SQL Query Optimization
******** Nested loop information *******************
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:
There are two approaches to performance tuning
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
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 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|
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.
|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.|
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
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';|
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
|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.|
|Mode Level (system wide)||alter system set optimizer_mode = ALL_ROWS;|
|Mode Level (session level)||alter session set optimizer_goal = first_rows_10;|
The CBO may not use the same plan for the same SQL statement every time, you need to watch out for the following
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
|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');|
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(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)
|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 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
|Display||show parameter optimizer_dynamic_sampling;|
|Instance||alter system set optimizer_dynamic_sampling = 2;|
|Session||alter session set optimizer_dynamic_sampling = 5;|
|SQL Hint||select /*+ dynamic_sampling(employees 5) */
emp_id, fname, lname, job, sal
where dept_num = 50;
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
Try using the where clause instead of the having clause, as the having clause incurs the additional overhead of sorting and summing.
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
select /*+ FULL (employees) */ ... from employees
Note: see oracle documentation for full listing of all the hints
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|
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
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
|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.|
(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
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;
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.
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$HINTS (these are created with you install Oracle).
alter system set query_rewrite_enabled = true;
Note: the above values must be the same on all instances, when using this feature across different databases.
|Stored Outlines (database)||
alter system set create_stored_outlines = true;
Note: this can use lots of disk space
|Stored Outlines (session)||
alter session set create_stored_outlines = true;
|Create Outline||create outline test_outline
on select employee_id, last_name
|Use Outline||alter system set use_stored_outline = true;
alter session set use_stored_outline = true;
Use the dbms_outln_edit package
|Remove Outline||drop outline test_outline;|
|DBA_OUTLINES||A view of the below tables (name, owner, category, used, sql_text)|
Contains the outlines (name, sql_text, signature, category, flags, etc)
Note: flags is useful as it can tell you if the outline is being used.
|OL$HINTS||Contains the outlines hints (name, hint_type, hint_text, table_name, etc)|
|OL$NODES||Contains the outlines nodes|
|Manage stored outlines and their outline categories|
|Manage stored outlines and their outline categories|
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
ownname => 'HR',
tabname => 'benefits',
method_opt => 'for column size 20 department_id'
|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|
When you are developing a database there are four system parameters that can help the developer
(disabled by default)
Used to indentify errors and poor performance
alter system set plsql_warnings = 'ENABLE:ALL' scope = both;
(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;
(2 by default)
|Optimize when compiled
alter system set plsql_optimize_mode = 2 scope=both;
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