Azure SQL Managed Instance Memory-to-Core Math Still Doesn't Work, Even in GPv2

Microsoft recently announced that Azure SQL Managed Instance Next-gen General Purpose (GPv2) is generally available. GPv2 brings significant storage performance improvements over GPv1. If you’re using GPv1, you should plan to upgrade.

But GPv2 still has the same memory-to-core ratio problem that makes Managed Instance a rough deal for running SQL Server. SQL Server is engineered to use lots of memory—it’s a rare OLTP or mixed-OLTP workload that doesn’t need significant cache for reliable performance. We’ll have a look at the pricing math.

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Index Bloat in Postgres: Why It Matters, How to Identify, How to Resolve

Index bloat in Postgres can cause problems, but it’s easy to miss.

I’ve written about how vacuum problems can prevent PostgreSQL from using covering indexes, and index bloat is one of the things that can make vacuum struggle.

Here’s what you need to know about index bloat, how to find it, and how to fix it.

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How to See Rowcounts and Execution Time for In-Flight Queries in SQL Server

I frequently need to see rowcounts and execution time for queries while they’re running. Maybe I’m troubleshooting a slow query that’s still executing, or I want to understand which operators are causing the slowdown before the query completes.

Last week at the PASS Summit I learned some little nuances about how this works that I’d missed.

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Three Reasons RDS SQL Server Is Better Than Azure SQL Managed Instance

While every managed database service has high points and low points, there are three things that make RDS for SQL Server shine in comparison to Azure SQL Managed Instance: options for a higher memory:vCPU ratio, a well documented API that works beautifully with python, and fast and effective customer support that isn’t painful to use.

It’s just three things, but they make a huge difference. And these are three things that Microsoft can, and SHOULD, really fix for their cloud database services.

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Why Won't PostgreSQL Use My Covering Index?

Dear Postgres, Why won’t you use my covering index?

Lately I’ve been learning to tune queries running against PostgreSQL, and it’s pretty delightful. One fun question that I worked through struck me something that most Postgres users probably encounter one way or another: sometimes you may create the perfect index that covers a given query, but the query planner will choose to ignore it and scan the base table.

Why in the world would it do that?

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Fixing OPTIMIZATION REPLAY FAILED in Query Store: When Plan Forcing Breaks in SQL Server 2022+

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.

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Comparing Single Column, Multi-Column, and Filtered Statistics in SQL Server

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.

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