Monthly Archives: May 2019

Workarounds for JPPD with view and table(kokbf$), xmltable or json_table functions

Posted on by Posted in CBO, oracle, query optimizing, SQL, troubleshooting 2,186 Page views Leave a comment

You may know that table() (kokbf$ collection functions), xmltable and json_table functions block Join-Predicate PushDown(JPPD).

Simple example:

DDL

[sourcecode language=”sql”]
create table xtest(a, b, c) as
select mod(level,1000),level,rpad(‘x’,100,’x’)
from dual
connect by level<=1e4
/
create index itest on xtest(a)
/
create or replace view vtest as
select a,count(b) cnt
from xtest
group by a
/
call dbms_stats.gather_table_stats(user,’xtest’);
/
[/sourcecode]

[collapse]

select distinct v.* 
from table(sys.odcinumberlist(1,2,3)) c, vtest v
where v.a = c.column_value;

Plan hash value: 699667151

-------------------------------------------------------------------------------------------------
| Id  | Operation                               | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                        |       |     1 |    19 |    80   (4)| 00:00:01 |
|   1 |  HASH UNIQUE                            |       |     1 |    19 |    80   (4)| 00:00:01 |
|*  2 |   HASH JOIN                             |       |     1 |    19 |    79   (3)| 00:00:01 |
|   3 |    COLLECTION ITERATOR CONSTRUCTOR FETCH|       |     1 |     2 |    29   (0)| 00:00:01 |
|   4 |    VIEW                                 | VTEST |  1000 | 17000 |    49   (3)| 00:00:01 |
|   5 |     HASH GROUP BY                       |       |  1000 |  8000 |    49   (3)| 00:00:01 |
|   6 |      TABLE ACCESS FULL                  | XTEST | 10000 | 80000 |    48   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - access("V"."A"=VALUE(KOKBF$))

same for json_table

[sourcecode language=”sql”]
select/*+ cardinality(c 1) use_nl(v) push_pred(v) */ *
from json_table(‘{"a":[1,2,3]}’, ‘$.a[*]’ COLUMNS (a int PATH ‘$’)) c
,vtest v
where c.a = v.a;

Plan hash value: 664523328

——————————————————————————–
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
——————————————————————————–
| 0 | SELECT STATEMENT | | 1 | 28 | 78 (2)| 00:00:01 |
| 1 | NESTED LOOPS | | 1 | 28 | 78 (2)| 00:00:01 |
| 2 | JSONTABLE EVALUATION | | | | | |
|* 3 | VIEW | VTEST | 1 | 26 | 49 (3)| 00:00:01 |
| 4 | SORT GROUP BY | | 1000 | 8000 | 49 (3)| 00:00:01 |
| 5 | TABLE ACCESS FULL | XTEST | 10000 | 80000 | 48 (0)| 00:00:01 |
——————————————————————————–

Predicate Information (identified by operation id):
—————————————————

3 – filter("V"."A"="P"."A")

Hint Report (identified by operation id / Query Block Name / Object Alias):
Total hints for statement: 1 (U – Unused (1))
—————————————————————————

1 – SEL$F534CA49 / V@SEL$1
U – push_pred(v)
[/sourcecode]

[collapse]
same for xmltable

[sourcecode language=”sql”]
select/*+ leading(c v) cardinality(c 1) use_nl(v) push_pred(v) */ v.*
from xmltable(‘(1,3)’ columns a int path ‘.’) c,vtest v
where c.a = v.a(+);

Plan hash value: 564839666

————————————————————————————————————
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
————————————————————————————————————
| 0 | SELECT STATEMENT | | 1 | 28 | 78 (2)| 00:00:01 |
| 1 | NESTED LOOPS OUTER | | 1 | 28 | 78 (2)| 00:00:01 |
| 2 | COLLECTION ITERATOR PICKLER FETCH| XQSEQUENCEFROMXMLTYPE | 1 | 2 | 29 (0)| 00:00:01 |
|* 3 | VIEW | VTEST | 1 | 26 | 49 (3)| 00:00:01 |
| 4 | SORT GROUP BY | | 1000 | 8000 | 49 (3)| 00:00:01 |
| 5 | TABLE ACCESS FULL | XTEST | 10000 | 80000 | 48 (0)| 00:00:01 |
————————————————————————————————————

