Liz Bennett explains types of regular expressions which do not scale:

With recursive backtracking based regex engines, it is possible to craft regular expressions that match in exponential time with respect to the length of the input, whereas the Thompson NFA algorithm will always match in linear time. As the name would imply, the slower performance of the recursive backtracking algorithm is caused by the backtracking involved in processing input. This backtracking has serious consequences when working with regexes at a high scale because an inefficient regex can take orders of magnitude longer to match than an efficient regex. The standard regex engines in most modern languages, such as Java, Python, Perl, PHP, and JavaScript, use this recursive backtracking algorithm, so almost any modern solution involving regexes will be vulnerable to poorly performing regexes. Fortunately, though, in almost all cases, an inefficient regex can be optimized to be an efficient regex, potentially resulting in enormous savings in terms of CPU cycles.

There’s a significant performance difference, so if you work frequently with regular expressions, check this out.

Erin Stellato hacks DBCC CLONEDATABASE and makes it that much more powerful:

Very often when I mention testing before an upgrade, I’m told that there is no environment in which to do the testing.  I know some of you have a Test environment. Some of you have Test, Dev, QA, UAT and who knows what else. You’re lucky.

For those of you that state you have no test environment at all in which to test, I give you DBCC CLONEDATABASE. With this command, you have no excuse to not run the most frequently-executed queries and the heavy-hitters against a clone of your database. Even if you don’t have a test environment, you have your own machine.  Backup the clone database from production, drop the clone, restore the backup to your local instance, and then test.  The clone database takes up very little space on disk and you won’t incur memory or I/O contention as there’s no data.  You will be able to validate query plans from the clone against those from your production database. Further, if you restore on SQL Server 2016 you can incorporate Query Store into your testing! Enable Query Store, run through your testing in the original compatibility mode, then upgrade the compatibility mode and test again. You can use Query Store to compare queries side by side! (Can you tell I’m dancing in my chair right now?)

Erin’s discovery makes CLONEDATABASE go from being an interesting tool to being outright powerful for handling upgrades.

Kenneth Fisher explains RBAR with the help of an animated GIF:

So 23 milliseconds for the batch version and 850 milliseconds for RBAR. What a difference.

Now in this case the code for the RBAR is also a lot more complicated. But that isn’t always the case. It also isn’t always the case that RBAR is slower. But it’s almost always a lot slower than batch.

So, while the code for RBAR is often easier to write, even though it might be physically longer, it’s probably going to be slower too.

Well-written, set-based solutions aren’t always guaranteed to be faster, but that’s one of the safest bets to make with T-SQL.

Kendra Little gives some tips on how to gather performance tuning skills:

Start writing queries that demonstrate TSQL anti patterns – and make them slow

You know how people say that the best way to learn something is to teach it?

The best way to learn to speed up queries is to write slow ones.

The best way to get a job speeding up queries is to write a blog about the queries you’ve sped up.

This is a long-term learning process, but is absolutely a worthwhile skill for any database professional.

SQL Sasquatch is starting a new series on optimizing disk write to maximize CPU throughput:

When I work with SQL Server batch-controlled workflows, I use the theory “feed the CPUs”.  That’s the simplest positive adaptation I could come up with of Kevin Closson’s paradigm “Everything is a CPU problem” 🙂

What I mean by “Feed the CPUs” is that memory and disk response times are primary factors determining the maximum rate for the CPUs to process the data.  Nuts & bolts of such a model for SQL Server are slightly different than a similar model for Oracle.  SQL Server access to persistent data is always through database cache, while Oracle uses shared access to database cache in SGA and private access to persistent data through direct access in PGA.

Click through for more details.

Dan Guzman shows that setting TVP column sizes correctly can have a major performance impact:

LOB values are especially problematic when a trace captures the RPC completed event of a TVP query. Tracing uses memory from the OBJECTSTORE_LBSS memory pool to build trace records that contain TVP LOB values. From my observations of the sys.dm_os_memory_clerks DMV, each LOB cell of a TVP requires about 8K during tracing regardless of the actual value length. This memory adds up very quickly when many rows and lob columns are passed via a TVP with a trace running. For example, the 10,000 row TVP with 10 LOB columns used in the earlier test required over 800MB memory for a single trace record. Consider that a large number of TVP LOB cells and/or concurrent TVP queries can cause queries to fail with insufficient memory errors. In extreme cases, the entire instance can become unstable and even crash under due to tracing of TVP queries.

This is a must-read if you use TVPs in your environment.

Arvind Shyamsundar looks at parallel insertion using the INSERT SELECT pattern:

For row store targets, it is important to note that the presence of a clustered index or any additional non-clustered indexes on the target table will disable the parallel INSERT behavior. For example, here is the query plan on the same table with an additional non-clustered index present. The same query takes 287 seconds without a TABLOCK hint and the execution plan is as follows

This post goes into detail on when you can expect parallelism in rowstore and columnstore insertions.  I highly recommend reading it.

Grant Fritchey describes a couple built-in options for monitoring query performance:

It’s not enough to know that you have a slow query or queries. You need to know exactly how slow they are. You must measure. You need to know how long they take to run and you need to know how many resources are used while they run. You need to know these numbers in order to be able to determine if, after you do something to try to help the query, you’ll know whether or not you’ve improved performance. To measure the performance of queries, you have a number of choices. Each choice has positives and negatives associated with them. I’m going to run through my preferred mechanisms for measuring query performance and outline why. I’ll also list some of the other mechanisms you have available and tell you why I don’t like them. Let’s get started.

This is an intro-level blog post, so Grant doesn’t go into much detail, but he does provide some good links for getting started.