Best Practices for Oracle Exadata and the Oracle Optimizer

1 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
Best Practices for Oracle Exadata and the
Oracle Optimizer
Maria Colgan, Product Manager, Oracle Optimizer
Levi Norman, Product Marketing Director, Oracle Exadata

reserved.
• Fastest Data Warehouse & OLTP
• Best Cost/Performance Data Warehouse & OLTP
• Optimized Hardware
• Software Breakthroughs
• Scales from ¼ Rack to 8 Full Racks
Oracle Exadata Database Machine
Extreme performance. Lowest cost.

reserved.
Hybrid Columnar Compression Smart Flash
Cache
Smart Scan Queries
Up to
50X10X
+ ++
Oracle Exadata Innovation
Storage Server Software

reserved.
Standardized and Simple to Deploy
Delivered Ready-to-Run
• Oracle Exadata Database Machines Are The Same
Thoroughly Tested & Highly Supportable
Identical Configuration Used by Oracle Engineering
• Run Existing OLTP and DW Applications
30 Years of Oracle DB Capabilities
No Exadata Certification Required
• Leverage Oracle Ecosystem
Skills, Knowledge Base, People, & Partners

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Oracle Exadata in the Market
Rapid Adoption Across Geographies and Industries

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Agenda
• How to gather statistics
• Additional types of statistics
• When to gather statistics
• Statistics gathering performance

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How to Gather Statistics
• Analyze command is deprecated
– Only good for row chaining
• The GATHER_*_STATS procedures take 13 parameters
– Ideally you should only set the first 2-3 parameters
• SCHEMA NAME
• TABLE NAME
• PARTITION NAME
Use DBMS_STATS Package

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Use DBMS_STATS Package
• Your gather statistics commands should be this simple

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• Can change the default value at the global level
– DBMS_STATS.SET_GLOBAL_PREF
– This changes the value for all existing objects and any new objects
• Can change the default value at the table level
– DBMS_STATS.SET_TABLE_PREF
Changing Default Parameter Values for Gathering Statistics

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• CASCADE
• CONCURRENT
• DEGREE
• ESTIMATE_PERCENT
• METHOD_OPT
• NO_INVALIDATE
• GRANULARITY
• PUBLISH
• INCREMENTAL
• STALE_PERCENT
• AUTOSTATS_TARGET
(SET_GLOBAL_PREFS only)
Changing Default Parameter Values for Gathering Statistics
•The following parameter defaults can be changed:

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• # 1 most commonly asked question
– “What sample size should I use?”
• Controlled by ESTIMATE_PRECENT parameter
• From 11g onwards use default value AUTO_SAMPLE_SIZE
– New hash based algorithm
– Speed of a 10% sample
– Accuracy of 100% sample
Sample Size

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• Speed of a 10% sample
• Accuracy of 100% sample
Sample Size
Run Num AUTO_SAMPLE_SIZE 10% SAMPLE 100% SAMPLE
1 00:02:21.86 00:02:31.56 00:08:24.10
2 00:02:38.11 00:02:49.49 00:07:38.25
3 00:02:39.31 00:02:38.55 00:07:37.83
Column
Name
NDV with
AUTO_SAMPLE_SIZE
NDV with 10%
SAMPLE
NDV with 100%
SAMPLE
C1 59852 31464 60351
C2 1270912 608544 1289760
C3 768384 359424 777942

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Agenda

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Additional Types of Statistics
• Two types of Extended Statistics
– Column groups statistics
• Column group statistics useful when multiple column from the same
table are used in where clause predicates
– Expression statistics
• Expression statistics useful when a column is used as part of a
complex expression in where clause predicate
• Can be manually or automatically created
• Automatically maintained when statistics are gathered
When Table and Column Statistics are not enough

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Extended Statistics –
SELECT * FROM vehicles
WHERE model = ‘530xi’
AND color = 'RED’;
Column Group Statistics
SLIVERC320MERC
REDSLKMERC
RED911PORSCHE
SILVER530xiBMW
BLACK530xiBMW
RED530xiBMW
ColorModelMake
Vehicles Table
Cardinality #ROWS * 1 * 1 12 * 1 * 1 1
NDV c1 NDV c2 4 3
= =>
MAKE MODEL COLOR
BMW 530xi RED
=

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Extended Statistics
SELECT * FROM vehicles WHERE model = ‘530xi’
AND make = ‘BMW’;
SLIVERC320MERC
REDSLKMERC
RED911PORSCHE
SILVER530xiBMW
BLACK530xiBMW
RED530xiBMW
ColorModelMake
Vehicles Table
Cardinality #ROWS * 1 * 1 12 * 1 * 1 1
NDV c1 NDV c2 4 3
= => =
MAKE MODEL COLOR
BMW 530xi RED
BMW 530xi BLACK
BMW 530xi SLIVER