Predicate Information (identified by operation id):
—————————————————

3 – filter("V"."A"(+)=CAST(TO_NUMBER(SYS_XQ_UPKXML2SQL(SYS_XQEXVAL(VALUE(KOKBF$),0,0,54525952,0),
50,1,2)) AS int ))

Hint Report (identified by operation id / Query Block Name / Object Alias):
Total hints for statement: 1 (U – Unused (1))
—————————————————————————

1 – SEL$6722A2F6 / V@SEL$1
U – push_pred(v)
[/sourcecode]

[collapse]

And compare with this:

create global temporary table temp_collection(a number);

insert into temp_collection select * from table(sys.odcinumberlist(1,2,3));

select/*+ cardinality(c 1) no_merge(v) */
   distinct v.* 
from temp_collection c, vtest v
where v.a = c.a;

Plan hash value: 3561835411

------------------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name            | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |                 |     1 |    26 |    41   (3)| 00:00:01 |
|   1 |  HASH UNIQUE                             |                 |     1 |    26 |    41   (3)| 00:00:01 |
|   2 |   NESTED LOOPS                           |                 |     1 |    26 |    40   (0)| 00:00:01 |
|   3 |    TABLE ACCESS FULL                     | TEMP_COLLECTION |     1 |    13 |    29   (0)| 00:00:01 |
|   4 |    VIEW PUSHED PREDICATE                 | VTEST           |     1 |    13 |    11   (0)| 00:00:01 |
|*  5 |     FILTER                               |                 |       |       |            |          |
|   6 |      SORT AGGREGATE                      |                 |     1 |     8 |            |          |
|   7 |       TABLE ACCESS BY INDEX ROWID BATCHED| XTEST           |    10 |    80 |    11   (0)| 00:00:01 |
|*  8 |        INDEX RANGE SCAN                  | ITEST           |    10 |       |     1   (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   5 - filter(COUNT(*)>0)
   8 - access("A"="C"."A")

You can see that JPPD works fine in case of global temporary tables and, obviously, the first workaround is to avoid such functions with complex views.
But in such simple queries you have 2 other simple options:
1. you can avoid JPPD and get optimal plans using CVM(complex view merge) by just simply rewriting the query using IN or EXISTS:

select * 
from vtest v
where v.a in (select/*+ cardinality(c 1) */ c.* from table(sys.odcinumberlist(1,2,3)) c);

Plan hash value: 1474391442

---------------------------------------------------------------------------------------------------
| Id  | Operation                                 | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                          |       |    10 |   100 |    42   (5)| 00:00:01 |
|   1 |  SORT GROUP BY NOSORT                     |       |    10 |   100 |    42   (5)| 00:00:01 |
|   2 |   NESTED LOOPS                            |       |    10 |   100 |    41   (3)| 00:00:01 |
|   3 |    NESTED LOOPS                           |       |    10 |   100 |    41   (3)| 00:00:01 |
|   4 |     SORT UNIQUE                           |       |     1 |     2 |    29   (0)| 00:00:01 |
|   5 |      COLLECTION ITERATOR CONSTRUCTOR FETCH|       |     1 |     2 |    29   (0)| 00:00:01 |
|*  6 |     INDEX RANGE SCAN                      | ITEST |    10 |       |     1   (0)| 00:00:01 |
|   7 |    TABLE ACCESS BY INDEX ROWID            | XTEST |    10 |    80 |    11   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   6 - access("A"=VALUE(KOKBF$))
the same with json_table and xmltable

[sourcecode language=”sql”]
select *
from vtest t
where t.a in (select/*+ cardinality(v 1) */ v.a from json_table(‘{"a":[1,2,3]}’, ‘$.a[*]’ COLUMNS (a int PATH ‘$’)) v);

