1. Decoding the Cardinality Estimator to Speed Up Queries
Joe Obbish
Decoding the Cardinality Estimator to Speed Up Queries

2. About Me Business Intelligence Developer for EHR company
Answer questions on dba.stackexchange.com
Blog about SQL Server at
Done performance tuning for thousands of queries

3. This Slide Intentionally Left Blank

4. Why are Some Queries Always Slow?
Data model or indexing problems
Unreasonable questions – “It’s slow if I cross join 2 tables with a million rows each”
Cardinality estimate issues

5. What is a Cardinality Estimate?
The estimated number of rows output by an operator in a query plan.

6. Why is This Important? Query plans are chosen on cost
Operator costs are mostly determined by estimates
Key estimates need to be close enough
Good performance with bad estimates often comes down to luck or processing power

7. Poor Estimates Often Lead to Poor Plans
Wrong table access type
Wrong join type
Wrong join order
Spills to tempdb
And much more!

8. The Root Cause of Poor Estimates
I try to find the root cause of cardinality estimate issues when I can.
Goal isn’t to understand or memorize every detail, but to have a strong foundation to tackle future problems.
The community can help you.

9. Basic Principles Use available statistics objects
Assume data is uniform
Always produce an estimate
Never estimate 0 rows
Don’t require consistency

10. Statistics
Use statistics instead of looking at table data

11. Data is Uniform There’s no data skew
Histogram is the truth, the whole truth, and nothing but the truth
Somewhat relaxed with new CE or with some trace flags

12. Always Produce an Estimate
CE always produces an estimate, even if it has to guess
Common guesses:
30% - unknown inequality
9% or 16.4% for BETWEEN (legacy and new CE)
10% for some other cases

13. Minimum Estimate You’ll never see a cardinality estimate of 0 rows
Optimizer assumes that you’re always looking for data that exists
Costing plans with 0 row estimates doesn’t really make sense

14. Inconsistency
Cardinality estimates do not need to be consistent between plans
Filters with > and <= can both give 30%, even though they must add up to 100% (ignoring NULLs)

15. Academic Demos All testing done on the new CE in SQL Server 2017
SQL Server 2008 R2 and 2012 only have the legacy CE

16. Our Statistics

17. Example 1
SELECT *
FROM T
WHERE ID = 1;

18. Example 1 Solution
SELECT *
FROM T
WHERE ID = 1;

19. Example 2
SELECT *
FROM T
WHERE ID = 2;

20. Example 2 Solution
SELECT *
FROM T
WHERE ID = 2;

21. Example 3
SELECT *
FROM T
WHERE ID = 7;

22. Example 3 Solution
SELECT *
FROM T
WHERE ID = 7;

23. Example 4 DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

24. Example 4 Solution DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

25. Example 5
SELECT *
FROM T
WHERE ID > 1;

26. Example 5 Solution
SELECT *
FROM T
WHERE ID > 1;

27. Example 6 DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

28. Example 6 Solution DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

29. Example 7 DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

30. Example 7 Solution DECLARE @local_variable BIGINT = 1; SELECT * FROM T
WHERE ID

31. Example 8 Solution
SELECT ID
FROM T
GROUP BY ID;

32. Example 9
SELECT *
FROM T
WHERE ABS(ID) = -1;

33. Example 9 Solution
SELECT *
FROM T
WHERE ABS(ID) = -1;

34. Filter Independence
How should filters on different columns be combined?
Is there some level of correlation between the columns or are they independent?

35. “Real World” Demos

36. Predicting Problems Window functions Column correlation
Other functions
Local variables with no RECOMPILE hint
Filtering on aggregates
Undeclared relationships in data
Data skew

37. Check For CE Problems
Easiest way is to look at actual plans and use what you know about the data
Is that estimate of 1 row reasonable?

38. Tricky Plans
Nested loop joins

39. Bitmap filters

40. Columnstore aggregate pushdown

41. Review
Try to figure out the root cause of a cardinality estimate issue that causes performance problems
Recognize patterns
Know if the problem might come back

42. Resources BOL Cardinality Estimation White Paper by Joe Sack on New CE
CE Questions on Stack Exchange
Blog post on containment
Blog post by Paul White on DISTINCT estimates
Blog post by Dmitry Pilugin on Join Estimation Internals

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