2. An Insiders View of How the Optimizer Works
Tang Tao Oracle University Principal Instructor

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4. Optimizer evolution Databases became more feature rich
Optimizer evolved to
be cost based
CBO
Rule are not enough
Databases became more feature rich
As environment changes Potential for plan changes
RULES
Reactive tuning with the use of advisors and auto jobs
In the beginning there were rules
CBO
Optimizer proactively adapts to become self-learning
Databases become more real-time, ad-hoc environments
Reactive tuning not enough

5. Adaptive Query Optimization
Overview
Adaptive Query Optimization
Adaptive Plans Join methods Parallel distribution methods
Adaptive Statistics Discovered at compile time Discovered at run time
Adaptive Plans
Adaptive Statistics
Join Methods
Parallel distribution Methods
At compile time
At run time

6. Adaptive Execution Plans
Adapt join methods
Query: Find all of the products with a unit price of 15 that we have sold more that 1 of
Two possible join methods for this query
Nested Loops
Hash Join

7. Adaptive Execution Plans
Adapt join methods
Rows coming out of order_items table are buffered up to a point If row count is less than the threshold use nested Loops otherwise switch to hash join
Alternative sub-plans are pre-computed
Sub-plans stored in the cursor
Stats collect inserted before join
Rows buffered until final decision is made
Statistics Collector
Table scan
Order _items
NESTED LOOPS
Index Scan
Prod_info_ind
HASH JOIN
Table scan
Prod_info
Default Plan is a nested loops join

8. Adaptive Execution Plans
Adapt join methods
Statistics collector disabled after decision is made and becomes a pass through operation
Table scan
Order _items
NESTED LOOPS
Index Scan
Prod_info_ind
HASH JOIN
Prod_info
Statistics Collector
Number of rows seen in statistics collector exceeds threshold
Plan switches to hash join
Statistics collect disabled
Plan resolved on first execution & remains the same for subsequent executions
The plan is resolved after one execution and after that, it is always the same, and the statistics collector and buffering are disabled.
Final Plan is a hash join

9. Adaptive Execution Plans
Displaying the default plan
Explain plan command always shows default plan
Example shows a nested loops join as default plan
No statistics collector shown in plan

10. Adaptive Execution Plans
Displaying the final plan
After the statement has completed use DBMS_XPLAN.DISPLAY_CURSOR to see the final plan selected
Example shows that hash join picked at execution time
Again the statistics collector is not visible in the plan

11. Adaptive Execution Plans
Displaying the full adaptive plan
Full adaptive plan displayed when format parameter ‘+adaptive’ is set
Example shows both the nested loops and hash join in the plan

12. Adaptive Execution Plans
Displaying plan with +adaptive & +report formats
Additional information displayed on why operations are inactive can be seen with format parameter ‘+report’

13. Adaptive Execution Plans
Indicator in V$SQL
New column in V$SQL IS_RESOLVED_ADAPTIVE_PLAN
Indicates statement had an adaptive plan which was resolved on first execution
Resolved plan is used for subsequent executions
Statistics collectors and buffering is disabled

14. Adaptive Execution Plans
Reporting mode
Adaptive plans are enabled by default
Can be put in reporting mode
OPTIMIZER_ADAPTIVE_REPORTING_ONLY
Reporting mode shows what would have happened during execution in the plan
Note: Reporting mode incurs the same overhead of buffering, plan size, etc. that regular mode does. We just don’t change the plan on the fly.

15. Adaptive Execution Plans
Adaptive join methods
Join method decision deferred until runtime
Default plan is computed using available statistics
Alternate sub-plans are pre-computed and stored in the cursor
Statistic collectors are inserted at key points in the plan
Final decision is based on statistics collected during execution
Default plan and sub-plans have a valid range for statistics collected
If statistics prove to be out of range, the sub-plans will be swapped
Requires buffering near the swap point to avoid returning rows to user
Possible sub-plans are nested loop joins or hash joins and vice versa

