Oracle Query Optimization

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Performance tuning focuses on writing efficient SQL, allocating computer resources and analyzing wait events and contention in the system. The design approach to a database is critical to ensuring the best performance from a database, here are the steps when designing a database:

Design the application correctly
Tune the application SQL code
Tune memory
Tune I/O
Tune contention and other issues

There are two approaches to performance tuning

pro-active - try to see the problem before it happens i.e. warning alert on a tablespace threshold before it becomes a problem
reactive - the problem has already occurred and action is required

pro-active means that it does not impact the end user where as reactive is generally when the user tells you he/she has a problem. OEM provides many graphs that can shown you trends about a particular area of the database for more information see OEM.

One rule is first try and fix the SQL code, especially if it is old code, no matter how you tune the database if the code is badly written it won't make any difference, however oracle does provide some parameters that can help with poorly written code.

Oracle Query process

Query processing requires the transformation of your SQL query into an efficient execution plan, Query optimization requires that the best execution plan is the one executed, the goal is to use the least amount of resources possible (CPU and I/O), the more resources a query uses the more impact it has on the general performance of the database.

A users query will go through 3 phases

Parsing

At this stage the syntax ( decomposed into a relational algebra that's analyzed to see if its syntactically correct) and semantics (make sure that tables and columns exist and you have permissions to access the objects) are checked, at the end you have a parse tree which represents the query's structure.

The statement is normalized so that it can be processed more efficiently, once all the checks have completed it is considered a valid parse tree and is sent to the logical query plan generation stage.

All this is done in the library cache of the SGA

Optimization

The optimizer is used at this stage, which is a cost-based optimizer (CBO - see below for more info), this chooses the best access method to retrieve the requested data. It uses statistics and any hints specified in the SQL query, the CBO produces an optimal execution plan for the SQL statement.

The optimization process can be divided in two parts

Query rewrite phase

The parse tree is converted into an abstract logical query plan, the various nodes and branches are replaced by operators of relational algebra.

Execution plan generation phase

Oracle transforms the logical query plan into a physical query plan, the physical query or execution plan takes into account the following factors

The various operations (joins) to be performed during the query
The order in which the operations are performed
The algorithm to be used for performing each operation
The best way to retrieve data from disk or memory
The best way to pass data from operation to another during the query

The optimizer may well come up with multiple physical plans, all of which are potential execution plans. The optimizer then chooses among them by estimating the costs of each possible plan (based on table and index statistics) and selecting the plan with the lowest cost. This is called the cost-based query optimization (CBO).

Execution

The final stage is to execute the physical query plan that was selected by the CBO

Optimization Mode

In previous versions of Oracle you had two choices RBO (rule-based optimizer) or CBO (cost-based optimizer), RBO is available in Oracle 10g but is a deprecated product the CBO is the preferred and default method.

RBO
Rule-Base Optimizer

used a heuristic method to select among several alternative access paths with the help of certain rules. All paths were ranked and the lowest was chosen i.e. using the ROWID was a cost of 1, a full table scan was a cost of 19.

CBO
Cost-based Optimizer

The CBO uses statistics on tables and indexes, the order of tables and columns in the SQL statements, available indexes, and any user-supplied hints to pick the most efficient way to access the data requested.

CBO almost always performs better than RBO.

Cost-Based Optimizer (CBO)

The optimizers job is to find out the optimal or best plan to execute your DML statements (select, insert, update and delete). The CBO uses statistics on tables and indexes, the order of tables and columns in the SQL statements, available indexes, and any user-supplied hints to pick the most efficient way to access the data requested, the CBO uses the least costly method (cost being CPU and I/O which is the most expensive) to get at the data.

Oracle 10g has a automatic job that collects statistics which is used the the CBO, the gather_stats_job is run between 10pm-6am everyday. The job collects statistics on all tables that either have no statistics or stale statistics (more than 10% of data has changed in the table since the last collection).

