You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/pAle50A5aoG Since the new cardinality estimator was released in SQL Server 2014, every version on SQL Server has introduced new great features designed to help your queries run at max performance. In 2016, it was Query Store; in 2017, Adaptive Query Processing and Automatic Tuning have been introduced. Join IDERA and Lance Tidwell as he explores these new features and how they can make your life easier as a DBA. Lance will also look at some of the pitfalls and potential issues that can arise from these new features. This is a special Geek Sync you will not want to miss! About Lance: Lance Tidwell, from the Austin, TX, area, has worked as a SQL Server DBA for the last 11 years. During his time as a DBA he has found his passion for all things SQL, but specifically for performance tuning. He has a life-long quest to always be learning new things; currently this quest has lead him to explore more about the Data Analytics/ Data Science areas, as well as working toward the Microsoft Professional Program Certificate in Data Science. During his spare time, he volunteers at his son's school as a member of Watch D.O.G.S. and as an off-ice official for the American Hockey League (AHL).

1. New Features in SQL That Will Change the
Way You Tune
Lance Tidwell
February 21, 2018

2. Agenda
Adaptive Query Processing (2017)
New Cardinality Estimator (2014)
Query Store (2016)
Automatic Plan Correction (2017)
3
1
2
4
5 Automatic Index Management
(Azure SQL Database)

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

4. 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.

5. 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

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

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

9. 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).

10. 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.

11. Query Store

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

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. Additonal Information
Joe Sack whitepaper: https://msdn.microsoft.com/en-us/library/dn673537.aspx
Cardinality Estimator
Query Store
https://www.sqlskills.com/blogs/erin/category/query-store/
https://www.scarydba.com/?s=query+store
Adaptive Query Processing
https://docs.microsoft.com/en-us/sql/relational-
databases/performance/adaptive-query-processing
Automatic Tuning
https://docs.microsoft.com/en-us/sql/relational-
databases/automatic-tuning/automatic-tuning
Bonus
Microsoft Gray System Lab Technical Report
on improving UDFs: ift.tt/2jOzglW
@Lance_LT silentdba@gmail.com