Sort merge joins can be used to join rows from two independent sources. Hash joins generally perform better than sort merge joins. On the other hand, sort merge joins can perform better than hash joins if both of the following conditions exist:
However, if a sort merge join involves choosing a slower access method (an index scan as opposed to a full table scan), then the benefit of using a sort merge might be lost.
Sort merge joins are useful when the join condition between two tables is an inequality condition (but not a nonequality) like <, <=, >, or >=. Sort merge joins perform better than nested loop joins for large data sets. You cannot use hash joins unless there is an equality condition.
In a merge join, there is no concept of a driving table. The join consists of two steps:
If the input is already sorted by the join column, then a sort join operation is not performed for that row source. However, a sort merge join always creates a positionable sort buffer for the right side of the join so that it can seek back to the last match in the case where duplicate join key values come out of the left side of the join.
3.2 Sort Merge Join Hints
To instruct the optimizer to use a sort merge join, apply the
USE_MERGE hint. You might also need to give hints to force an access path.
There are situations where it is better to override the optimizer with the
USE_MERGE hint. For example, the optimizer can choose a full scan on a table and avoid a sort operation in a query. However, there is an increased cost because a large table is accessed through an index and single block reads, as opposed to faster access through a full table scan.
Note : if we consider on the broader look Nested Loop join is performed on small tables with index on driven (inner) column will add edge to it, on the other hand Hash Join is used on Large tables with no indexes and use pga for preparing hash table. Sort Merge join is used in case of medium sized tables.
Above note is referenced from Oracle Internals and manuals.
Example:
SQL> conn hr/*****
Connected.
SQL> create table e as select * from emp;
Table created.
SQL> create table d as select * from dept;
Table created.
SQL> create index e_deptno on e(deptno);
Index created.
Gather D stats as it is
SQL> exec dbms_stats.gather_table_stats('hr','D')
PL/SQL procedure successfully completed.
Set artificial stats for E:
SQL> exec dbms_stats.set_table_stats(ownname => 'hr', tabname => 'E', numrows => 100, numblks => 100, avgrlen => 124);
PL/SQL procedure successfully completed.
Set artificial stats for E_DEPTNO index
SQL> exec dbms_stats.set_index_stats(ownname => 'hr', indname => 'E_DEPTNO', numrows => 100, numlblks => 10);
PL/SQL procedure successfully completed.
Check out the plan:
A) With less number of rows(100 in E), you will see Nested loop getting used.
SQL> select e.ename,d.dname from e, d where e.deptno=d.deptno;
Execution Plan
----------------------------------------------------------
Plan hash value: 3204653704
----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 100 | 2200 | 6 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID| E | 25 | 225 | 1 (0)| 00:00:01 |
| 2 | NESTED LOOPS | | 100 | 2200 | 6 (0)| 00:00:01 |
| 3 | TABLE ACCESS FULL | D | 4 | 52 | 3 (0)| 00:00:01 |
|* 4 | INDEX RANGE SCAN | E_DEPTNO | 33 | | 0 (0)| 00:00:01 |
----------------------------------------------------------------------------------------
B) Let us set some more artificial stats to see which plans is getting used:
SQL> exec dbms_stats.set_table_stats(ownname => 'hr', tabname => 'E', numrows => 1000000, numblks => 10000, avgrlen => 124);
PL/SQL procedure successfully completed.
SQL> exec dbms_stats.set_index_stats(ownname => 'hr', indname => 'E_DEPTNO', numrows => 1000000, numlblks => 1000);
PL/SQL procedure successfully completed.
SQL> exec dbms_stats.set_table_stats(ownname => 'hr', tabname => 'D', numrows => 1000000,numblks => 10000 , avgrlen => 124);
PL/SQL procedure successfully completed.
Now we have 1000000 number of rows in E and D table both and index on E(DEPTNO) reflects the same.
Plans changes !!
SQL> select e.ename,d.dname from e, d where e.deptno=d.deptno;
Execution Plan
----------------------------------------------------------
Plan hash value: 51064926
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 250G| 5122G| | 3968K(100)| 13:13:45 |
|* 1 | HASH JOIN | | 250G| 5122G| 20M| 3968K(100)| 13:13:45 |
| 2 | TABLE ACCESS FULL| E | 1000K| 8789K| | 2246 (3)| 00:00:27 |
| 3 | TABLE ACCESS FULL| D | 1000K| 12M| | 2227 (2)| 00:00:27 |
-----------------------------------------------------------------------------------
C) Now to test MERGE JOIN, we set moderate number of rows and do some ordering business.
SQL> exec dbms_stats.set_table_stats(ownname => 'hr', tabname => 'E', numrows => 10000, numblks => 1000, avgrlen => 124);
PL/SQL procedure successfully completed.
SQL> exec dbms_stats.set_index_stats(ownname => 'hr', indname => 'E_DEPTNO', numrows => 10000, numlblks => 100);
PL/SQL procedure successfully completed.
SQL> exec dbms_stats.set_table_stats(ownname => 'hr', tabname => 'D', numrows => 1000, numblks => 100, avgrlen => 124);
PL/SQL procedure successfully completed.
SQL> select e.ename,d.dname from e, d where e.deptno=d.deptno order by e.deptno;
Execution Plan
----------------------------------------------------------
Plan hash value: 915894881
-----------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2500K| 52M| 167 (26)| 00:00:02 |
| 1 | MERGE JOIN | | 2500K| 52M| 167 (26)| 00:00:02 |
| 2 | TABLE ACCESS BY INDEX ROWID| E | 10000 | 90000 | 102 (1)| 00:00:02 |
| 3 | INDEX FULL SCAN | E_DEPTNO | 10000 | | 100 (0)| 00:00:02 |
|* 4 | SORT JOIN | | 1000 | 13000 | 25 (4)| 00:00:01 |
| 5 | TABLE ACCESS FULL | D | 1000 | 13000 | 24 (0)| 00:00:01 |
-----------------------------------------------------------------------------------------
