Cost Based Optimizer - Part 2 of 2

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Cost Based Optimizer – 2 of 2

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Oracle. Performance. Now.

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Agenda

• Cost Based Optimizer and its impact on performance• Skewed Data• Histograms• Impact

– Performance (Logical I/O Impact)– Performance (Join Strategy)– Bind Variables– Cardinality and Cost

• Conclusion


Cost Based Optimizer


Cost Based Optimizer (CBO)

• The CBO in reality is a complex decision making software– Use several Database Initialization Parameters

• These are listed in the 10053 trace file

– Uses several session level initialization parameter• These are parameters at the session level that override the

database initialization parameters

– Uses statistics about the objects (Tables, Indexes)– Hints to the optimizer– Uses Statistics about the system (CPU, Disk etc)– Use this information and makes decisions on the “best way” to

generate an execution plan– Use Information about the skew of the column if that

information is gathered


CBO will be part of your life if you keep working with Oracle.

• The cost-based query optimizer (CBO)…– Uses data from a variety of sources– Estimates the costs of several execution plans– Chooses the plan it estimates to be the least expensive

• Characteristics– Adapts to changing circumstances– Frustrating if you don’t know what it considers as input

• Works great if you know how to use it

• But produces very poor results if you lie to it

– The only query optimizer supported by Oracle Corporation from release 10 onward


The cost-based query optimizer chooses the plan that it computes as having the lowest estimated cost.

• Don’t assume the following are identical– CBO’s estimated cost of an execution plan– The actual cost of an execution plan

• CBO’s cost estimate can be imperfect– Are your CBO inputs perfect?– CBO isn’t perfect, but by 9.2 it’s almost always good enough

• Without properly collected statistics, the CBO will– use RBO if no statistics exist on any object in the statement– use default statistics if statistics exist for a single object in the

statement but not others– use dynamic sampling to generate statistics (based on

parameter setting and Oracle version)


Cost Based Optimizer

dbms_stats.gather_database_stats

execution

databasetable and

indexstatistics

dbms_stats.gather_system_stats

execution

systemCPU and

I/Ostatistics

databaseschemaconfig

index, partitionmanipulation, etc.

Oracle cost-basedoptimizer

Oraclequery cost

model

execution plan

application SQLmanipulation

SQL text

Oracle DBMSauthorship

Oracleinstance

parameters

parameter editsstored outlinemanipulation

storedoutlines


Execution plan changes can result in profoundly different application performance.

A. Table size change

B. Device latency change

C. Execution plan change

• Type C performance changes are the most profound

200 400 600 800 1000rows

20

40

60

80

100LIOs C

1000 2000 3000 4000 5000rows

1000

2000

3000

4000

5000LIOs B

5000 10000 15000 20000 25000 30000 35000rows

2000

4000

6000

8000

10000

12000

14000

16000LIOs A

size change

performance change

performance change

performance change


Recap

• The CBO is a complex piece of software• It uses several data points to calculate the cost of the execution

plan and will choose the plan with the lowest cost• It is dynamic and will adapt to changing data better than the Rule

Based Optimizer• A good understanding of the Cost Based Optimizer is imperative

in understanding the rationale behind some of the choices


Skewed Data


Skewed Data

• Skewed Data is where the data distribution is not uniform• A good example is the owner column for dba_objects• The column is highly skewed• Select owner,count(*) from dba_objects • Group by owner;


Some kinds of data skew naturally; some don’t.

• Guaranteed to be skewed– E.g., status attribute (open | closed) of a sales order table

• Possibly not skewed– E.g., sale date attribute of a sales order table


Histograms


What are the costs and benefits of histograms?

• Benefits of histograms– CBO sometimes needs the information to make good

decisions• Costs of histograms

– Computing histograms will consume extra computing capacity during the statistics collection

– Some CPU time and extra latching is required during plan determination for the optimizer to consider histograms


Histograms provide the optimizer with better information from which to derive an execution plan for a query.

• A histogram is a graphic representation of frequency distribution by means of rectangles whose widths represent class intervals and whose heights represent corresponding frequencies

• Oracle implements histograms in two ways– Height-balanced – created if column NDV > SIZE– Frequency – created if column NDV <= SIZE


Types of Histograms

• Frequency– Every distinct value in the column will have a count of how

many occurrences of that value• Height Balanced Histograms

– All histogram entries will have the same value but a range for the columns will be used


Frequency Histogram

020406080

100120140160180200

SCOTT SYS SYSTEM SYSMAN


Height Balanced Histogram

0102030405060708090

100

AB-SC SC-SYS SYS-SYSM

SYSM-SYST


Histograms can be gathered by setting the parameter for METHOD_OPT.

For a specific column:

FOR COLUMNS column_x SIZE <n|REPEAT|AUTO|SKEWONLY>

For all the columns in a table:

FOR ALL COLUMNS

For only the columns that have an index:

FOR ALL INDEXED COLUMNS

EXEC DBMS_STATS.GATHER_TABLE_STATS(

ownname=>'OP',

tabname=>'my_table',

method_opt=>'FOR COLUMNS column_x SIZE 10')


Histograms are not useful in all cases.

• Histograms are not useful for columns with the following characteristics:– All (or most) predicates on the column use bind variables– The column data is uniformly distributed– The column is unique and is used only with equality

predicates– Data distribution changes frequently and statistics aren't

collected to match


Even in the most recent Oracle versions, histogram optimization doesn’t completely work with bind variables.

• Oracle version 8– Use of bind variables prohibits histogram optimization

• Oracle version 9 and above– Oracle query optimizer “peeks” at bind value to use histogram

optimization– But only on initial hard parse of a query


Be prepared for how application developers might have worked around skew problems.

• The old-fashioned RBO technique

1. Create the index

2. Hard-code the selective query with “status=1”

3. Hard-code the un-selective query with “status+0=1”• A CBO technique

1. Create the index

2. Hard-code the selective query with /*+ index(t) */

3. Hard-code the un-selective query with /*+ full(t) */

Don’t resort to either of these!


Where Histogram Information is Stored

• DBA_TAB_HISTOGRAMS• DBA_TAB_COL_STATISTICS


Demo

Histogram Data Dictionary Tables


Impact

Performance in terms of Logical I/O’s


Demo

Cardinality


Demo

Join Cardinality


Recap

• Histograms can be really useful when gathered on skewed columns

• Histograms are specific to your data and version• Test it out and prove that gathering histograms is beneficial• Be careful of bind variable substitutions as histograms may not

be used

Cost Based Optimizer - Part 2 of 2

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