Plan hash value: 2910004067

—————————————————————————————
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
—————————————————————————————
| 0 | SELECT STATEMENT | | 10 | 100 | 42 (5)| 00:00:01 |
| 1 | SORT GROUP BY NOSORT | | 10 | 100 | 42 (5)| 00:00:01 |
| 2 | NESTED LOOPS | | 10 | 100 | 41 (3)| 00:00:01 |
| 3 | NESTED LOOPS | | 10 | 100 | 41 (3)| 00:00:01 |
| 4 | SORT UNIQUE | | | | | |
| 5 | JSONTABLE EVALUATION | | | | | |
|* 6 | INDEX RANGE SCAN | ITEST | 10 | | 1 (0)| 00:00:01 |
| 7 | TABLE ACCESS BY INDEX ROWID| XTEST | 10 | 80 | 11 (0)| 00:00:01 |
—————————————————————————————

Predicate Information (identified by operation id):
—————————————————

6 – access("A"="P"."A")

select v.*
from vtest v
where exists(select/*+ cardinality(c 1) */ 1 from xmltable(‘(1,3)’ columns a int path ‘.’) c where c.a = v.a);

Plan hash value: 1646016183

—————————————————————————————————————
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
—————————————————————————————————————
| 0 | SELECT STATEMENT | | 10 | 100 | 42 (5)| 00:00:01 |
| 1 | SORT GROUP BY NOSORT | | 10 | 100 | 42 (5)| 00:00:01 |
| 2 | NESTED LOOPS | | 10 | 100 | 41 (3)| 00:00:01 |
| 3 | NESTED LOOPS | | 10 | 100 | 41 (3)| 00:00:01 |
| 4 | SORT UNIQUE | | 1 | 2 | 29 (0)| 00:00:01 |
| 5 | COLLECTION ITERATOR PICKLER FETCH| XQSEQUENCEFROMXMLTYPE | 1 | 2 | 29 (0)| 00:00:01 |
|* 6 | INDEX RANGE SCAN | ITEST | 10 | | 1 (0)| 00:00:01 |
| 7 | TABLE ACCESS BY INDEX ROWID | XTEST | 10 | 80 | 11 (0)| 00:00:01 |
—————————————————————————————————————

Predicate Information (identified by operation id):
—————————————————

6 – access("A"=CAST(TO_NUMBER(SYS_XQ_UPKXML2SQL(SYS_XQEXVAL(VALUE(KOKBF$),0,0,54525952,0),50,1,2)) AS int ))
[/sourcecode]

[collapse]

2. Avoid JPPD using lateral():

select/*+ cardinality(c 1) no_merge(lat) */
   distinct lat.* 
from table(sys.odcinumberlist(1,2,3)) c, 
     lateral(select * from vtest v where v.a = c.column_value) lat;

Plan hash value: 18036714

-----------------------------------------------------------------------------------------------------------
| Id  | Operation                               | Name            | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                        |                 |    10 |   190 |    41   (3)| 00:00:01 |
|   1 |  HASH UNIQUE                            |                 |    10 |   190 |    41   (3)| 00:00:01 |
|   2 |   NESTED LOOPS                          |                 |    10 |   190 |    40   (0)| 00:00:01 |
|   3 |    COLLECTION ITERATOR CONSTRUCTOR FETCH|                 |     1 |     2 |    29   (0)| 00:00:01 |
|   4 |    VIEW                                 | VW_LAT_4DB60E85 |    10 |   170 |    11   (0)| 00:00:01 |
|   5 |     SORT GROUP BY                       |                 |    10 |    80 |    11   (0)| 00:00:01 |
|   6 |      TABLE ACCESS BY INDEX ROWID BATCHED| XTEST           |    10 |    80 |    11   (0)| 00:00:01 |
|*  7 |       INDEX RANGE SCAN                  | ITEST           |    10 |       |     1   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   7 - access("A"=VALUE(KOKBF$))

