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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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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Query Hash Values Are Meaningless in SQL Server: They May Be Reset to Be the Same Value as the Query Plan Hash

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. The query_hash is a ‘Zobrist hash over the shape of the individual query, based on the bound (input) logical query tree. Query hints aren’t included as part of the hash.’ (Source)

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SQL Server Page Compression: Should You Worry About CPU Usage Increasing on Inserts, Updates, and Deletes?

Every time I share a recommendation to use data compression in SQL Server to reduce physical IO and keep frequently accessed data pages in memory, I hear the same concern from multiple people: won’t this increase CPU usage for inserts, updates, and deletes?

DBAs have been trained to ask this question by many trainings and a lot of online content – I used to mention this as a tradeoff to think about, myself– but I’ve found this is simply the wrong question to ask.

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Script to Automate Unforcing Failed Forced Plans in Query Store (SQL Server)

tldr; I’ve published a script to loop through all databases on an instance, identify if there are any query plans in a problematic ‘failed" forced state (which can hurt query performance), and un-force them if found. Get the dbo.dba_QueryStoreUnforceFailed stored procedure on GitHub.

This script is designed to work on SQL Server on-prem, in a VM, or in Azure SQL Managed Instance or SQL Server RDS. Since the script is instance-level and loops through all databases, this isn’t really designed for Azure SQL Database – and you don’t get a SQL Agent there anyway, so you probably want to change this around for that use case. The script is shared under the MIT license, feel free to contribute code and/or adapt away for your own uses.

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