variable, or value.
  • implicit – when CAST and CONVERT are not used explicitly, but SQL Server ends up doing the conversation behind the scenes due to two distinct data types being compared.

Read on to learn more about which types of implicit conversion are relevant for performance and what you can do instead.

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Query Start Times in Query Store

Published 2024-09-11 by Kevin Feasel

Hugo Kornelis describes an issue:

I was hired by a customer who had a very annoying issue with the daily data load of their data warehouse. The volume of data to be loaded is high and they were already struggling to finish the load before business opens. But that was not their biggest issue. The biggest problem, the real pain point that they hired me for, is that at unpredictable moments, the load would run much longer than normal, pushing it well into business hours. They wanted me to find out what caused those irregular delays, and find a way to stop them from happening.

Read on to learn more about the issue itself, as well as a discrepancy in what Query Store showed. Hugo also points out that the quick-and-easy solution may not be the right solution.

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Troubleshooting a Performance Issue using Query Store

Published 2024-09-03 by Kevin Feasel

Edwin Sarmiento shows a practical application of Query Store:

In a previous tip on SQL Server 2016 Query Store Introduction, we’ve seen how to enable Query Store and find out the top resource consumers. We’ve experienced high CPU utilization recently and wanted to know the root cause and how to fix it. How can we use Query Store to achieve this?

Read on for the scenario and how Edwin diagnosed and fixed the issue.

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Performance Profile of Fast-Forward Cursors

Published 2024-09-03 by Kevin Feasel

Hugo Kornelis continues a deep dive into cursors:

One of the things that has always bothered me about the fast forward cursor type is the shocking lack of documentation of what it does exactly. Okay, the name suggests that it is fast. But is it really? When I first looked at cursor performance (granted, a long time ago), I found that a static cursor was actually faster than a fast forward cursor for the same query. So… fastish forward?

The name also suggests that this cursor is forward only. That is indeed the case. At least they got that right in the naming.

Read on to learn more about this cursor property, as well as how it compares to static and dynamic cursors. Hugo ends on a spicy take you won’t want to miss.

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Computed Columns and Wide Index Updates

Published 2024-08-30 by Kevin Feasel

Paul White takes us through a performance scenario:

Update execution plans are not something the T-SQL statement writer has much control over. You can affect the data reading side of the plan with query rewrites and hints, but there’s not nearly as much tooling available to affect the writing side of the plan.

Update processing can be extremely complex and reading data-changing execution plans correctly can also be difficult. Many important details are hidden away in obscure and poorly documented properties, or simply not present at all.

In this X article, I want to you show a particularly bad update plan example. It has value in and of itself, but it will also give me a chance to describe some less well-known SQL Server details and behaviours.

Read on for the full story.

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Cost Threshold for Parallelism and Missing Indexes

Published 2024-08-22 by Kevin Feasel

Jared Westover explains a phenomenon:

Did you know that the Cost Threshold for Parallelism (CTFP) affects SQL Server’s choice of a trivial execution plan? One area that can suffer from this setting is the optimizer’s ability to suggest index recommendations. When SQL Server picks a trivial plan, it skips suggesting any missing indexes. So, if you’ve set a high CTFP and run simple queries, you might never get those handy index recommendations.

Click through to learn more.

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Performance Tuning via Query History in Snowflake

Published 2024-08-21 by Kevin Feasel

Kevin Wilkie gets down to tuning:

In our last post, we talked about some of my favorite queries I use in Snowflake to see various items of interest – such as finding the worst-performing queries. For today’s post, though, I want to talk about performance tuning.

Yes, you read that right. We’re going to use query history to do some fun performance tuning.

Click through for two queries that can help you find what you may need to tune.

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Query Re-Optimization in Postgres

Published 2024-08-20 by Kevin Feasel

Andrei Lepikhov walks through an interesting scenario:

What was the impetus to begin this work? It was caused by many real cases that may be demonstrated clearly by the Join Order Benchmark. How much performance do you think Postgres loses if you change its preference of employing parallel workers from one to zero? Two times regression? What about 10 or 100 times slower?

The black line in the graph below shows the change in execution time of each query between two cases: with parallel workers disabled and with a single parallel worker per gather allowed. For details, see the test script and EXPLAINs, with and without parallel workers.

Click through for an overview of what Andrei wrote, including architectural notes. But stick around until the end to see just how difficult the challenge is to re-optimize without making performance worse in the end.

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