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Category: Performance Tuning

Troubleshooting High CPU via PAGELATCH Waits

Ajay Dwiveldi does some digging:

In the above dashboards, I could clearly notice PAGELATCH_** wait at the top along with SOS_SCHEDULER_YIELD. The presence of the above 2 waits is indicative of high CPU issues due to contention on the access of data file pages. I validated and found that this PAGELATCH_** wait is present almost all the time on the server. So decided to check the data of dbo.WhoIsActive that stores captured data of sp_WhoIsActive in SQLMonitor tool.

Read on for the outcome.

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Apache Spark Performance Tuning Tips

Amit Kumar shares a few tips with us:

RDD does serialisation and de-serialisation of data whenever it distributes the data across clusters such as during repartition and shuffle, and we all know that serialisation and de-serialisation are very expensive operations in spark.
On the other hand, DataFrame stores the data as binary using off-heap storage, no need for deserialization and serialization of data when it distributes to clusters. We see a big performance improvement in DataFrame over RDD

Click through for several additional tips.

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Breaking the World with auto_explain

Ryan Lambert gets a lot of explanation:

Postgres has a handy module called auto_explain. The auto_explain module lives up to its name: it runs EXPLAIN automatically for you. The intent for this module is to automatically provide information useful for troubleshooting about your slow queries as they happen. This post outlines a pitfall I recently discovered with auto_explain. Luckily for us, it’s an easy thing to avoid.

I discovered this by running CREATE EXTENSION postgis; and watching it run for quite a while before failing with an out of disk space error. That is not my typical experience with a simple CREATE EXTENSION command!

Read on to learn what happened and how you can prevent making a similar mistake.

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Azure SQL Database Performance Roundup

Reitse Eskens shares the goods:

In the past 9 blogs, I’ve shown you all sorts of Azure SQL database solutions and gave them a little run for their money. I’ve tested a lot and written about them. This blog will be about the summation of the data and my views on the combined graphs. At the end I’ll wrap it up with my way of working when a new project starts.

But before I kick off, a little Christmas present. What I didn’t do, until now, is give you access to more raw data. Now is the moment to give you more raw number to play around with for yourself and do your own analysis. Fun as it might be, I’d highly encourage you to use my sheets as a jumping point and adapt them for your own workloads. You can find the two Excel files via the link for the scripts.

This is a post I’d been waiting for, as it covers the comparisons between tiers directly, rather than inferring it from the various posts.

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Azure SQL Managed Instance Performance

Reitse Eskens wraps up a series on Azure SQL performance comparisons:

So far, the blogs were about the really SaaS databases; the database is deployed and you don’t have think about it anymore. This ease of use comes at a ‘price’. You’ve got no control whatsoever on files, you’ve lost the SQL Agent and a number of other features. The managed instance is a bit different. When I was testing you could see the TempDB files but not change them, since then a few changes have been made to this tier where you’re able to change settings and, Niko Neugebauer told the data community on twitter, there are more changes coming. With the managed instance, you get the agent back and you can run cross database query’s again. So you can safely say the managed instance is a hybrid between your trusty on-premises server and the fully managed Azure SQL database.

Click through for Reitse’s thoughts.

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Performance-Killing Pre-Emptive Waits

Sean Gallardy finds the real killer:

If you haven’t already read up on cooperative and preemptive scheduling or aren’t sure what those are, please read the intro to that first, otherwise you’ll be lost.

Much as I’ve discussed before, SQL Server uses a cooperative scheduling model. This doesn’t mean that Windows does, nor does it mean Windows will scheduler whatever SQL Server schedules, in fact much of the time there are many other threads that run before the ones from SQL Server, that’s the job of the operating system to figure out. Due to SQL Server using cooperative scheduling there needs to be a mechanism that exists such that when a resource not under SQL Server’s control needs interaction the scheduler can keep on scheduling and threads can switch in and out (in SQL Server, Windows does what Windows wants). Enter preemptive status and associated waits.

Click through for a deep dive on the topic.

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RCSI and Blocking

Michael J. Swart says don’t worry, be happy:

What’s the best way to avoid most blocking issues in SQL Server? Turn on Read Committed Snapshot Isolation (RCSI). That’s it.

Do check out Erik Darling’s comment as well for one thing to keep in mind if you turn on RCSI.

The other thing to keep in mind is that, if you have WITH(NOLOCK) hanging around everywhere in your code, you won’t get as much of a benefit with RCSI until you remove them.

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New Query Tuning Book

Grant Fritchey has a book for us:

If you’re interested in getting a digital copy, my brand spanking new book is now available here.

It’s in the intro, but let me tell you a little bit about the new book. It’s really new. Some of the older versions of the book were simply updated, a bunch of changes to most chapters, a couple of new chapters, fixes for old mistakes, ta-da, new book. Not this time. This time, I rewrote it all. From scratch.

Looks like I’ll need to get a copy.

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Troubleshooting I/O Issues in SQL Server

Ajay Dwivedi shares some advice:

Storage performance is something that puzzles a lot of SQL Server professionals. So in this blog, I will cover the basic steps I perform to ensure I get the best performance from the underlying storage.

Read on for some thoughts on storage testing prior to SQL Server installation, as well as what to do to ensure your SQL Server instance is up and at them.

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Performance Tuning Tables with Filters in Power BI

Chris Webb doesn’t want to wait:

There are four columns: Date, Town and two measures. One measure called [Fast Measure] is, as the name suggests, very quick to execute; the other measure, called [Slow Measure], is very complex and slow. The definitions are irrelevant here. Notice that there is a filter on this table visual so only the rows where [Fast Measure] is greater than 1 are shown.

If I measure the amount of time to render this table in Performance Analyzer, it takes around 17.5 seconds to run. However, if I remove the filter on [Fast Measure], the table only takes 8 seconds to run. Why? The filter is on the fast measure and surely more rows are returned without the filter, so wouldn’t the slow measure be evaluated more?

Click through for the answer.

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