Author Archives: Sayan Malakshinov

SQL: Split periods

Posted on by Posted in oracle, SQL 1,692 Page views Leave a comment

Some time ago there was a question on the sql.ru about splitting validity periods for gym cards to enabled and blocked periods:

-- we have a table with gym cards (card id, validity period: from/to)
CREATE TABLE cards(id_card NUMBER, num_card VARCHAR2(100), dt_from DATE, dt_to DATE);
-- periods when cards were blocked (record id, card id, dates from/to)
CREATE TABLE blocks(id_block NUMBER, id_card NUMBER, dt_from DATE, dt_to DATE);
-- cards data:
INSERT INTO cards (id_card, num_card, dt_from, dt_to) VALUES (1,'111111', TO_DATE('01.09.2016','DD.MM.YYYY'), TO_DATE('01.09.2019','DD.MM.YYYY'));
INSERT INTO cards (id_card, num_card, dt_from, dt_to) VALUES (2,'222222', TO_DATE('06.10.2017','DD.MM.YYYY'), TO_DATE('31.12.2050','DD.MM.YYYY'));
INSERT INTO cards (id_card, num_card, dt_from, dt_to) VALUES (3,'333333', TO_DATE('09.05.2019','DD.MM.YYYY'), TO_DATE('31.12.2050','DD.MM.YYYY'));
-- blocks data:
INSERT INTO blocks (id_block, id_card, dt_from, dt_to) VALUES (101, 1, TO_DATE('20.03.2017','DD.MM.YYYY'), TO_DATE('09.04.2017','DD.MM.YYYY'));
INSERT INTO blocks (id_block, id_card, dt_from, dt_to) VALUES (102, 1, TO_DATE('14.01.2018','DD.MM.YYYY'), TO_DATE('03.02.2018','DD.MM.YYYY'));
INSERT INTO blocks (id_block, id_card, dt_from, dt_to) VALUES (103, 1, TO_DATE('18.02.2019','DD.MM.YYYY'), TO_DATE('10.03.2019','DD.MM.YYYY'));
INSERT INTO blocks (id_block, id_card, dt_from, dt_to) VALUES (201, 2, TO_DATE('02.08.2018','DD.MM.YYYY'), TO_DATE('10.11.2018','DD.MM.YYYY'));
COMMIT;

Just for fun, I’ve solved it using match_recognise:

with 
 cards (id_card, num_card, dt_from, dt_to) as (
   select 1,'111111', TO_DATE('01.09.2016','DD.MM.YYYY'), TO_DATE('01.09.2019','DD.MM.YYYY') from dual union all
   select 2,'222222', TO_DATE('06.10.2017','DD.MM.YYYY'), TO_DATE('31.12.2050','DD.MM.YYYY') from dual union all
   select 3,'333333', TO_DATE('09.05.2019','DD.MM.YYYY'), TO_DATE('31.12.2050','DD.MM.YYYY') from dual 
),blocks (id_block, id_card, dt_from, dt_to) as (
   select 101, 1, TO_DATE('20.03.2017','DD.MM.YYYY'), TO_DATE('09.04.2017','DD.MM.YYYY') from dual union all
   select 102, 1, TO_DATE('14.01.2018','DD.MM.YYYY'), TO_DATE('03.02.2018','DD.MM.YYYY') from dual union all
   select 103, 1, TO_DATE('18.02.2019','DD.MM.YYYY'), TO_DATE('10.03.2019','DD.MM.YYYY') from dual union all
   select 201, 2, TO_DATE('02.08.2018','DD.MM.YYYY'), TO_DATE('10.11.2018','DD.MM.YYYY') from dual
)
,data as (
   select *
   from (
         select * 
         from (
            select id_card,dt_from,dt_to,'c' t from cards
            union all
            select id_card,dt_to,dt_from,'b' from blocks
         ) v
         --where v.id_card=1 -- here we can filter card ID if needed
        ) piv
   unpivot(dt for x in (dt_from as 'dt_from', dt_to as 'dt_to'))
)
select * 
from data
match_recognize (
   partition by id_card
   order by dt /* we can modify add "t" into "order by clause" to make it more deterministic */
   measures
      match_number() as N,
      first(dt) as dt_from,
      last(dt) as dt_to
   pattern (x_start+ x_end)
   define
      x_start as x = 'dt_from'
     ,x_end   as x = 'dt_to'
);

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

Posted on by Posted in CBO, oracle, query optimizing, SQL, troubleshooting 2,150 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 1,995 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 1,870 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.

Another bug with lateral

Posted on by Posted in 12c, bug, CBO, curious, oracle, troubleshooting 1,616 Page views Leave a comment

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.

