Category: Performance Tuning

SQL Sentry Plan Explorer is now compatible with SQL Server 2016:

We’ve been busy modifying SQL Sentry Plan Explorer so it will work seamlessly with the changed add-in model in the new Management Studio. (As a bit of background, SSMS is now based on the Visual Studio 2015 shell, and some work is required to transition existing add-ins.)

I am happy to announce that, as of today, Plan Explorer 2.8 (build 10.1.94), with add-in support for SSMS 2016, is now available!

Huzzah.  That’s two plug-ins down and two more to go before I’m 100% ready for SSMS 2016.

Kathi Kellenberger discusses the importance of RAM to a SQL Server instance:

In order for SQL Server to read and update data, the data must be in the buffer. SQL Server does not work directly with the data in the files on disk. Once the pages of data are in the buffer, they can be used for multiple queries. This means that the data doesn’t have to be retrieved from disk every time it’s needed, thereby decreasing the amount of I/O work required.

You may have seen this yourself when selecting all the rows of a large table twice. The second time, the query can run much faster because the data does not have to be copied from the disk to the buffer. If you run another query from a different large table, it may cause the pages from the first table to be removed to make room. If there is not enough memory, pages will have to be read from disk more frequently causing your queries to be slow.

Even with extremely fast SSDs and flash storage arrays, RAM is still typically an order of magnitude faster, so having enough RAM and using it wisely is critical to a well-functioning SQL Server instance.

Grant Fritchey looks at implicit conversion and the havoc it can wreak:

Letting SQL Server change data types automatically can seriously impact performance in a negative way. Because a calculation has to be run on each column, you can’t get an index seek. Instead, you’re forced to use a scan. I can demonstrate this pretty simply. Here’s a script that sets up a test table with three columns and three indexes and tosses a couple of rows in:

You might get lucky and have the database engine realize that it doesn’t need to give you a horrible execution plan, but it’s sound advice to ensure that data types match on joins and filters.

Michael J. Swart notes that you cannot force query plans if you’re using a user-defined table type with a non-named primary key constraint:

When defining table variables, avoid primary key or unique key constraints. Opt instead for named indexes if you’re using SQL Server 2014 or later. Otherwise, be aware that plan forcing is limited to queries that don’t use these table variables.

Helpful advice when dealing with user-defiened table types.  Read the whole thing.

Aaron Bertrand has another update on the String_Split function, specifically how it compares to user-defined table types:

For this specific test, with a specific data size, distribution, and number of parameters, and on my particular hardware, JSON was a consistent winner (though marginally so). For some of the other tests in previous posts, though, other approaches fared better. Just an example of how what you’re doing and where you’re doing it can have a dramatic impact on the relative efficiency of various techniques, here are the things I’ve tested in this brief series, with my summary of which technique to use in that case, and which to use as a 2nd or 3rd choice (for example, if you can’t implement CLR due to corporate policy or because you’re using Azure SQL Database, or you can’t use JSON or STRING_SPLIT() because you aren’t on SQL Server 2016 yet). Note that I didn’t go back and re-test the variable assignment and SELECT INTO scripts using TVPs – these tests were set up assuming you already had existing data in CSV format that would have to be broken up first anyway. Generally, if you can avoid it, don’t smoosh your sets into comma-separated strings in the first place, IMHO.

That’s a rather interesting result, given how poorly JSON fared in some of the previous tests.

Michael Swart shows how to dig into the transaction log to trace down those WRITELOG waits:

WRITELOG waits are a scalability challenge for OLTP workloads under load. Chris Adkin has a lot of experience tuning SQL Server for high-volume OLTP workloads. So I’m going to follow his advice when he writes we should minimize the amount logging generated. And because I can’t improve something if I can’t measure it, I wonder what’s generating the most logging? OLTP workloads are characterized by frequent tiny transactions so I want to measure that activity without filters, but I want to have as little impact to the system as I can. That’s my challenge.

Check out the entire post, as this is a good exercise in investigating busy transactional systems.