12c Optimizer Overview

8/13/2019 12c Optimizer Overview

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Copyright 2012, Oracle and/or its affiliates. All rights reserved.1


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Oracle Database 12cOptimizer Overview

Maria ColganMaster Product Manager


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Agenda

Adaptive Query Optimization

Optimizer Statistics enhance

SQL Plan Management enha


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Adaptive PlansJoin methods

Parallel distribution methods

Adaptive Statistics

Discovered at compile timeDiscovered at run time

Adaptive Query OptimizationOverview

Adaptive Query

Optimization

Adaptive Plans

Join

Methods

Parallel

distribution

Methods

At

compil

time


5/102Copyright 2012, Oracle and/or its affiliates. All rights reserved.5

Query: Find all of theproducts with a unit price of15 that we have sold morethat 1 of

Two possible join methods

for this query

Nested Loops

Hash Join

Adaptive Execution PlansAdapt join methods


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Alternative sub-plans arepre-computed

Sub-plans stored in thecursor

Stats collect inserted beforejoin

Rows buffered until finaldecision is made


Table scan

Order _items

NESTED LOOPS

Index Scan

Prod_info_ind

StatisticsCollector

Default Plan is a nested lo

HASH JO

Rows coming out of order_itemstabl

to a point If row count is less than the th

nested Loops otherwise switch to hash j


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Number of rows seen instatistics collector exceedsthreshold

Plan switches to hash join

Statistics collect disabled

Plan resolved on firstexecution & remains thesame for subsequentexecutions


Final Plan is a hash join

Statistics collector disabled after decisio

made and becomes a pass through ope

Table scan

Order _items

NESTED LOOPS

Index ScanProd_info_ind

HASH JOI

T

P

StatisticsCollector


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Explain plan commandalways shows default plan

Example shows a nestedloops join as default plan

No statistics collector shownin plan

Adaptive Execution PlansDisplaying the default plan


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After the statement hascompleted useDBMS_XPLAN.DISPLAY_CURSOR

tosee the final plan selected

Example shows that hash

join picked at execution timeAgain the statistics collectoris not visible in the plan

Adaptive Execution PlansDisplaying the final plan


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Full adaptive plan displayedwhen format parameter+adaptive is set

Example shows both the

nested loops and hash joinin the plan

Adaptive Execution PlansDisplaying the full adaptive plan


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Additional informationdisplayed on why operationsare inactive can be seen

with format parameter+report

Adaptive Execution PlansDisplaying plan with +adaptive & +report formats


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New column in V$SQLIS_RESOLVED_ADAPTIVE_PLAN

Indicates statement had anadaptive plan which wasresolved on first execution

Resolved plan is used forsubsequent executions

Statistics collectors andbuffering is disabled

Adaptive Execution PlansIndicator in V$SQL


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Adaptive plans are enabled bydefault

Can be put in reporting mode

OPTIMIZER_ADAPTIVE_REPORTING_ONLY

Reporting mode shows what

would have happened duringexecution in the plan

Adaptive Execution PlansReporting mode


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Adaptive Execution Plans

Join method decision deferred until runtime Default plan is computed using available statistics

Alternate sub-plans are pre-computed and stored in the curso

Statistic collectors are inserted at key points in the plan

Final decision is based on statistics collected during execut

Default plan and sub-plans have a valid range for statistics co

If statistics prove to be out of range, the sub-plans will be swa

Requires buffering near the swap point to avoid returning rows

Possible sub-plans are nested loop joins or hash joins and

Adaptive join methods


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Adaptive Execution PlansAdaptive parallel data distribution

User connects to the

databaseUser

Shadow process is

spawned

WhparthebecCo

Parallel servers communicate among themselves & theQC using messages that are passed via memory buffersin the shared pool

How the data is distributed among the processes isbased on the operation being done & number of rowsexpected

QCsepo

the

Paindpe


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Adaptive Distribution Methods

Cardinality based distribution skew common scenario Crucial for parallel join of very small data sets with very large data