Lateral view decorrelation(VW_DCL) causes wrong results with rownum

Posted on by Posted in 12c, bug, CBO, oracle, query optimizing, rownum, troubleshooting 1,930 Page views 3 Comments

Everyone knows that rownum in inline views blocks many query transformations, for example pushing/pulling predicates, scalar subquery unnesting, etc, and many people use it for such purposes as a workaround to avoid unwanted transformations(or even CBO bugs).

Obviously, the main reason of that is different calculation of rownum:

If we pull the predicate “column_value = 3″ from the following query to higher level
[sourcecode language=”sql” highlight=””]
select *
from (select * from table(odcinumberlist(1,1,1,2,2,2,3,3,3)) order by 1)
where rownum <= 2
and column_value = 3;

COLUMN_VALUE
————
3
3
[/sourcecode]
we will get different results:
[sourcecode language=”sql” highlight=”8″]
select *
from (select *
from (select * from table(odcinumberlist(1,1,1,2,2,2,3,3,3)) order by 1)
where rownum <= 2
)
where column_value = 3;

no rows selected
[/sourcecode]
Doc ID 62340.1

[collapse]

But we recently encountered a bug with it: lateral view with ROWNUM returns wrong results in case of lateral view decorrelation.
Compare results of this query with and without no_decorrelation hint:

with 
 t1(a) as (select * from table(odcinumberlist(1,3)))
,t2(b) as (select * from table(odcinumberlist(1,1,3,3)))
,t(id) as (select * from table(odcinumberlist(1,2,3)))
select
  *
from t,
     lateral(select/*+ no_decorrelate */ rownum rn 
             from t1,t2 
             where t1.a=t2.b and t1.a = t.id
            )(+)
order by 1,2;

        ID         RN
---------- ----------
         1          1
         1          2
         2
         3          1
         3          2

with 
 t1(a) as (select * from table(odcinumberlist(1,3)))
,t2(b) as (select * from table(odcinumberlist(1,1,3,3)))
,t(id) as (select * from table(odcinumberlist(1,2,3)))
select
  *
from t,
     lateral(select rownum rn 
             from t1,t2 
             where t1.a=t2.b and t1.a = t.id
            )(+)
order by 1,2;

        ID         RN
---------- ----------
         1          1
         1          2
         2
         3          3
         3          4

Of course, we can draw conclusions even from these results: we can see that in case of decorrelation(query with hint) rownum was calculated before the join. But to be sure we can check optimizer’s trace 10053:

Final query after transformations:

[sourcecode language=”sql”]
******* UNPARSED QUERY IS *******
SELECT VALUE(KOKBF$2) "ID", "VW_DCL_76980902"."RN" "RN"
FROM TABLE("ODCINUMBERLIST"(1, 2, 3)) "KOKBF$2",
(SELECT ROWNUM "RN_0", VALUE(KOKBF$0) "ITEM_3"
FROM TABLE("ODCINUMBERLIST"(1, 3)) "KOKBF$0",
TABLE("ODCINUMBERLIST"(1, 1, 3, 3)) "KOKBF$1"
WHERE VALUE(KOKBF$0) = VALUE(KOKBF$1)
) "VW_DCL_76980902"
WHERE "VW_DCL_76980902"."ITEM_3"(+) = VALUE(KOKBF$2)
ORDER BY VALUE(KOKBF$2), "VW_DCL_76980902"."RN"

*************************
[/sourcecode]

[collapse]

I’ll modify it a bit just to make it more readable:
we can see that 

select
  *
from t,
     lateral(select rownum rn 
             from t1,t2 
             where t1.a=t2.b and t1.a = t.id)(+)
order by 1,2;

was transformed to 

select
  t.id, dcl.rn
from t,
     (select rownum rn 
      from t1,t2 
      where t1.a=t2.b) dcl
where dcl.a(+) = t.id
order by 1,2;

And it confirms that rownum was calculated on the different dataset (t1-t2 join) without join filter by table t.
I created SR with Severity 1 (SR #3-19117219271) more than a month ago, but unfortunately Oracle development doesn’t want to fix this bug and moreover they say that is not a bug. So I think this is a dangerous precedent and probably soon we will not be able to be sure in the calculation of rownum and old fixes…

Top-N again: fetch first N rows only vs rownum

Posted on by Posted in adaptive serial direct path reads, CBO, oracle, query optimizing, SQL, troubleshooting 7,301 Page views Leave a comment

Three interesting myths about rowlimiting clause vs rownum have recently been posted on our Russian forum:

  1. TopN query with rownum<=N is always faster than "fetch first N rows only" (ie. row_number()over(order by ...)<=N)
  2. “fetch first N rows only” is always faster than rownum<=N
  3. “SORT ORDER BY STOPKEY” stores just N top records during sorting, while “WINDOW SORT PUSHED RANK” sorts all input and stores all records sorted in memory.

