New Features in SQL That Will Change the Way You Tune

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Dwight Taylor
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1 New Features in SQL That Will Change the Way You Tune LANCE TIDWELL

2 Lance Tidwell Senior DBA,

3 Agenda New Cardinality Estimator (2014) Query Store (2016) Automatic Plan Correction (2017) Adaptive Query Processing (2017) Automatic Index Management (Azure SQL Database)

4 Meet Quincy Hi, I m Quincy the query adventuring pug!! Quincy is an adventurous pug who loves to explore queries and execution plans.

5 Cardinality Estimator What is the Cardinality Estimator? In the building of a execution plan, the Cardinality Estimator is what estimates the number of rows that will flow through each operator.

6 Cardinality Estimator What changed in SQL Server 2014? Increased Correlation Assumption for Multiple Predicates Modified Ascending Key and Out- Of-Range Value Estimation Join Estimate Algorithm Changes

7 Cardinality Estimator What changed in SQL Server 2014?

8 Cardinality Estimator Potential pitfall to watch out for Migration to SQL Server 2014 and above can cause plan regression.

9 Query Store What is the Query Store? The query store captures a history of queries, plans, and runtime stats. `

10 Query Store What can you do with it? Gives insight to performance stats for various queries and plans over time. Force a specific plan to be used. Track query regression. Lets you see the wait stats information on a specific query/plan (2017).

11 Query Store Potential pitfalls to watch out for There is overhead in having Query Store running. (But worth it in most cases) It is hard to tell if a plan has been forced by just looking at an execution plan. User could force a bad plan and cause degraded performance.

12 DEMO

Automatic Plan Correction What is the Automatic Plan Correction? Using Query Store, whenever plan choice regression is detected, it will automatically force the last good plan.

13 Automatic Plan Correction What is the Automatic Plan Correction? Using Query Store, whenever plan choice regression is detected, it will automatically force the last good plan. If the forced plan is found to be no better than regressed plan, then it will be unforced. If the forced plan is better than the regressed plan, the forced plan will be kept until the underlying schema or statistics change.

14 Automatic Plan Correction Potential pitfalls to watch out for If you have never had a good plan, you have nothing to fall back on. Putting all trust in any new automation tool is uncomfortable and risky. Forcing plans should be a temporary fix.

15 DEMO

16 Adaptive Query Processing What is Adaptive Query Processing? Interleaved execution Batch-mode memory grant feedback Batch-mode adaptive joins

17 Adaptive Query Processing Interleaved execution A Multi-Statement Table Valued Function (MSTVF) instead of having a fixed cardinality estimation of 100 rows, will now have a more accurate cardinality estimation A plan with a MSTVF will now materialize the MSTVF to get the cardinality estimation and use that number to help determine all down stream operations.

18 Adaptive Query Processing Batch-mode memory grant feedback The memory grant for a query execution will no longer be a static amount that is determined when the plan is created. On repeated calls of a query, the plan will now adjust the memory grant to help avoid temp spills of under-estimated grants, and concurrency issues of over-estimated grants.

Adaptive Query Processing Batch-mode adaptive joins Adaptive joins will choose at the time of execution between doing a nested loop join or a hash join.

19 Adaptive Query Processing Batch-mode adaptive joins Adaptive joins will choose at the time of execution between doing a nested loop join or a hash join. A threshold number is created with the adaptive join to determine if the number of row exceeds that number a hash join will be used instead of a nested loop Currently only works with columnstore indexes.

20 Adaptive Query Processing Potential pitfall to watch out for Interleaved execution: Though cardinality of MSTVFs may be more accurate, you could get a worse plan.

21 DEMO

22 Automatic Index Management What is Automatic Index Management? Only in Azure SQL Database. Automatically create or drop indexes that the system determines will help performance. A created index will only be retained if performance of affected queries improve. A dropped index will be recreated if queries run slower due to its absence.

23 Automatic Index Management Potential pitfalls to watch out for Putting all trust in any new automation tool is uncomfortable and risky. Black box with limited options.

24 Lance

Triangle SQL Server User Group Adaptive Query Processing with Azure SQL DB and SQL Server 2017

Triangle SQL Server User Group Adaptive Query Processing with Azure SQL DB and SQL Server 2017 Joe Sack, Principal Program Manager, Microsoft Joe.Sack@Microsoft.com Adaptability Adapt based on customer