Check if statistics are being collected

select last_analyzed, table_name, owner, num_rows, sample_size from dba_tables order by last_analyzed;

Note: sample_size can vary from 1 to 100%, the greater the sample_size the better statistics are obtained, however a general 5 to 20% should be enough in most cases especially in very large tables

Once the statistics have been gathered a number of columns will have been updated in a table or index

Number of rows and number of rows per block
Average row length
Total number of database blocks in a table
Number of levels in each index
Number of leaf blocks in each index
Number of distinct values in each column of a table
Data distribution histograms
Number if distinct index keys
Cardinality (the number of columns with similar values for each column)
Minimum and Maximum values for each column
System statistics, which include I/O characteristics of your system and CPU statistics which include CPU speed and other related statistics.

The CBO will use all of the above statistics and other statistics (CPU, I/O and O/S statistics) to help with finding the optimal plan, using the above statistics the CBO can estimate costs of individual operations. The less number of statistics collected will result in less physical plans the CBO can come up with, thus the less number of choices the CBO can make.

Column statistics

select column_name, num_distinct fro dba_col_statistics where table_name = 'PERSONNEL';

Optimizer Configuration

The mode level can be set to a number of levels, depending on what your application requirements are you can start sending data to the user quickly, if the user requires all the data to be seen altogether then the ALL_ROWS option is best but if the user does not care about getting all the data only getting something fast then FIRST_ROWS may be an option, also by using hints you can force the optimizer to take a chosen path see hints below for more details

Mode Levels
ALL_ROWS (default)	The optimizer will process all rows before outputting data
FIRST_ROWS_n	The optimizer will process n rows before outputting data
FIRST_ROWS	Once data is available start outputting immediately.
Setting Level
Mode Level (system wide)	alter system set optimizer_mode = ALL_ROWS;
Mode Level (session level)	alter session set optimizer_goal = first_rows_10;

CBO Drawbacks

The CBO may not use the same plan for the same SQL statement every time, you need to watch out for the following

Execution plans can change across oracle versions, to make sure that the same plan is used then you need to use stored outlines
The application developer may know of a better plan than the CBO, in which case hints can be used to direct or help the CBO.
The CBO depends enormously on current statistics, if the statistics are absent or outdated the optimizer can make poor decisions

Manually Collect Statistics

To collect statistics manually you need to use the package dbms_stats, you can sample row or block and depending on the size of the table

Database	dbms_stats.gather_database_stats ( estimate_percent => NULL, method_opt => 'auto', granularity => 'all, cascade => 'true', option => 'gather_auto' ); Note: estimate - refers to the percentage of rows that should be used to estimate the statistics, null means all rows method_opt - can specify several things , like include histograms, AUTO means collect histograms granularity - only applies to tables, ALL means collect statistics for subpartitions, partitions, etc cascade - collect all statistics for both table and index option - gather = collect statistics for all objects even it the object has stale or fresh statistics gather_auto = Oracle will decide on what to collect gather_empty = collect only for objects that do not have any statistics gather_stale = collect only for objects with stale statistics
Schema	dbms_stats.gather_schema_stats( ownname => 'VALLEP');
Table	dbms_stats.gather_table_stats( 'hr', 'employees');
Index	dbms_stats.gather_index_stats( 'hr', 'employ_idx');
System	dbms_stats.create_stat_table(ownname => 'vallep', stat_tab => 'stats_table', tblspace => 'stat_tbs'); dbms_stats.gather_system.stats('start'); (wait a while then run stop) dbms_stats.gather_system.stats('stop'); dbms_stats.gather_system_stats(interval => 720, stat_tab => 'stats_table', stat_id => 'OLTP'); Note: you can use the create_stat_table to transfer system stats to other databases.