16. Adaptive Execution Plans
Adaptive parallel data distribution
User
User connects to the database
Shadow process is spawned
When user issues a parallel SQL statement the shadow process becomes the Query Coordinator
Parallel servers communicate among themselves & the QC using messages that are passed via memory buffers in the shared pool
How the data is distributed among the processes is based on the operation being done & number of rows expected
QC gets parallel servers from global pool and distributes the work to them
Parallel servers - individual sessions that perform work in parallel
Data distribution is necessary when parallel execution is used
Distribution method decision based on operation and expected number of rows
New adaptive distribution method HYBRID-HASH
Statistic collectors are inserted in front of the parallel server process on the left hand side of the join
If the actual number of rows is less than threshold, switch from HASH distribution to Broadcast

17. Adaptive Distribution Methods
Hybrid-HASH Join
Cardinality based distribution skew common scenario
Crucial for parallel join of very small data sets with very large data sets
Distribution method decision based on expected number of rows
New adaptive distribution method HYBRID-HASH
Statistic collectors inserted in front of PX process on the left hand side of the join
If actual number of rows less than threshold, switch from HASH to Broadcast
Threshold number of total rows < 2x DOP
Enabled by default

18. Adaptive Execution Plans
Adaptive parallel data distribution
# rows returned less than threshold so rows distributed via Broadcast
Query coordinator
Hybrid hash join between EMP and DEPT
Distribution method based on runtime stats
Statistics collector inserted in front of PX processes scanning DEPT P1 P2 P3 P4
Statistics Collector threshold 2X DOP
DEPT P5 P6 P7 P8
EMP
Threshold is set at 2X DOP of the query

19. Adaptive Distribution Methods
Broadcast/Round Robin
Hybrid hash join between EMP and DEPT
Distribution method based on runtime stats
If DEPT uses Broadcast - EMP uses Round-Robin
DOP used is 6
Number of rows returned is 4
Broadcast used because 24 rows distributed (6 X 4)

20. Adaptive Query Optimization
Overview
Adaptive Query Optimization
Adaptive Plans Join methods Parallel distribution methods
Adaptive Statistics Discovered at compile time Discovered at run time
Adaptive Plans
Adaptive Statistics
Join Methods
Parallel distribution Methods
At compile time
At run time

21. Dynamic Statistics
During compilation optimizer decides if statistics are sufficient to generate a good plan or not
Dynamic statistics are used to compensate for missing, stale, or incomplete statistics
They can be used for table scans, index access, joins and group bys
One type of dynamic statistics is dynamic sampling
Dynamic statistics is a new and improved flavor of dynamic sampling.
Dynamic statistics allow the optimizer to augment existing statistics to
Get more accurate cardinality estimates for single table accesses joins
And group by predicates.

22. Dynamic Statistics Dynamic Sampling
Dynamic sampling has a new level 11(AUTO)
Decision to use dynamic sampling depends on the complexity of predicate, existing statistics and total execution time
Dynamic statistics shared among queries
Dynamic sampling has a new level, 11 in 12c
In level 11 the optimizer automatically decides
To use DS base on the complexity of the predicates
The existing base statistics and the total execution
Time expected for the SQL statement.

23. Dynamic Statistics SGA 5 4 2 3 1 DATA DICTIONARY
SHARED_POOL
SHARED DYNAMIC STATISTICS
Select *
From product_information
Where list_price-min_price=29
And category_id not in (11,22)
And prod_name like ‘Smart%’;
Table cardinality estimate for prod_info
Optimizer determines plan using dynamic statistics 5
Cursor 0: Select * from product_information …
SQL statement is submitted 1
Dynamic sampling occurs on small number of blocks from table
Resulting dynamic statistics are stored in cache 4
Optimizer checks
for existing statistics in data dictionary 2
DATA DICTIONARY
Statistics found but need to be augmented due to complex predicates 3

24. Dynamic Statistics SGA 10 9 7 8 6 DATA DICTIONARY
SHARED_POOL
SHARED DYNAMIC STATISTICS
Select supplier_id, prod_name
From product_information
Where list_price-min_price=29
And category_id not in (11,22)
And prod_name like ‘Smart%’;
Table cardinality estimate for prod_info
Optimizer determines plan using dynamic statistics 10
Cursor 0: Select supplier_id, prod_name …
Different SQL statement is submitted with same predicates 6
Necessary Dynamic statistics found in shared cache 9
Optimizer checks
for existing statistics in data dictionary 7
DATA DICTIONARY
Statistics found but need to be augmented due to complex predicates 8

