Built atop 2016’s Query Store, 2017 adds Automatic Tuning: the ability to monitor Query Store, watch for query plans that have gotten worse over time, and automatically force the better prior plans. The problem is that it only works well if there’s one plan that actually works well for enough of the possible parameters. Demo Script…
3.3 How Automatic Tuning Mitigates Parameter Sniffing
Built atop 2016’s Query Store, 2017 adds Automatic Tuning: the ability to monitor Query Store, watch for query plans that have gotten worse over time, and automatically force the better prior plans. The problem is that it only works well if there’s one plan that actually works well for enough of the possible parameters. Demo Script...
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- 0.1 Prerequisites Before the Class
- 0.2 Download the Slides and Scripts
- 1.1 How Index Tuning Reduces the Stench
- 1.2 How Query Hints Reduce the Stench
- 1.3 The Drawbacks of Using Recompile Hints
- 1.4 Lab 1: Get One Plan to Work Well Enough for Most
- 1.5 Bad Branching Causes Sniffing, Good Branching Reduces It
- 1.6 Lab 2: Reducing Sniffing by Caching Multiple Plans
- 2.1 Spotting Wild Variations in the Plan Cache
- 2.2 How Freeing the Plan Cache Reduces the Blast Radius: usp_PlanCacheAutopilot
- 2.3 Tracking Even More Plan Cache Details with Query Store
- 2.4 Lab 3: Track Down Plans & Parameters
- 3.1 How Memory Grant Feedback Multiplies Parameter Sniffing
- 3.2 How Adaptive Joins Help, and How to Get Them
- 4.1 Lab 4 Setup: Fixing the Problems You’ve Been Tracking Down
- 4.2 Tuning usp_SearchPostsByPostType
- 4.3 Tuning usp_RptFastestAnswers
- 4.4 Tuning usp_SearchPostsByLocation
- 4.5 Tuning usp_RptQuestionsAnsweredForUser
- 4.6 Tuning usp_SearchUsers
- 4.7 Tuning usp_RptPostLeaderboard
- 4.8 Tuning usp_RptAvgAnswerTimeByTag
- 4.9 Tuning usp_GetTagsForUser
- Bonus Lab: Seeing the Effects of SQL Server 2019 Compat Mode
- Bonus: Storytelling Time