You can also collect statistics by using the analyze command

Analyze a table	analyze table employee compute statistics; Note: additional columns are filled in when this is run i.e. average_row_size, last_analyzed, size_of_table (in data blocks)
Useful Views
DBA_TABLES	look at the last_analyzed column to make sure statistics are being collected
DBA_OBJECT_TABLES	look at the last_analyzed column to make sure statistics are being collected

Dynamic Sampling

Dynamic sampling is controlled by the optimizer_dynamic_sampling parameter which accepts values from 0 (off) to 10 (aggressive sampling) with 2 as the default.

Dynamic sampling can be a benefit

The sample time is small compared to the overall query execution time
Results in better performing query
Query may be executed multiple times

Configuration
Display	show parameter optimizer_dynamic_sampling;
Instance	alter system set optimizer_dynamic_sampling = 2;
Session	alter session set optimizer_dynamic_sampling = 5;
Usage
SQL Hint	select /+ dynamic_sampling(employees 5) / emp_id, fname, lname, job, sal from employees where dept_num = 50;

Efficient SQL

To elimate the number of rows that the optimizer has to retrieve we use where clauses, however the optimizer may not end up writing the best execution plan, you have better knowledge of the application than the optimizer and with this knowledge you can use hints which force the optimizer to use that knowledge. Using where clauses efficiently will reduce the I/O, thus increasing performance, the optimizer will use statistics (row count) to determine how to create the best plan, if no statistics are available then it has no option but to perform a full table scan.

Sometimes the optimizer will not use a index, even if you know one exists, the possible reason for these could be any of the following

Views in a query sometimes prevent the use of indexes
If you think heavy data skew is in the table, using histograms will help the optimizer to provide more accurate representations of the data distribution in the table.
If for some reason it still refuses to use the index you can force it by using a hint.

Try using the where clause instead of the having clause, as the having clause incurs the additional overhead of sorting and summing.

Hints

Sometimes the CBO does not know best and needs help to point it in the right direction, this is where hints help by forcing the optimizer to take a path that you have knowledge about, hints can alter the join methods, join order or even access paths. The are many hint options you would need to see the Oracle documentation for a full listing but i have listed some of the more common one

all_rows - optimize throughput, not optimizer the response time of the statement
first_rows - return the first rows quickly
full - perform a full table scan
ordered - force the join order of the tables in the query
index - force the use of indexes
index_ffs - force a full scan of a index, can use parallelization for this to improve performance.

Examples

select /*+ FULL (employees) */ ... from employees
select /*+ INDEX_FFS (employees) */ ... from employees
select /*+ USE_NL (employees, dept) */ .. from employees, dept

Note: see oracle documentation for full listing of all the hints

Join Methods

You should choose a join method based on how many rows you expect to be returned from the join

Cartesian Joins	cartesian joins are normally a result of not using a where clause, it basically joins every row in all tables, so for an example if one table as 50,000 rows and the other table has 100 rows then a cartesian join of the tables would be 50,000 * 100 = 500,000 rows
Nested Loops	If you are join tables with few than 10,000 rows then a Nested Loop would be the way to go, see hints section above for more details on how to use a Nested Loop
Hash Join	Use if the join will produce large subsets of data or a substantial proportion of a table is going to be joined.
Merge Join	If the tables in the join are being joined with an inequality condition (not an equi join), then use a merge join
Bitmap Join	Basically used for data warehouses, do not use if running OLTP system, they are used with low cardinality columns (columns having low distinct values - gender, marital _status, relation, etc)

See join methods for more information regarding different types of joins

Index Strategy

An index is a data structure that takes the value of one or more columns of a table (the key) and returns all rows (or the requested columns in that row) with that value of the column quickly. The efficiency of the index is that it lets you find the necessary rows without having to perform a full table scan, this leads to few I/O's.

