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Bushy Joins – a closer look

When 12.2 came out most of the (optimizer) focus was around SPD and how to avoid the challenges from 12.1. Still 12.2 introduced several (less acclaimed) optimizations including “Bushy Join” transformation, which is interesting since (I think, corrections welcome) Bushy Join concept isn’t necessarily tied to query transformation in general, especially before 12.2 (some reference about “manual” bushy joins here and here) or in other RDBMS (a manual example on SQL Server here).
Anyway being the CBO way of improving our code query transformations here we go again.

There isn’t much on the internet about Bushy Joins and 12.2 beside this article so I decided to take a closer look. All the tests are from a 12.2 vanilla installation with bushy joins enabled

SQL> @hparam bushy
NAME                              DESCRIPTION                         SESSION_VA
--------------------------------- ----------------------------------- ----------
_optimizer_bushy_cost_factor      cost factor for bushy join          100       
_optimizer_bushy_fact_dim_ratio   bushy join dimension to fact ratio  20        
_optimizer_bushy_fact_min_size    minimumm fact size for bushy join   100000    
_optimizer_bushy_join             enables bushy join                  ON        

and the DDL to create the objects are the following

create table f1 as select a.* from dba_objects a, (select 1 from dual connect by rownum <= 2);
create table d1 as select object_id, object_type from dba_objects;
create table f2 as select a.* from dba_objects a, (select 1 from dual connect by rownum <= 2);
create table d2 as select object_id, object_type from dba_objects;

exec dbms_stats.gather_table_stats(user,'F1');
exec dbms_stats.gather_table_stats(user,'D1');
exec dbms_stats.gather_table_stats(user,'D2');
exec dbms_stats.gather_table_stats(user,'F2');

create index f1_idx1 on f1(object_id);
create index f2_idx1 on f2(object_id);
create index f1_idx2 on f1(object_type);
create index f2_idx2 on f2(object_type);

select table_name, num_rows from user_tables where table_name like 'F_' or table_name like 'D_';

------------- ----------
F1                147200
D1                 73601
F2                147204
D2                 73603

The DUAL to duplicate the number of rows in DBA_OBJECTS is just to have more than 100k rows in the two fact tables F1 and F2 (also indexes *IDX1 are never used in my examples but I created them so in the spirit of full disclosure I included them).

select f1.*, f2.* 
  from f1, f2, d1, d2 
 where f1.object_type = f2.object_type 
   and d1.object_type = f1.object_type 
   and f2.object_type = d2.object_type 
   and d1.object_id = 123 
   and d2.object_id = 456;

| Id |Operation                      |Name             | Rows | Cost|
|   0|SELECT STATEMENT               |                 |  208K|  414|
|*  1| HASH JOIN                     |                 |  208K|  414|
|   2|  NESTED LOOPS                 |                 | 3132 |  207|
|   3|   NESTED LOOPS                |                 | 3132 |  207|
|*  4|    TABLE ACCESS FULL          |D1               |    1 |   58|
|*  5|    INDEX RANGE SCAN           |F1_IDX2          | 3132 |    9|
|   6|   TABLE ACCESS BY INDEX ROWID |F1               | 3132 |  148|
|   7|  VIEW                         |VW_BUSHY_A9E4AA31| 3132 |  207|
|   8|   NESTED LOOPS                |                 | 3132 |  207|
|   9|    NESTED LOOPS               |                 | 3132 |  207|
|* 10|     TABLE ACCESS FULL         |D2               |    1 |   58|
|* 11|     INDEX RANGE SCAN          |F2_IDX2          | 3132 |    9|
|  12|    TABLE ACCESS BY INDEX ROWID|F2               | 3132 |  148|

Predicate Information (identified by operation id):
   1 - access("F1"."OBJECT_TYPE"="ITEM_1")
   4 - filter("D1"."OBJECT_ID"=123)
   5 - access("D1"."OBJECT_TYPE"="F1"."OBJECT_TYPE")
  10 - filter("D2"."OBJECT_ID"=456)
  11 - access("F2"."OBJECT_TYPE"="D2"."OBJECT_TYPE")

From the execution plan D1 and F1 are joined together first and the result is then joined with [the result of the join between D2 and F2].
In this case this is a good idea since the two large fact tables are joined together without any dimension in between, thus the filtering introduced by the two dimensions cannot be applied before a large (and filtered) fact table is joined with another large (and not filtered) fact table. Btw what I just said is a bit incorrect since a merge join cartesian between the two dimensions wouldn’t be a too bad idea in this case (it’s actually what the CBO does once Bushy Joins are disabled).

Let’s take a look under the hood, aka 10053 trace </p />

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