@tracefile_read_last_by_mask filemask [regexp] [ignore_regexp]
– finds last trace by filemask and filters rows by regexp and filters out rows by ignore_regexp:
@ tracefile_by_mask.sql [mask] – finds and shows last 10 trace files by mask
Continue readingOracle diagnostic events is a great feature, but unfortunately poorly documented and nonintuitive, so it’s difficult to remember all events/actions/parameters and even read its internal documentation using oradebug. So I decided to compile its internal doc as a more convenient html-version (https://orasql.org/files/events/) and make a cheat sheet of some unknown or little-known use cases.
alter system set events
'kg_event[1476]
{occurence: start_after 1, end_after 3}
trace("stack is: %\n", shortstack())
errorstack(2)
';
alter system set events
'trace[SQL_Compiler.* | SQL_Execution.*]
[SQL: ...]
{process: ospid = ...}
{occurence:end_after 3}
controlc_signal()';
You can add also any information from v$rtsm_sql_plan_monitor if needed
create or replace function px_session_info return varchar2 parallel_enable as
vSID int;
res varchar2(30);
begin
vSID:=userenv('sid');
select
to_char(s.server_group,'fm000')
||'-'||to_char(s.server_set,'fm0000')
||'-'||to_char(s.server#,'fm0000')
||'('||s.sid||','||s.degree||'/'||s.req_degree||')'
into res
from v$px_session s
where s.sid=vSID;
return res;
exception when no_data_found then
return 'no_parallel';
end;
/
Simple example:
select--+ parallel
px_session_info, count(*)
from sys.obj$
group by px_session_info
/
PX_SESSION_INFO COUNT(*)
------------------------ --------
001-0002-0001(630,2/2) 38298
001-0002-0002(743,2/2) 34706
Since Christmas I have been asked to investigate two different “failures to use Smart Scan”. It turns out they both fell into the same little known restriction on the use of Direct Read. Smart Scan critically depends on Direct Read in order to read the synthetic output blocks into private buffers in PGA so with Direct Read disabled Smart Scan is also disabled. In these two cases the restriction is on using Direct Read on Serial Recursive queries.
A customer asked me to investigate why his MView refresh was running slowly and was failing to use Smart Scan. He had used 'trace[NSMTIO] disk=highest' which showed the cause as:
Direct Read for serial qry: disabled(::recursive_call::kctfsage:::)
As of Oracle 19c OJPPD doesn’t support connect-by and TABLE():
OJPPD: OJPPD bypassed: query block contains START WITH/CONNECT BY.
OJPPD: OJPPD bypassed: View contains TABLE expression.
Simple example: tracefiles for the last 5 days:
select fc.*
from v$diag_trace_file f
join v$diag_trace_file_contents fc
on f.adr_home=fc.adr_home
and f.trace_filename=fc.trace_filename
where f.modify_time >= systimestamp - interval'5' minute
and fc.timestamp >= systimestamp - interval'5' minute
and fc.component_name = 'SQL_Trace'
--and fc.section_name like 'kests%'
;
--or:
select tr.*
from v$diag_app_trace_file tf,
v$diag_sql_trace_records tr
where tf.sql_trace = 'Y'
and tf.modify_time > systimestamp - interval'5'minute
and tr.adr_home = tf.adr_home
and tr.trace_filename = tf.trace_filename
and tr.timestamp > systimestamp - interval'5'minute;
You may know that table() (kokbf$ collection functions), xmltable and json_table functions block Join-Predicate PushDown(JPPD).
Simple example:
[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]
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$))
[sourcecode language=”sql”]
select/*+ cardinality(c 1) use_nl(v) push_pred(v) */ *
from json_table(‘{"a":[1,2,3]}’, ‘$.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$1
U – push_pred(v)
[/sourcecode]
[sourcecode language=”sql”]
select/*+ leading(c v) cardinality(c 1) use_nl(v) push_pred(v) */ v.*
from xmltable(‘(1,3)’ columns a int path ‘.’) c,vtest v
where c.a = v.a(+);
Plan hash value: 564839666
————————————————————————————————————
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
————————————————————————————————————
| 0 | SELECT STATEMENT | | 1 | 28 | 78 (2)| 00:00:01 |
| 1 | NESTED LOOPS OUTER | | 1 | 28 | 78 (2)| 00:00:01 |
| 2 | COLLECTION ITERATOR PICKLER FETCH| XQSEQUENCEFROMXMLTYPE | 1 | 2 | 29 (0)| 00:00:01 |
|* 3 | VIEW | VTEST | 1 | 26 | 49 (3)| 00:00:01 |
| 4 | SORT GROUP BY | | 1000 | 8000 | 49 (3)| 00:00:01 |
| 5 | TABLE ACCESS FULL | XTEST | 10000 | 80000 | 48 (0)| 00:00:01 |
————————————————————————————————————
Predicate Information (identified by operation id):
—————————————————
3 – filter("V"."A"(+)=CAST(TO_NUMBER(SYS_XQ_UPKXML2SQL(SYS_XQEXVAL(VALUE(KOKBF$),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]
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$))
[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$),0,0,54525952,0),50,1,2)) AS int ))
[/sourcecode]
2. Avoid JPPD using lateral():
select/*+ cardinality(c 1) no_merge(lat) */
distinct lat.*
from table(sys.odcinumberlist(1,2,3)) c,
lateral(select * from vtest v where v.a = c.column_value) lat;
Plan hash value: 18036714
-----------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 10 | 190 | 41 (3)| 00:00:01 |
| 1 | HASH UNIQUE | | 10 | 190 | 41 (3)| 00:00:01 |
| 2 | NESTED LOOPS | | 10 | 190 | 40 (0)| 00:00:01 |
| 3 | COLLECTION ITERATOR CONSTRUCTOR FETCH| | 1 | 2 | 29 (0)| 00:00:01 |
| 4 | VIEW | VW_LAT_4DB60E85 | 10 | 170 | 11 (0)| 00:00:01 |
| 5 | SORT GROUP BY | | 10 | 80 | 11 (0)| 00:00:01 |
| 6 | TABLE ACCESS BY INDEX ROWID BATCHED| XTEST | 10 | 80 | 11 (0)| 00:00:01 |
|* 7 | INDEX RANGE SCAN | ITEST | 10 | | 1 (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
7 - access("A"=VALUE(KOKBF$))
Let’s see a bit more complex query:
[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]
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.