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Extended Statistics
• Create extended statistics on the Model & Make columns using
DBMS_STATS.CREATE_EXTENDED_STATS
New Column
with system
generated
name

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Extended Statistics
SELECT * FROM vehicles WHERE model = ‘530xi’
AND make = ‘BMW’;
SLIVERC320MERC
REDSLKMERC
RED911PORSCHE
SILVER530xiBMW
BLACK530xiBMW
RED530xiBMW
ColorModelMake
Vehicles Table
Cardinality calculated using column group statistics
MAKE MODEL COLOR
BMW 530xi RED
BMW 530xi BLACK
BMW 530xi SLIVER

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Extended Statistics
SELECT *
FROM Customers
WHERE UPPER(CUST_LAST_NAME) = ‘SMITH’;
• Optimizer doesn’t know how function affects values in the column
• Optimizer guesses the cardinality to be 1% of rows
SELECT count(*) FROM customers;
COUNT(*)
55500
Expression Statistics
Cardinality estimate
is 1% of the rows

reserved.
Extended Statistics
Expression Statistics
New Column with
system generated
name

reserved.
Extended Statistics
1. Start column group usage capture
Automatic Column Group Detection
• Oracle can automatically
detect column group
candidates based on an STS
or by monitoring a workload
• Uses DBMS_STATS procedure
SEED_COL_USAGE
• If the first two arguments are
set to NULL the current
workload will be monitored
• The third argument is the
time limit in seconds

reserved.
Extended Statistics
2. Run your workload
• Actual number of rows
returned by this query is 932
• Optimizer under-estimates
the cardinality as it assumes
each where clause predicate
will reduce number of rows
returned
• Optimizer is not aware of
real-world relations between
city, state, & country

reserved.
Extended Statistics
2. Run your workload
• Actual number of rows
returned by this query is 145
• Optimizer over-estimates the
cardinality as it is not aware
of the real-world relations
between state & country

reserved.
Extended Statistics
3. Check column usage information recorded for our table
COLUMN USAGE REPORT FOR SH.CUSTOMERS
1. COUNTRY_ID : EQ
2. CUST_CITY : EQ
3. CUST_STATE_PROVINCE : EQ
4. (CUST_CITY, CUST_STATE_PROVINCE, COUNTRY_ID) : FILTER
5. (CUST_STATE_PROVINCE, COUNTRY_ID) : GROUP_BY
SELECT dbms_stats.report_col_usage(user, 'customers') FROM dual;
EQ means column was used in
equality predicate in query 1
GROUP_BY columns used in group
by expression in query 2
FILTER means columns used together
as filter predicates rather than join
etc. Comes from query 1

reserved.
Statistics Enhancements
4. Create extended stats for customers based on usage
EXTENSIONS FOR SH.CUSTOMERS
1. (CUST_CITY, CUST_STATE_PROVINCE, COUNTRY_ID):
SYS_STUMZ$C3AIHLPBROI#SKA58H_N created
2. (CUST_STATE_PROVINCE, COUNTRY_ID) :
SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ created
Column group statistics will now be automatically maintained
every time you gather statistics on this table
SELECT dbms_stats.create_extended_stats(user, 'customers')
FROM dual;

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Agenda

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When to Gather Statistics
• Oracle automatically collect statistics for all database
objects, which are missing or have stale statistics
• AutoTask run during a predefined maintenance window
• Internally prioritizes the database objects
– Both user schema and dictionary tables
– Objects that need updated statistics most are processed first
• Controlled by DBMS_AUTO_TASK_ADMIN package or via
Enterprise Manager
Automatic Statistics Gathering

reserved.
Automatic Statistics Gathering in Enterprise Manager
• Enterprise Manager allows
you to control all aspects of
the automatic statistics
gathering task
• The statistics gathering task
can be set to only run during
certain maintenance windows

reserved.
• If you want to disable auto job for application schema
leaving it on for Oracle dictionary tables
• The scope of the auto job is controlled by the global
preference AUTOSTATS_TARGET
• Possible values are
– AUTO Oracle decides what tables need statistics (Default)
– All Statistics gathered for all tables in the system
– ORACLE Statistics gathered for only the dictionary tables
Automatic Statistics Gathering

reserved.
• Need to determine when to manually gather statistics
• After large data loads
– Add statistics gather to the ETL or ELT process
• If trickle loading or online transactions
– Manually determine when statistics are stale and trigger gather
– USER_TAB_MODIFICATIONS lists # INSERTS, UPDATES, and
DELETES that occurs on each table
• If trickle loading into a partition table
– Used dbms.stats.copy_table_stats()
If the Auto Statistics Gather Job is not suitable