As a general rule you so only use a index if you select about 10-15% of a table, when using a index this prevents you from performing a full table scan. When you want to retrieve a row, oracle has to perform a lookup in the index to obtain the ROWID, using this ROWID it can then retrieve the requested row. Using to many indexes can degrade performance as when a table is updated the index has to be updated as well, so only index when the trade-off is better, in other words if you use a OLTP database then limit the amount of indexes that you use. Here are some guidelines on when you should index

Index columns with high selectivity (few rows with identical values)
Index all important foreign keys
Index all predicate columns
Index columns used in table joins
Try to avoid indexing columns that consist of long character strings
Try to use index-only query plans, in other words try to retrieve the query using the index only.
Use secondary indexes on columns frequently involved in order by and group by as well as sorting operations union and distinct

B-Tree index	this is the default index type, you will probably use it for almost all the indexes in a typical OLTP application.
Bitmap Index	This is ideal for column data that has a low cardinality (few distinct values). Do not use if you have heavy DML going on the table, this is ideal in a data warehouse environment.
IOT (index organized table)	IOT's place all the table data in its primary key index, thus eliminating the need for a separate index. The data is sorted and rows are stored in primary key order. This type of index will save storage space when compared to a normal B-Tree index.
Concatenated Index	Concatenated or Composite indexes are indexes that include more than one column and are excellent for improving selectivity of the where predicates. You are trying to elimate the optimizer performing full table scans thus reducing I/O.
Funcation-Based Index	are efficient in frequently used statements that involve functions or complex expression on columns, they can quickly return the computed value of the function or expression directly from the index.
Reverse-Key Index	These are ideal when you have a heavy insert application, the reverse key index provide an efficient way to distribute the index values more evenly and thus improve performance.
Partioned Index	each partition can be distributed across disks thus increasing performance, also each partition can be maintained separately (backup, remove) without affecting any other partition You can also use parallel query options to improve performance.

It is worth rebuilding indexes regularly so queries can run faster, see indexes for more information.

For more information on indexes click here

Bind Variables

In a ideal world you should parse just once and use the same parsed version of the statement for repeated executions, this operation is much more expensive than actually executing the statement. In order you use the same execution plan for the same query the SQL statement must be identical, you use bind variables to archive this.

The way oracle knows if the query is identical is that the statement is hashed and a hash key stored, if this key does not match then the statement is not the same, so even you add a space or replace a letter with a upper case letter the hash key will be different.

There is a parameter you can set to force statements that fail to use bind variables to do so

Force bind variable usage

alter system set cursor_sharing = force;
alter system set cursor_sharing = similar;
alter system set cursor_sharing = extact;

Note:
force - forces the use of bind variables, oracle will substitute the hard coded variable
similar - force the use of a bind variable only if oracle thinks it will not adversely affects optimization.
extact - only use the already parsed query if it is identical (hash key matches)

Materialized Views

If you are dealing with very large amounts of data, you should consider using materialized views to improve response time. Materialized views are objects with summary data from the underlying table. Expensive table joins can be done beforehand and saved in the materialized view. You can use the package dbms_olap package to get recommendations on ideal materialized views.

For more information about materialized views click here.

Stored Outlines

The CBO doesn't always use the same execution strategy, changes within the database can force the CBO to change its plan. You can force oracle to use the same plan by using the plan stability feature stored outlines to preserve the current execution plans, even if the statistics and optimizer mode changes. The only catch is that the SQL statement must be identical if you wish to use the stored outline.

Stored outlines are use when you plan to migrate from one oracle version to another, thus you can cut risks and preserve the applications present performance via stored outlines. Outlines ensure that the execution paths the queries used in a test instance successfully carry over to the production instance. Also it can be used to override the code that is imbedded in the application.

The more common name for this feature is called optimizer stability. All the information on stored outlines is stored in the OUTLN schema in two tables OL$ and OL$HINTS (these are created with you install Oracle).