Distribution method decision based on expected number of row

New adaptive distribution method HYBRID-HASH

Statistic collectors inserted in front of PX process on the left hand

If actual number of rows less than threshold, switch from HASH to

Threshold number of total rows < 2x DOP

Enabled by default

Hybrid-HASH Join


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Hybrid hash join betweenEMP and DEPT

Distribution method basedon runtime stats

Statistics collector insertedin front of PX processesscanning DEPT

Adaptive Execution PlansAdaptive parallel data distribution Query

coordinator

P1 P2 P3

P5

P6

P7

P8

DEPT

Statistics Collectorthreshold 2X DOP


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Distribution methodbased on runtime stats

Statistics collectorinserted in front of PXprocesses scanningDEPT



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If DEPT uses Broadcast- EMP uses Round-Robin


Broadcast/Round Robin

DOP used is 6Number of rows returned is 4

Broadcast used because 24 rows dist


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If DEPT uses Broadcast

- EMP uses Round-Robin

If DEPT used HASH- EMP uses HASH


Broadcast/Round Robin

Hash/Hash

DOP used is 2Hash used because only 4 rows distrib


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Adaptive Statistics

Discovered at compile timeDiscovered at run time


Adaptive Query

Optimization

Adaptive Plans

Join

Methods

Parallel

distribution

Methods

At

compil

time


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Dynamic Statistics

During compilation optimizer decides if statistics are sufficiegenerate a good plan or not

Dynamic statistics are used to compensate for missing, staincomplete statistics

They can be used for table scans, index access, joins and g

One type of dynamic statistics is dynamic sampling


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Dynamic sampling has anew level 11(AUTO)

Decision to use dynamicsampling depends on thecomplexity of predicate,existing statistics and

total execution time

Dynamic statistics sharedamong queries

Dynamic StatisticsDynamic Sampling


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Dynamic Statistics SGA

SHARED_POOL

Select *

Fromproduct_information

Where list_price-min_price=29

And category_id not in (11,22)

And prod_name like Smart%;

Optimizer checksfor existing statisticsin data dictionary

2

SHARED DYNAMIC

DATA DICTIONARY

SQL statementis submitted

1

Optimizer determines

plan using dynamicstatistics

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Cursor 0: Select * from prod

Dynamic samplingoccurs on small number ofblocks from tableResulting dynamic statisticsare stored in cache

4

Statistics found but need to beaugmented due to complex predicates

3

Table cardinality estima


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Dynamic Statistics SGA

SHARED_POOL

Select supplier_id, prod_name

Fromproduct_information

Where list_price-min_price=29

And category_id not in (11,22)

And prod_name like Smart%;

Optimizer checksfor existing statisticsin data dictionary

7

SHARED DYNAMIC

DATA DICTIONARY

Different SQLstatement issubmitted withsame predicates

6


plan using dynamicstatistics

10

Cursor 0: Select supplier_id

Necessary Dynamicstatistics found in sharedcache

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Statistics found but need to beaugmented due to complex predicates

8

Table cardinality estima


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Adaptive Statistics

During execution optimizer estimates are compared to exec If statistics vary significantly then a new plan will be chosen

subsequent executions based on execution statistics

Re-optimization uses statistics gathered from previous exec

First introduced as Cardinality Feedback in 11.2

Re-optimization


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Adaptive Statistics

Statistics gathered about data volume and data type seen d If execution statistics vary significantly statement will be har

the next execution using the execution statistics instead

Statements are only monitored once if they dont show sign

differences initially they wont change in the future

Only individual table cardinalities and group by estimates e Information is stored in the cursor only and is lost if cursor a

Cardinality Feedback pre 12c


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Initial execution of a queryis monitored

Example shows initial planis hash join between sales

and customers

Cardinality estimates 8X off

Adaptive StatisticsCardinality Feedback pre 12c

Initial Cardinality estimat


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Execution statistics used toreparse the statement onthe second execution

New plan shows correctcardinality estimates and a

new join order

Information learnt is storedin the cursor only and is lostif cursor ages out

Adaptive StatisticsCardinality Feedback pre 12c

Execution Plan after the second execu


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Adaptive Statistics

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 Pla

Re-optimization in 12c


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New column in V$SQLIS_REOPTIMIZABLE