Interestingly that after Vyacheslav posted first statement as an axiom and someone posted old tests(from 2009) and few people made own tests which showed that “fetch first N rows” is about 2-3 times faster than the query with rownum, the final decision was that “fetch first” is always faster.

First of all I want to show that statement #3 is wrong and “WINDOW SORT PUSHED RANK” with row_number works similarly as “SORT ORDER BY STOPKEY”:
It’s pretty easy to show using sort trace:
Let’s create simple small table Tests1 with 1000 rows where A is in range 1-1000 (just 1 block):

create table test1(a not null, b) as
  select level, level from dual connect by level<=1000;

alter session set max_dump_file_size=unlimited;
ALTER SESSION SET EVENTS '10032 trace name context forever, level 10';

ALTER SESSION SET tracefile_identifier = 'rownum';
select * from (select * from test1 order by a) where rownum<=10;

ALTER SESSION SET tracefile_identifier = 'rownumber';
select * from test1 order by a fetch first 10 rows only;

And we can see from the trace files that both queries did the same number of comparisons:

rownum:

[sourcecode language=”sql” highlight=”7″]
—– Current SQL Statement for this session (sql_id=bbg66rcbt76zt) —–
select * from (select * from test1 order by a) where rownum<=10

—- Sort Statistics ——————————
Input records 1000
Output records 10
Total number of comparisons performed 999
Comparisons performed by in-memory sort 999
Total amount of memory used 2048
Uses version 1 sort
—- End of Sort Statistics ———————–
[/sourcecode]

[collapse]
row_number

[sourcecode language=”sql” highlight=”7″]
—– Current SQL Statement for this session (sql_id=duuy4bvaz3d0q) —–
select * from test1 order by a fetch first 10 rows only

—- Sort Statistics ——————————
Input records 1000
Output records 10
Total number of comparisons performed 999
Comparisons performed by in-memory sort 999
Total amount of memory used 2048
Uses version 1 sort
—- End of Sort Statistics ———————–
[/sourcecode]

[collapse]

Ie. each row (except first one) was compared with the biggest value from top 10 values and since they were bigger than top 10 value, oracle doesn’t compare it with other TopN values.

And if we change the order of rows in the table both of these queries will do the same number of comparisons again:

from 999 to 0

[sourcecode language=”sql”]
create table test1(a not null, b) as
select 1000-level, level from dual connect by level<=1000;

alter session set max_dump_file_size=unlimited;
ALTER SESSION SET EVENTS ‘10032 trace name context forever, level 10’;

ALTER SESSION SET tracefile_identifier = ‘rownum’;
select * from (select * from test1 order by a) where rownum<=10;

ALTER SESSION SET tracefile_identifier = ‘rownumber’;
select * from test1 order by a fetch first 10 rows only;
[/sourcecode]

[collapse]
rownum

[sourcecode language=”sql” highlight=”7″]
—– Current SQL Statement for this session (sql_id=bbg66rcbt76zt) —–
select * from (select * from test1 order by a) where rownum<=10

—- Sort Statistics ——————————
Input records 1000
Output records 1000
Total number of comparisons performed 4976
Comparisons performed by in-memory sort 4976
Total amount of memory used 2048
Uses version 1 sort
—- End of Sort Statistics ———————–
[/sourcecode]

[collapse]
row_number

[sourcecode language=”sql” highlight=”7″]
—– Current SQL Statement for this session (sql_id=duuy4bvaz3d0q) —–
select * from test1 order by a fetch first 10 rows only

—- Sort Statistics ——————————
Input records 1000
Output records 1000
Total number of comparisons performed 4976
Comparisons performed by in-memory sort 4976
Total amount of memory used 2048
Uses version 1 sort
—- End of Sort Statistics ———————–
[/sourcecode]

[collapse]

We can see that both queries required much more comparisons(4976) here, that’s because each new value is smaller than the biggest value from the topN and even smaller than lowest value, so oracle should get right position for it and it requires 5 comparisons for that (it compares with 10th value, then with 6th, 3rd, 2nd and 1st values from top10). Obviously it makes less comparisons for the first 10 rows.