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$: col_usage.sql
[sourcecode language=”sql”]
col owner format a30
col oname format a30 heading "Object name"
col cname format a30 heading "Column name"
accept owner_mask prompt "Enter owner mask: ";
accept tab_name prompt "Enter tab_name mask: ";
accept col_name prompt "Enter col_name mask: ";
SELECT a.username as owner
,o.name as oname
,c.name as cname
,u.equality_preds as equality_preds
,u.equijoin_preds as equijoin_preds
,u.nonequijoin_preds as nonequijoin_preds
,u.range_preds as range_preds
,u.like_preds as like_preds
,u.null_preds as null_preds
,to_char(u.timestamp, ‘yyyy-mm-dd hh24:mi:ss’) when
FROM
sys.col_usage$ 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]
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:
[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 $c,",")’
passing
xmltype(
cursor(
(select distinct
nvl(
regexp_substr(
access_predicates||’ ‘||filter_predicates
,'("’||alias||’"\.|[^.]|^)"([A-Z0-9#_$]+)"([^.]|$)’
,1
,level
,’i’,2
),’ ‘)
col
from dual
connect by
level<=regexp_count(
access_predicates||’ ‘||filter_predicates
,'("’||alias||’"\.|[^.]|^)"([A-Z0-9#_$]+)"([^.]|$)’
)
)
))
columns cols varchar2(400) path ‘.’
)(+)
order by total_by_cols desc, table_name, cnt desc
)
select
table_name
,cols
,sum(cnt)over(partition by table_name,cols) total_by_cols
,access_predicates
,filter_predicates
,cnt
from cols
where rownum<=50
order by total_by_cols desc, table_name, cnt desc;
[/sourcecode]
As you can see it shows top 50 predicates and their columns for last 3 hours. Despite the fact that ASH stores just sampled data, its results are representative enough for high-load databases.
Just few details:
Depending on the needs, we can modify this query to analyze ASH data by different dimensions, for example with additional analysis of partitioning or wait events.
A friend contacted me to ask why they were having problems using the table_stats hint to influence optimizer decision making and also to influence the decision to use direct read or buffer cache scan so this is just a quick blog post to clarify the syntax as it is not well documented.
table_stats(<table_name> <method> {<keyword>=<value>} )
Method is one of: DEFAULT, SET, SCALE, SAMPLE
Keyword is one of: BLOCKS, ROWS, ROW_LENGTH
Compare the results of the following query with the clause “fetch first 2 rows only”
with
t1(a) as (select * from table(odcinumberlist(1,3)))
,t2(a,b) as (select * from table(ku$_objnumpairlist(
sys.ku$_objnumpair(1,1),
sys.ku$_objnumpair(1,2),
sys.ku$_objnumpair(1,3),
sys.ku$_objnumpair(3,1),
sys.ku$_objnumpair(3,2),
sys.ku$_objnumpair(3,3)
)))
,t(id) as (select * from table(odcinumberlist(1,2,3,4,5,6,7)))
select
*
from t,
lateral(select t1.a,t2.b
from t1,t2
where t1.a = t2.a
and t1.a = t.id
order by t2.b
fetch first 2 rows only
)(+)
order by id;
ID A B
---------- ---------- ----------
1 1 1
1 3 1
2 1 1
2 3 1
3 1 1
3 3 1
4 1 1
4 3 1
5 1 1
5 3 1
6 1 1
6 3 1
7 1 1
7 3 1
14 rows selected.
with this one (i’ve just commented out the line with “fetch-first-rows-only”:
with
t1(a) as (select * from table(odcinumberlist(1,3)))
,t2(a,b) as (select * from table(ku$_objnumpairlist(
sys.ku$_objnumpair(1,1),
sys.ku$_objnumpair(1,2),
sys.ku$_objnumpair(1,3),
sys.ku$_objnumpair(3,1),
sys.ku$_objnumpair(3,2),
sys.ku$_objnumpair(3,3)
)))
,t(id) as (select * from table(odcinumberlist(1,2,3,4,5,6,7)))
select
*
from t,
lateral(select t1.a,t2.b
from t1,t2
where t1.a = t2.a
and t1.a = t.id
order by t2.b
-- fetch first 2 rows only
)(+)
order by id;
ID A B
---------- ---------- ----------
1 1 2
1 1 3
1 1 1
2
3 3 2
3 3 1
3 3 3
4
5
6
7
11 rows selected.
Obviously, the first query should return less rows than second one, but we can see that it returned more rows and join predicate “and t1.a = t.id” was ignored, because A and B are not empty and “A” is not equal to t.ID.