Let’s see a bit more complex query:

Test tables 2

[sourcecode language=”sql”]
create table xtest1(id primary key, a) as
select level,level from dual connect by level<=1000;

create table xtest2(a, b, c) as
select mod(level,1000),level,rpad(‘x’,100,’x’)
from dual
connect by level<=1e4
/
create index itest2 on xtest2(a)
/
create or replace view vtest2 as
select a,count(b) cnt
from xtest2
group by a
/
[/sourcecode]

[collapse]

select v.* 
from xtest1 t1,
     vtest2 v
where t1.id in (select/*+ cardinality(c 1) */ * from table(sys.odcinumberlist(1,2,3)) c)
  and v.a = t1.a;

Plan hash value: 4293766070

-----------------------------------------------------------------------------------------------------------
| Id  | Operation                                  | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                           |              |     1 |    36 |    80   (3)| 00:00:01 |
|*  1 |  HASH JOIN                                 |              |     1 |    36 |    80   (3)| 00:00:01 |
|   2 |   JOIN FILTER CREATE                       | :BF0000      |     1 |    10 |    31   (4)| 00:00:01 |
|   3 |    NESTED LOOPS                            |              |     1 |    10 |    31   (4)| 00:00:01 |
|   4 |     NESTED LOOPS                           |              |     1 |    10 |    31   (4)| 00:00:01 |
|   5 |      SORT UNIQUE                           |              |     1 |     2 |    29   (0)| 00:00:01 |
|   6 |       COLLECTION ITERATOR CONSTRUCTOR FETCH|              |     1 |     2 |    29   (0)| 00:00:01 |
|*  7 |      INDEX UNIQUE SCAN                     | SYS_C0026365 |     1 |       |     0   (0)| 00:00:01 |
|   8 |     TABLE ACCESS BY INDEX ROWID            | XTEST1       |     1 |     8 |     1   (0)| 00:00:01 |
|   9 |   VIEW                                     | VTEST2       |  1000 | 26000 |    49   (3)| 00:00:01 |
|  10 |    HASH GROUP BY                           |              |  1000 |  8000 |    49   (3)| 00:00:01 |
|  11 |     JOIN FILTER USE                        | :BF0000      | 10000 | 80000 |    48   (0)| 00:00:01 |
|* 12 |      TABLE ACCESS FULL                     | XTEST2       | 10000 | 80000 |    48   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   1 - access("V"."A"="T1"."A")
   7 - access("T1"."ID"=VALUE(KOKBF$))
  12 - filter(SYS_OP_BLOOM_FILTER(:BF0000,"A"))

As you see, CVM can’t help in this case, but we can use lateral():

select/*+ no_merge(lat) */ lat.* 
from xtest1 t1,
     lateral(select * from vtest2 v where v.a = t1.a) lat
where t1.id in (select/*+ cardinality(c 1) */ * from table(sys.odcinumberlist(1,2,3)) c);

Plan hash value: 1798023704

------------------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name            | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |                 |    10 |   360 |    42   (3)| 00:00:01 |
|   1 |  NESTED LOOPS                            |                 |    10 |   360 |    42   (3)| 00:00:01 |
|   2 |   NESTED LOOPS                           |                 |     1 |    10 |    31   (4)| 00:00:01 |
|   3 |    SORT UNIQUE                           |                 |     1 |     2 |    29   (0)| 00:00:01 |
|   4 |     COLLECTION ITERATOR CONSTRUCTOR FETCH|                 |     1 |     2 |    29   (0)| 00:00:01 |
|   5 |    TABLE ACCESS BY INDEX ROWID           | XTEST1          |     1 |     8 |     1   (0)| 00:00:01 |
|*  6 |     INDEX UNIQUE SCAN                    | SYS_C0026365    |     1 |       |     0   (0)| 00:00:01 |
|   7 |   VIEW                                   | VW_LAT_A18161FF |    10 |   260 |    11   (0)| 00:00:01 |
|   8 |    SORT GROUP BY                         |                 |    10 |    80 |    11   (0)| 00:00:01 |
|   9 |     TABLE ACCESS BY INDEX ROWID BATCHED  | XTEST2          |    10 |    80 |    11   (0)| 00:00:01 |
|* 10 |      INDEX RANGE SCAN                    | ITEST2          |    10 |       |     1   (0)| 00:00:01 |
------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   6 - access("T1"."ID"=VALUE(KOKBF$))
  10 - access("A"="T1"."A")