Now let’s talk about statements #1 and #2:
We know that rownum forces optimizer_mode to switch to “first K rows”, because of the parameter “_optimizer_rownum_pred_based_fkr”

SQL> @param_ rownum

NAME                               VALUE  DEFLT  TYPE      DESCRIPTION
---------------------------------- ------ ------ --------- ------------------------------------------------------
_optimizer_rownum_bind_default     10     TRUE   number    Default value to use for rownum bind
_optimizer_rownum_pred_based_fkr   TRUE   TRUE   boolean   enable the use of first K rows due to rownum predicate
_px_rownum_pd                      TRUE   TRUE   boolean   turn off/on parallel rownum pushdown optimization

while fetch first/row_number doesn’t (it will be changed after the patch #22174392) and it leads to the following consequences:
1. first_rows disables serial direct reads optimization(or smartscan on Exadata), that’s why the tests with big tables showed that “fetch first” were much faster than the query with rownum.
So if we set “_serial_direct_read”=always, we get the same performance in both tests (within the margin of error).

2. In cases when index access (index full scan/index range scan) is better, CBO differently calculates the cardinality of underlying INDEX FULL(range) SCAN:
the query with rownum is optimized for first_k_rows and the cardinality of index access is equal to K rows, but CBO doesn’t reduce cardinality for “fetch first”, so the cost of index access is much higher, compare them:

rownum

[sourcecode language=”sql” highlight=”13″]
SQL> explain plan for
2 select *
3 from (select * from test order by a,b)
4 where rownum<=10;

——————————————————————————————–
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
——————————————————————————————–
| 0 | SELECT STATEMENT | | 10 | 390 | 4 (0)| 00:00:01 |
|* 1 | COUNT STOPKEY | | | | | |
| 2 | VIEW | | 10 | 390 | 4 (0)| 00:00:01 |
| 3 | TABLE ACCESS BY INDEX ROWID| TEST | 1000K| 12M| 4 (0)| 00:00:01 |
| 4 | INDEX FULL SCAN | IX_TEST_AB | 10 | | 3 (0)| 00:00:01 |
——————————————————————————————–

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

1 – filter(ROWNUM<=10)
[/sourcecode]

[collapse]
fetch first

[sourcecode language=”sql” highlight=”13″]
SQL> explain plan for
2 select *
3 from test
4 order by a,b
5 fetch first 10 rows only;

—————————————————————————————–
| Id | Operation | Name | Rows | Bytes |TempSpc| Cost (%CPU)| Time |
—————————————————————————————–
| 0 | SELECT STATEMENT | | 10 | 780 | | 5438 (1)| 00:00:01 |
|* 1 | VIEW | | 10 | 780 | | 5438 (1)| 00:00:01 |
|* 2 | WINDOW SORT PUSHED RANK| | 1000K| 12M| 22M| 5438 (1)| 00:00:01 |
| 3 | TABLE ACCESS FULL | TEST | 1000K| 12M| | 690 (1)| 00:00:01 |
—————————————————————————————–

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

1 – filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=10)
2 – filter(ROW_NUMBER() OVER ( ORDER BY "TEST"."A","TEST"."B")<=10)
[/sourcecode]

[collapse]
fetch first + first_rows

[sourcecode language=”sql” highlight=”14″]
SQL> explain plan for
2 select/*+ first_rows */ *
3 from test
4 order by a,b
5 fetch first 10 rows only;

——————————————————————————————–
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
——————————————————————————————–
| 0 | SELECT STATEMENT | | 10 | 780 | 27376 (1)| 00:00:02 |
|* 1 | VIEW | | 10 | 780 | 27376 (1)| 00:00:02 |
|* 2 | WINDOW NOSORT STOPKEY | | 1000K| 12M| 27376 (1)| 00:00:02 |
| 3 | TABLE ACCESS BY INDEX ROWID| TEST | 1000K| 12M| 27376 (1)| 00:00:02 |
| 4 | INDEX FULL SCAN | IX_TEST_AB | 1000K| | 2637 (1)| 00:00:01 |
——————————————————————————————–

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

1 – filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=10)
2 – filter(ROW_NUMBER() OVER ( ORDER BY "TEST"."A","TEST"."B")<=10)
[/sourcecode]

[collapse]
fetch first + index

[sourcecode language=”sql” highlight=”14″]
SQL> explain plan for
2 select/*+ index(test (a,b)) */ *
3 from test
4 order by a,b
5 fetch first 10 rows only;