reserved.
If the Auto Statistics Gather Job is not suitable
Partitioned Table
Partition 1
June 1st 2012
:
Partition 4
June 4th 2012
Partition 5
June 5th 2012
DBMS_STATS.COPY_TABLE_STATS();
• Copies statistic from source
partition to new partition
• Adjusts min & max values for
partition column
• Both partition & global statistics
• Copies statistics of the
dependent objects
• Columns, local (partitioned)
indexes* etc.
• Does not update global indexes

reserved.
Agenda

reserved.
Statistics Gathering Performance
• Three parallel options to speed up statistics gathering
– Inter object using parallel execution
– Intra object using concurrency
– The combination of Inter and Intra object
• Incremental statistics gathering for partitioned tables
How to speed up statistics gathering

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• Controlled by GATHER_*_STATS parameter DEGREE
• Default is to use parallel degree specified on object
• If set to AUTO Oracle decide parallel degree used
• Works on one object at a time
Inter Object using parallel execution

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P4
P3
P2
P1
Customers
Table
• Customers table has a
degree of parallelism of 4
• 4 parallel server processes
will be used to gather stats

reserved.
Exec DBMS_STATS.GATHER_TABLE_STATS(‘SH’,’SALES);
Sales Table
Partition 1
May 18th 2012
Partition 2
May 19th 2012
Partition 3
May 20th 2012
• Each individual partition will
have statistics gathered one
after the other
• The statistics gather
procedure on each individual
partition operates in parallel
BUT the statistics gathering
procedures won’t happen
concurrently
P4
P3
P2
P1

reserved.
• Gather statistics on multiple objects at the same time
• Controlled by DBMS_STATS preference, CONCURRENT
• Uses Database Scheduler and Advanced Queuing
• Number of concurrent gather operations controlled by
job_queue_processes parameter
• Each gather operation can still operate in parallel
Intra Object

reserved.
Intra Object Statistics Gathering for SH Schema
Exec DBMS_STATS.GATHER_SCHEMA_STATS(‘SH’);
• A statistics gathering job is
created for each table and
partition in the schema
• Level 1 contain statistics
gathering jobs for all non-
partitioned tables and a
coordinating job for each
partitioned table
• Level 2 contain statistics
gathering jobs for each
partition in the partitioned
tables

reserved.
Inter Object using concurrent parallel execution
Exec DBMS_STATS.GATHER_TABLE_STATS(‘SH’,’SALES);
Sales Table
Partition 1
May 18th 2012
Partition 2
May 19th 2012
Partition 3
May 20th 2012
Job1
Job2
Job3
• The number of concurrent
gathers is controlled by the
parameter
job_queue_processes
• In this example it is set to 3
• Remember each concurrent
gather operates in parallel
• In this example parallel
degree is 4

reserved.
• Typically gathering statistics after a bulk loading data
into one partition would causes a full scan of all
partitions to gather global table statistics
– Extremely time consuming
• With Incremental Statistic gather statistics for touched
partition(s) ONLY
– Table (global) statistics are accurately built from partition statistics
– Reduce statistics gathering time considerably
– Controlled by INCREMENTAL preference
Incremental Statistics Gathering for Partitioned tables

reserved.
Incremental Statistics Gathering
Sales Table
May 22nd 2011
May 23rd 2011
May 18th 2011
May 19th 2011
May 20th 2011
May 21st 2011
Sysaux Tablespace
1. Partition level stats are
gathered & synopsis created
2. Global stats generated by aggregating
partition level statistics and synopsis

reserved.
Incremental Statistics Gathering
Sales Table
May 22nd 2011
May 23rd 2011
May 18th 2011
May 19th 2011
May 20th 2011
May 21st 2011
Sysaux Tablespace
May 24th 2011
4. Gather partition
statistics for new partition
5. Retrieve synopsis for
each of the other partitions
from Sysaux
6. Global stats generated by aggregating
the original partition synopsis with the
new one
3. A new partition is added to the table & Data is Loaded

reserved.
More Information
• Accompanying Two Part White Paper Series
– Understanding Optimizer Statistics
– Best Practices for Managing Optimizer Statistics
• Optimizer Blog
– http://blogs.oracle.com/optimizer
• Oracle.com
– http://www.oracle.com/technetwork/database/focus-areas/bi-
datawarehousing/dbbi-tech-info-optmztn-092214.html
• Oracle Exadata Database Machine
– http://www.oracle.com/exadata

reserved.

Best Practices for Oracle Exadata and the Oracle Optimizer

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