Adaptive Joins and Memory Grants in SQL Server

Adaptive joins let the optimizer choose between a Hash Join and a Nested Loop join at runtime, which can be fantastic for performance when row count estimates are variable. Recently, when Erik Darling taught two days on TSQL at PASS Community Data Summit, a student asked why a query plan where an adaptive join used a Nested Loop at runtime ended up with a large memory grant anyway.

I didn’t remember the answer to this, but the great thing about co-teaching is that Erik did: adaptive joins always start executing as Hash Joins, which means they have to get memory grants upfront. Even if the query ultimately switches to a Nested Loop at runtime, that memory grant was already allocated. This has real implications for memory usage, especially in high-concurrency environments.

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Carrying Baggage Through Query Plans: Why Wide Queries Get Heavy

I see this pattern repeatedly: a “wide” query that returns many columns and less than 100k rows runs slowly. SQL Server gets slow when it drags large amounts of baggage through the entire query plan, like a solo traveler struggling with massive suitcases in an airport instead of picking them up close to their destination.

SQL Server often minimizes data access by grabbing all the columns it needs early in query execution, then doing joins and filters. This means presentation columns get picked up early.

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Testing SQL Server 2025 Resource Governor tempdb Limits with a Query that Spills a Terabyte

SQL Server 2025 introduces a new Resource Governor capability to manage tempdb usage, along with making Resource Governor available in Standard Edition.

I wondered: can Resource Governor’s new tempdb feature help contain queries that don’t use temporary tables, but which spill massive amounts of data to tempdb? The docs say yes, but I always like to get hands-on experience when I can.

I’ve got an awful query that spills like a soft-serve machine choosing violence. Let’s test drive the new tempdb governance features in SQL Server 2025.

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How to Trace Trigger Executions with Query Store and Extended Events

Triggers can be tricky to observe in SQL Server. When you need to understand exactly what a trigger is doing and what it impacts, Query Store and Extended Events both have something to offer, but using them effectively requires navigating some confusing nuances. Query Store tracks query-level executions aggregated by query_id, but has a habit of generating a lot of query_ids with different context settings for triggers. Extended Events can capture trigger module starts and individual statement completions within triggers.

This post walks through setting up both observation methods on a test trigger, then compares what each one shows. You’ll see how Query Store data gets spread across multiple query_ids and context_settings_id values, a variety of ways to trace triggers in XEvents, and why the numbers don’t always match up exactly between traces and Query Store.

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How to Filter RPC_Completed Events in Extended Events: SQL Server Guide

The rpc_completed event in Extended Events is useful when troubleshooting SQL Server performance. It captures detailed information about Remote Procedure Calls: that means stored procedure executions, including the calls to sp_executesql often used by applications (including Entity Framework) to run parameterized queries against SQL Server. The output for rpc_completed includes the parameters that were specified along with values provided, and the CPU time, logical reads, and duration used by the query.

It can be frustrating to figure out how to filter this in Extended Events. Struggling with this is one of the primary reasons I sometimes use ye Olde Profiler for initial investigations and to speedily observe something in SQL Server.

Here is my survival guide to filtering rpc_completed, which makes using XEvents suck less.

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Erik Darling and Kendra Little Talk AI, Databases, and SQL Server 2025

Erik Darling joins me on the Dear SQL DBA Podcast to chat about AI tools, why they work better for Python and PowerShell than SQL, and what’s exciting (and what’s not) in SQL Server 2025.

Along the way we describe the ONE THING we always want to configure in Resource Governor, why at least one SQL Server 2025 feature should be backported to 2022, and whether the universe is text files all the way down.

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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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