Forcing plans with Query Store can be a powerful tool—until it mysteriously fails. In real production systems, plan forcing sometimes just… doesn’t work. One common culprit is the cryptic OPTIMIZATION_REPLAY_FAILED error.
If you’re hitting OPTIMIZATION_REPLAY_FAILED, try re-forcing the plan using @disable_optimized_plan_forcing=1.
Statistics in SQL Server are simple in theory: they help the optimizer estimate how many rows a query might return. In practice? Things get weird fast. Especially when you start filtering on multiple columns, or wondering why the optimizer thinks millions of rows are coming back when you know it’s more like a few hundred thousand. In this post, I’ll walk through examples using single-column, multi-column, and filtered statistics—and show where estimates go off the rails, when they get back on track, and why that doesn’t always mean you need to update everything with FULLSCAN.
The biggest lesson I’ve learned from helping folks manage data in Azure is this: if you’ve got a truly terrible problem you’d rather people didn’t notice, a great way to hide it is by educating your support staff and users about something bad but not AS terrible— something with a small mitigation—and constantly refocusing them on that.
The user base— and even your own support staff— will think that anyone who talks about the bigger issue just doesn’t understand how to fix the “known” problem.
This is the story of Azure General Purpose storage for Azure SQL Managed Instance and Azure SQL Database.
Views let you do dumb things by accident in SQL Server. Then they make you have to think way too hard to fix them.
“Is it just me, or is SQL Server quality slipping?”
I asked myself that question for couple/few years until I faced up to it: SQL Server is well into a period where Microsoft investment is waning, and Microsoft regularly isn’t able to deliver the features they promise.
I listened to “Surviving the A.I. Endgame” this weekend and realized: I’ve become one of the believers that AI advances are very likely to completely change tech and knowledge roles as we know them over the next 10 years. This is going to dramatically shrink the workforce across MANY roles (and many of those impacted will be outside of the tech sector). It isn’t that people won’t be needed anymore, but far fewer people will be needed.
SQL Server’s free state-based version control tooling was introduced under the “Data Dude” brand, then became known as “SQL Server Data Tools” (SSDT). Its extension for the (now dying) Azure Data Studio IDE is called “SQL Database Projects.” If you need to find documentation, you often need to know to search for specific component names like SQLPackage.exe, which is a command line utility used to deploy SSDT Projects AKA SQL Database Projects.
What are your stories of unbelievably bad performance from cloud vendors? I’ll go first. For years, Azure SQL Managed Instance’s General Purpose Tier has documented “approximate” storage latency as being “5-10 ms." This week they added a footnote: “This is an average range. Although the vast majority of IO request durations will fall under the top of the range, outliers which exceed the range are possible.” How approximate is that 5-10 milliseconds, you might wonder?
This is the worst bug I’ve found in SQL Server to date. Previously, my top find was SQL Server Online Index Rebuild sometimes happens offline without warning. This one has taken top slot because it makes my life more difficult on a daily basis. Background: SQL Server generates a query_hash for each query. This is stored in sys.query_store_query and it’s one of the primary ways you can identify what a query is across different Query Stores, or even the same Query Store over time, as surrogate query_id values get reset if Query Store is cleared or data ages on.
I use Datadog on a regular basis, and I’m a pretty huge fan. The monitoring pack for SQL Server (and its PAAS variants) is still pretty rudimentary, but it evolves regularly. That’s NOT what I’m a fan of, though. What makes me a raving fan is the flexibility of Datadog’s notebooks and dashboards, combined with the ability to create all sorts of custom metrics and monitors. There are always things in SQL Server monitoring packs that I have strong opinions about.
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