System parameters	alter system set query_rewrite_enabled = true; alter system set star_transformation_enabled = true; alter system set optimizer_features_enabled = 10.2.0; Note: the above values must be the same on all instances, when using this feature across different databases.
Stored Outlines (database)	alter system set create_stored_outlines = true; Note: this can use lots of disk space
Stored Outlines (session)	alter session set create_stored_outlines = true;
Create Outline	create outline test_outline on select employee_id, last_name from hr.employees;
Use Outline	alter system set use_stored_outline = true; alter session set use_stored_outline = true;
Edit Outline	Use the dbms_outln_edit package Options ------------------------------------------------------------------------------------ change_join_pos - changes the join position for the hint identified by outline name create_edit_tables - creates outline editing tables in calling a users schema dropedit_tables - drops outline editong tables generate_signature - generates a signature for the specified SQL text referesh_private_outline - refreshes the in-memory copy of the outline
Remove Outline	drop outline test_outline;
Useful Views
DBA_OUTLINES	A view of the below tables (name, owner, category, used, sql_text)
OL$	Contains the outlines (name, sql_text, signature, category, flags, etc) Note: flags is useful as it can tell you if the outline is being used.
OL$HINTS	Contains the outlines hints (name, hint_type, hint_text, table_name, etc)
OL$NODES	Contains the outlines nodes
Packages
DBMS_OUTLN	Manage stored outlines and their outline categories
DBMS_OUTLN_EDIT	Manage stored outlines and their outline categories

Histograms

The CBO normally assumes that the data is uniformly distributed in the table, however there are times when the data is extremely skewed which means you are better off using histograms to store column statistics, histograms provide more efficient access methods. Histograms use buckets to represent distribution of data in a column and Oracle uses these buckets to how skewed the data distribution is.

You can use the following histograms

height-based - divide column values into bands, with each band containing a roughly equal number of rows
frequency-based - determine the number of buckets based on the distinct values in the column, each bucket contains all the data that has the same value

height-based	begin dbms_stats.gather_table_stats( ownname => 'HR', tabname => 'benefits', method_opt => 'for column size 10 number_of_vists' ); end; / begin dbms_stats.gather_table_stats( ownname => 'HR', tabname => 'benefits', method_opt => 'for column size skewonly' ); end; / begin dbms_stats.gather_table_stats( ownname => 'HR', tabname => 'BENEFITS', method_opt => 'for all columns size auto' ); end; / Note: for column - your own histogram creation auto - let oracle decide what to do based on data distribution and workload skewonly - based the decision only on the data distribution of the columns
frequency-based	begin dbms_stats.gather_table_stats( ownname => 'HR', tabname => 'benefits', method_opt => 'for column size 20 department_id' ); end; /
Useful Views
DBA_TAB_HISTOGRAMS	describes histograms on columns of all tables in the database
DBA_HISTOGRAMS	is a synonym for DBA_TAB_HISTOGRAMS
DBA_PART_HISTOGRAMS	provides the histogram data (end-points per histogram) for histograms on all table partitions in the database
DBA_SUBPART_HISTOGRAMS	lists actual histogram data (end-points per histogram) for histograms on all table subpartitions in the database

PL/SQL Performance

When you are developing a database there are four system parameters that can help the developer

PLSQL_WARNING (disabled by default)	Used to indentify errors and poor performance alter system set plsql_warnings = 'ENABLE:ALL' scope = both; alter system set plsql_warnings = 'ENABLE:PERFORMANCE','ENABLE:SERVE' scope=both;
PLSQL_DEBUG (false by default)	Additional debuging information, compiled code will be stored as interpreted regardless of PLSQL_CODE_TYPE alter system set plsql_debug = false scope = both;
PLSQL_OPTIMIZE_MODE (2 by default)	Optimize when compiled alter system set plsql_optimize_mode = 2 scope=both;
PLSQL_CODE_TYPE	Compiled code into interpreted byte code (default) or native machine code alter system plsql_code_type = native scope=both; Note: When PL/SQL objects are compiled the resulting code is stored depends on the PLSQL_CODE_TYPE, Native compiled code will be stored in an O/S system file, while interpreted code is stored in the data dictionary