Indicates that the statementwill be re-parsed on the nextexecution

Adaptive StatisticsRe-optimizationindicator in V$SQL


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On second executionstatement is re-parsedusing execution statistics

New child cursor createdwith new plan

New plan changes the joinorder

Adaptive StatisticsAdaptive Plans & Re-optimization working together


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Adaptive Statistics

Directives are additional information used during optimizatiogenerate a better plan

For example, when table T1 is joined to T2 use dynamic statisaccurate cardinality estimate

Directives are collected on query expressions not at a stateme

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

SQL Plan Directives


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SQL Plan Directives SGA

SHARED_POOL

Select *

FROM customers

where state =CA

AND country=USA;

SQL statementis submitted

Cursor 0: Select * from cust..


plan

2

DIRECTIV

During executioncardinality estimatediscovered to be wrongand directive created

3

IS_REOPTIMIZABLE

Directive: Use DS fowhen column city, co

1

Sysaux Tablespace


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SQL Plan Directives

Select *

FROM customers

where state=CA

AND country=USA;

SQL Directives

Sysaux Tablespace

SGA

This execution andsubsequent executionuse child cursor 1

6

Directive: Use DS fowhen column city, co

SHARED_POOLCursor 0: Select * from cust

IS_REOPTIMIZABLE =

Optimizer determinesnew plan usingexecution statistics

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Cursor 1: Select * from custoIS_REOPTIMIZABLE =

Directpersissysau

Extension Binds Stats Sqlid

SQL Directives

DIRECTIVDIRECTIV

Same SQLstatement issubmittedagain

4


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SQL Plan Directives

Select email

FROM customers

where state=MA

AND country=USA;

New SQLstatement issubmitted oncustomers table

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Optimizerchecks fordirectives oncustomers tableand finds one onthe columns stateand country

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Cursor 0: Select email from

Optimizer determinesplan with help of

directive

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Optimizer addscolumn group creationfor state & countrycolumns to next statisticsgather on customer table

10

SGADIRECTIV

SHARED_POOL

Sysaux Tablespace

Extension Binds Stats Sqlid

SQL Directives

DIRECTIV


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Number of SQL PlanDirectives used for astatement is displayed inthe notes section of theexecution plan

In this case one SQL

Plan Directive was used

SQL Plan DirectivesIn use


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SQL Plan DirectivesMonitoring

Auto flushed every 15 minutes


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Auto flushed every 15 minutes


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SQL Plan Directive statewas MISSING_STATS

Indicates Oracle canautomatically gatherstatistics to improve theoriginal mis-estimation

In this case a columngroup on STATEandCOUNTRYwill addressthe problem

SQL Plan DirectivesGather statistics to allow automatic creation of missing statist


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Adaptive StatisticsDiscovered at compile time

Discovered at run time


Adaptive Query

Optimization

Adaptive Plans

Join

Methods

Parallel

distribution

Methods

At

compil

time


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Summary

Optimizer begins to learn from its mistakes

Adaptive Plans

Adapt join methods

Adapt parallel distribution methods

Adaptive Statistics

Dynamic statistics Re-optimization

SQL Plan Directives

And lots of other great features!


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Agenda

Adaptive Query Optimization

Optimizer Statistics enhance

SQL Plan Management enha


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Statistics

Enhancements

New types of histograms

Online statistics gathering

Session level statistics for G

Enhanced incremental statis

Concurrent statistics gatheri

Automatic detection of colum

Statistic gathering reporting

Hi t


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Histograms

Histograms tell Optimizer about the data distribution in a Co

Creation controlled by METHOD_OPT parameter

Default create histogram on any column that has been usedWHERE clause or GROUP BY of a statement AND has a d

Relies on column usage information gathered at compilatio

stored in SYS.COL_USAGE$ Four types of histograms

Frequency

Top-Frequency

Oracle Confidential

Height balanced

Hybrid

Hi t


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Histograms

A frequency histogram is only created if the number of distin

a column (NDV) is less than 254 values

Frequency Histograms (FREQUENCY)

Frequency histogram

Oracle Confidential

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Histograms

Traditionally a frequency histogram is only created if NDV

Documents

12c Optimizer Overview