There is also another workaround with non-documented “precompute_subquery” hint:

select v.* 
from xtest1 t1,
     vtest2 v 
where t1.id in (select/*+ precompute_subquery */ * from table(sys.odcinumberlist(1,2,3)) c)
and v.a = t1.a;

Plan hash value: 1964829099

------------------------------------------------------------------------------------------------
| Id  | Operation                       | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                |              |    30 |   480 |    37   (3)| 00:00:01 |
|   1 |  HASH GROUP BY                  |              |    30 |   480 |    37   (3)| 00:00:01 |
|   2 |   NESTED LOOPS                  |              |    30 |   480 |    36   (0)| 00:00:01 |
|   3 |    NESTED LOOPS                 |              |    30 |   480 |    36   (0)| 00:00:01 |
|   4 |     INLIST ITERATOR             |              |       |       |            |          |
|   5 |      TABLE ACCESS BY INDEX ROWID| XTEST1       |     3 |    24 |     3   (0)| 00:00:01 |
|*  6 |       INDEX UNIQUE SCAN         | SYS_C0026365 |     3 |       |     2   (0)| 00:00:01 |
|*  7 |     INDEX RANGE SCAN            | ITEST2       |    10 |       |     1   (0)| 00:00:01 |
|   8 |    TABLE ACCESS BY INDEX ROWID  | XTEST2       |    10 |    80 |    11   (0)| 00:00:01 |
------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   6 - access("T1"."ID"=1 OR "T1"."ID"=2 OR "T1"."ID"=3)
   7 - access("A"="T1"."A")

It can help even in most difficult cases, for example if you can’t rewrite query (in this case you can create sql patch or sql profile with “precompute_subquery”), but I wouldn’t suggest it since “precompute_subquery” is non-documented, it can be used only with simple collections and has limitation in 1000 values.
I’d suggest to use the workaround with lateral, since it’s most reliable and very simple.

v$sql_hint.target_level

Posted on by Posted in CBO, oracle, SQL, undocumented 2,012 Page views Leave a comment

Today I wanted to give a link to the description of v$sql_hint.target_level to show that no_parallel can be specified for statement or object, and though it’s pretty obvious, but surprisingly I haven’t found any articles or posts about it, so this short post describes it.
v$sql_hint.target_level is a bitset, where
1st bit set to 1 means that the hint can be specified on statement level,
2nd – on query block level,
3rd – on object level,
4th – on join level(for multiple objects).
Short example:

   select name,sql_feature
          ,class,inverse
          ,version,version_outline
          ,target_level
         ,decode(bitand(target_level,1),0,'no','yes') Statement_level
         ,decode(bitand(target_level,2),0,'no','yes') Query_block_level
         ,decode(bitand(target_level,4),0,'no','yes') Object_level
         ,decode(bitand(target_level,8),0,'no','yes') Join_level
   from v$sql_hint h;

with hints as (
   select name,sql_feature
          ,class,inverse
          ,version,version_outline
          ,target_level
         ,decode(bitand(target_level,1),0,'no','yes') Statement_level
         ,decode(bitand(target_level,2),0,'no','yes') Query_block_level
         ,decode(bitand(target_level,4),0,'no','yes') Object_level
         ,decode(bitand(target_level,8),0,'no','yes') Join_level
   from v$sql_hint h
)
select *
from hints
where statement_level='yes'
  and to_number(regexp_substr(version,'^\d+')) >= 18
order by version;

Result:

NAME              SQL_FEATURE     CLASS                VERSION  TARGET_LEVEL STATEMENT_LEVEL QUERY_BLOCK_LEVEL OBJECT_LEVEL JOIN_LEVEL
----------------- --------------- -------------------- -------- ------------ --------------- ----------------- ------------ ----------
PDB_LOCAL_ONLY    QKSFM_DML       PDB_LOCAL_ONLY       18.1.0              1 yes             no                no           no
SUPPRESS_LOAD     QKSFM_DDL       SUPPRESS_LOAD        18.1.0              1 yes             no                no           no
SYSTEM_STATS      QKSFM_ALL       SYSTEM_STATS         18.1.0              1 yes             no                no           no
MEMOPTIMIZE_WRITE QKSFM_EXECUTION MEMOPTIMIZE_WRITE    18.1.0              1 yes             no                no           no
SKIP_PROXY        QKSFM_ALL       SKIP_PROXY           18.1.0              1 yes             no                no           no
CURRENT_INSTANCE  QKSFM_ALL       CURRENT_INSTANCE     18.1.0              1 yes             no                no           no
JSON_LENGTH       QKSFM_EXECUTION JSON_LENGTH          19.1.0              1 yes             no                no           no
QUARANTINE        QKSFM_EXECUTION QUARANTINE           19.1.0              1 yes             no                no           no

Top time-consuming predicates from ASH

Posted on by Posted in oracle, query optimizing, SQL, statistics, troubleshooting 1,880 Page views Leave a comment

Sometimes it might be useful to analyze top time-consuming filter and access predicates from ASH, especially in cases when db load is spread evenly enough by different queries and top segments do not show anything interesting, except usual things like “some tables are requested more often than others”.
Of course, we can start from analysis of SYS.COL_USAGE$: col_usage.sql

col_usage.sql

[sourcecode language=”sql”]
col owner format a30
col oname format a30 heading "Object name"
col cname format a30 heading "Column name"
accept owner_mask prompt "Enter owner mask: ";
accept tab_name prompt "Enter tab_name mask: ";
accept col_name prompt "Enter col_name mask: ";

SELECT a.username as owner
,o.name as oname
,c.name as cname
,u.equality_preds as equality_preds
,u.equijoin_preds as equijoin_preds
,u.nonequijoin_preds as nonequijoin_preds
,u.range_preds as range_preds
,u.like_preds as like_preds
,u.null_preds as null_preds
,to_char(u.timestamp, ‘yyyy-mm-dd hh24:mi:ss’) when
FROM
sys.col_usage$ u
, sys.obj$ o
, sys.col$ c
, all_users a
WHERE a.user_id = o.owner#
AND u.obj# = o.obj#
AND u.obj# = c.obj#
AND u.intcol# = c.col#
AND a.username like upper(‘&owner_mask’)
AND o.name like upper(‘&tab_name’)
AND c.name like upper(‘&col_name’)
ORDER BY a.username, o.name, c.name
;
col owner clear;
col oname clear;
col cname clear;
undef tab_name col_name owner_mask;
[/sourcecode]

[collapse]

But it’s not enough, for example it doesn’t show predicates combinations. In this case we can use v$active_session_history and v$sql_plan:

Top 50 predicates

[sourcecode language=”sql”]
with
ash as (
select
sql_id
,plan_hash_value
,table_name
,alias
,ACCESS_PREDICATES
,FILTER_PREDICATES
,count(*) cnt
from (
select
h.sql_id
,h.SQL_PLAN_HASH_VALUE plan_hash_value
,decode(p.OPERATION
,’TABLE ACCESS’,p.OBJECT_OWNER||’.’||p.OBJECT_NAME
,(select i.TABLE_OWNER||’.’||i.TABLE_NAME from dba_indexes i where i.OWNER=p.OBJECT_OWNER and i.index_name=p.OBJECT_NAME)
) table_name
,OBJECT_ALIAS ALIAS
,p.ACCESS_PREDICATES
,p.FILTER_PREDICATES
— поля, которые могут быть полезны для анализа в других разрезах:
— ,h.sql_plan_operation
— ,h.sql_plan_options
— ,decode(h.session_state,’ON CPU’,’ON CPU’,h.event) event
— ,h.current_obj#
from v$active_session_history h
,v$sql_plan p
where h.sql_opname=’SELECT’
and h.IN_SQL_EXECUTION=’Y’
and h.sql_plan_operation in (‘INDEX’,’TABLE ACCESS’)
and p.SQL_ID = h.sql_id
and p.CHILD_NUMBER = h.SQL_CHILD_NUMBER
and p.ID = h.SQL_PLAN_LINE_ID
— если захотим за последние 3 часа:
— and h.sample_time >= systimestamp – interval ‘3’ hour
)
— если захотим анализируем предикаты только одной таблицы:
— where table_name=’&OWNER.&TABNAME’
group by
sql_id
,plan_hash_value
,table_name
,alias
,ACCESS_PREDICATES
,FILTER_PREDICATES
)
,agg_by_alias as (
select
table_name
,regexp_substr(ALIAS,’^[^@]+’) ALIAS
,listagg(ACCESS_PREDICATES,’ ‘) within group(order by ACCESS_PREDICATES) ACCESS_PREDICATES
,listagg(FILTER_PREDICATES,’ ‘) within group(order by FILTER_PREDICATES) FILTER_PREDICATES
,sum(cnt) cnt
from ash
group by
sql_id
,plan_hash_value
,table_name
,alias
)
,agg as (
select
table_name
,’ALIAS’ alias
,replace(access_predicates,’"’||alias||’".’,’"ALIAS".’) access_predicates
,replace(filter_predicates,’"’||alias||’".’,’"ALIAS".’) filter_predicates
,sum(cnt) cnt
from agg_by_alias
group by
table_name
,replace(access_predicates,’"’||alias||’".’,’"ALIAS".’)
,replace(filter_predicates,’"’||alias||’".’,’"ALIAS".’)
)
,cols as (
select
table_name
,cols
,access_predicates
,filter_predicates
,sum(cnt)over(partition by table_name,cols) total_by_cols
,cnt
from agg
,xmltable(
‘string-join(for $c in /ROWSET/ROW/COL order by $c return $c,",")’
passing
xmltype(
cursor(
(select distinct
nvl(
regexp_substr(
access_predicates||’ ‘||filter_predicates
,'("’||alias||’"\.|[^.]|^)"([A-Z0-9#_$]+)"([^.]|$)’
,1
,level
,’i’,2
),’ ‘)
col
from dual
connect by
level<=regexp_count(
access_predicates||’ ‘||filter_predicates
,'("’||alias||’"\.|[^.]|^)"([A-Z0-9#_$]+)"([^.]|$)’
)
)
))
columns cols varchar2(400) path ‘.’
)(+)
order by total_by_cols desc, table_name, cnt desc
)
select
table_name
,cols
,sum(cnt)over(partition by table_name,cols) total_by_cols
,access_predicates
,filter_predicates
,cnt
from cols
where rownum<=50
order by total_by_cols desc, table_name, cnt desc;
[/sourcecode]

[collapse]

As you can see it shows top 50 predicates and their columns for last 3 hours. Despite the fact that ASH stores just sampled data, its results are representative enough for high-load databases.
Just few details:

  • Column “COLS” shows “search columns”, and total_by_cols – their number of occurrences
  • I think it’s obvious, that this info is not unambiguous marker of the problem, because for example few full table scans can misrepresent the statistics, so sometimes you will need to analyze such queries deeper (v$sqlstats,dba_hist_sqlstat)
  • We need to group data by OBJECT_ALIAS within SQL_ID and plan_hash_value, because in case of index access with lookup to table(“table access by rowid”) some predicates are in the row with index access and others are in the row with table access.

Depending on the needs, we can modify this query to analyze ASH data by different dimensions, for example with additional analysis of partitioning or wait events.