25. Adaptive Statistics Re-optimization
During execution optimizer estimates are compared to execution statistics
If statistics vary significantly then a new plan will be chosen for subsequent executions based on execution statistics
Re-optimization uses statistics gathered from previous executions
First introduced as Cardinality Feedback in 11.2

26. Adaptive Statistics Cardinality Feedback pre 12c
Initial execution of a query is monitored
Example shows initial plan is hash join between sales and customers
Cardinality estimates 8X off
Initial Cardinality estimates are more than 8X off

27. Adaptive Statistics Cardinality Feedback pre 12c
Execution Plan after the second execution
Execution statistics used to reparse the statement on the second execution
New plan shows correct cardinality estimates and a new join order
Information learnt is stored in the cursor only and is lost if cursor ages out
Estimates are now correct

28. Adaptive Statistics Re-optimization in 12c
Join statistics are also monitored
Works with adaptive cursor sharing for statement with binds
New Column in V$SQL IS_REOPTIMIZABLE
Information found at execution time is persisted as SQL Plan Directives

29. Adaptive Statistics Re-optimization – indicator in V$SQL
New column in V$SQL IS_REOPTIMIZABLE
Indicates that the statement will be re-parsed on the next execution

30. Adaptive Statistics Adaptive Plans & Re-optimization working together
Adapt join method example showed the join change from nested loops to hash join
But cursor is also marked IS_REOPTIMIZABLE
Why?
Re-optimization will occur on second execution because cardinality estimates off
Initial Cardinality estimates are off

31. Adaptive Statistics Adaptive Plans & Re-optimization working together
On second execution statement is re-parsed using execution statistics
New child cursor created with new plan
New plan changes the join order

32. Adaptive Statistics SQL Plan Directives
Directives are additional information used during optimization to generate a better plan
For example, when table T1 is joined to T2 use dynamic statistics to get accurate cardinality estimate
Directives are collected on query expressions not at a statement level
Allows for directives to be used for multiple statements
Persisted on disk in the SYSAUX tablespace
Directives will be automatically maintained
Managed using the new package DBMS_SPD

33. SQL Plan Directives SGA 2 3 1 DIRECTIVE CACHE SHARED_POOL
During execution cardinality estimate discovered to be wrong and directive created 3
IS_REOPTIMIZABLE = Y
Directive: Use DS for customers table when column city, country are used
Select *
FROM customers
where state =‘CA’
AND country=‘USA’;
Cursor 0: Select * from customers ……..
Optimizer determines plan 2 1
SQL statement is submitted
Sysaux Tablespace

34. SQL Plan Directives SGA 5 6 4 SQL Directives SQL Directives
Directives periodically persisted to disk in the sysaux tablespace
Extension
Binds
Stats
Sqlid
SQL Directives
DIRECTIVE CACHE
DIRECTIVE CACHE
Directive: Use DS for customer table when column city, country are used
Select *
FROM customers
where state=‘CA’
AND country=‘USA’;
Optimizer determines new plan using execution statistics 5
Cursor 1: Select * from customers …..
IS_REOPTIMIZABLE = N
SHARED_POOL
Cursor 0: Select * from customers …..
Same SQL statement is submitted again 4
IS_REOPTIMIZABLE = Y
This execution and subsequent execution use child cursor 1 6
Sysaux Tablespace
SQL Directives

35. SQL Plan Directives SGA 9 10 7 8 SQL Directives DIRECTIVE CACHE
Extension
Binds
Stats
Sqlid
SQL Directives
DIRECTIVE CACHE
DIRECTIVE CACHE
Select
FROM customers
where state=‘MA’
AND country=‘USA’;
Cursor 0: Select from customers
Optimizer determines plan with help of directive 9
SHARED_POOL
Optimizer adds column group creation for state & country columns to next statistics gather on customer table 10
New SQL statement is submitted on customers table 7
Optimizer checks for directives on customers table and finds one on the columns state and country 8
Sysaux Tablespace

36. Summary Optimizer begins to learn from its experiences Adaptive Plans
Adapt join methods
Adapt parallel distribution methods
Adaptive Statistics
Dynamic statistics
Re-optimization
SQL Plan Directives

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