——————————————————————————————–
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
——————————————————————————————–
| 0 | SELECT STATEMENT | | 10 | 780 | 27376 (1)| 00:00:02 |
|* 1 | VIEW | | 10 | 780 | 27376 (1)| 00:00:02 |
|* 2 | WINDOW NOSORT STOPKEY | | 1000K| 12M| 27376 (1)| 00:00:02 |
| 3 | TABLE ACCESS BY INDEX ROWID| TEST | 1000K| 12M| 27376 (1)| 00:00:02 |
| 4 | INDEX FULL SCAN | IX_TEST_AB | 1000K| | 2637 (1)| 00:00:01 |
——————————————————————————————–

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

1 – filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=10)
2 – filter(ROW_NUMBER() OVER ( ORDER BY "TEST"."A","TEST"."B")<=10)
[/sourcecode]

[collapse]

So in this case we can add hints “first_rows” or “index”, or install the patch #22174392.

ps. I thought to post this note later, since I hadn’t time enough to add other interesting details about the different TopN variants, including “with tie”, rank(), etc, so I’ll post another note with more details later.

Docker with Oracle database: install patches automatically

Posted on by Posted in Docker, oracle 1,909 Page views Leave a comment

Recently I had to install the patch for fixing cross-platform PDB transport bug onto the docker images with Oracle, so these are easy way how to do it:

1. create directory “patches” and create “install_patches.sh”: 

#!/bin/bash

unzip  -u ./*.zip
CURDIR=`pwd`

for D in *; do
    if [ -d "${D}" ]; then
        echo =================================================
        echo " *** Processing patch # ${D}... "   # your processing here
        cd "${D}"
        opatch apply -silent
    fi
    cd $CURDIR
done

2. add the following commands into the dockerfile:

USER root

# Copy DB install file
COPY --chown=oracle:dba patches $INSTALL_DIR/patches

# Install DB software binaries
USER oracle
RUN chmod ug+x $INSTALL_DIR/patches/*.sh && \
    sync && \
    cd $INSTALL_DIR/patches && \
    ./install_patches.sh

3. put downloaded patches into the “patches” directory and build image.

For example, dockerfile for 18.3:

FROM oracle/database:18.3.0-ee

MAINTAINER Sayan Malakshinov <sayan@orasql.org>

USER root

# Copy patches:
COPY --chown=oracle:dba patches $INSTALL_DIR/patches

# Install patches:
USER oracle
RUN chmod ug+x $INSTALL_DIR/patches/*.sh && \
    sync && \
    cd $INSTALL_DIR/patches && \
    ./install_patches.sh

ps. I’ve also create the request for that in the official Docker github: https://github.com/oracle/docker-images/issues/1070

Top N biggest tables (with lobs, indexes and nested table)

Posted on by Posted in oracle 2,696 Page views Leave a comment

Script for SQL*Plus: https://github.com/xtender/xt_scripts/blob/master/tops/top_seg_by_size.sql

with 
  seg as (
     select 
       owner,segment_name
      ,segment_type
      ,tablespace_name
      ,sum(blocks) blocks
      ,sum(bytes)  bytes
     from dba_segments s
     where  segment_type not in (
       'TYPE2 UNDO'
      ,'ROLLBACK'
      ,'SYSTEM STATISTICS'
     )
     and segment_name not like 'BIN$%' --not in recyclebin
     and owner like '&owner_mask' -- you can specify schema here
     group by owner,segment_name,segment_type,tablespace_name
  )
 ,segs as (
     select 
       owner,segment_name
      ,case when segment_name like 'DR$%$%' then 'CTX INDEX' else segment_type end segment_type
      ,tablespace_name
      ,case 
         when segment_name like 'DR$%$%' 
           then (select table_owner||'.'||table_name from dba_indexes i where i.owner=s.owner and i.index_name = substr(segment_name,4,length(segment_name)-5))
         when segment_type in ('TABLE','TABLE PARTITION','TABLE SUBPARTITION')
            then owner||'.'||segment_name
         when segment_type in ('INDEX','INDEX PARTITION','INDEX SUBPARTITION')
            then (select i.table_owner||'.'||i.table_name from dba_indexes i where i.owner=s.owner and i.index_name=s.segment_name)
         when segment_type in ('LOBSEGMENT','LOB PARTITION','LOB SUBPARTITION')
            then (select l.owner||'.'||l.TABLE_NAME from dba_lobs l where l.segment_name = s.segment_name and l.owner = s.owner)
         when segment_type = 'LOBINDEX'
            then (select l.owner||'.'||l.TABLE_NAME from dba_lobs l where l.index_name = s.segment_name and l.owner = s.owner)
         when segment_type = 'NESTED TABLE'
            then (select nt.owner||'.'||nt.parent_table_name from dba_nested_tables nt where nt.owner=s.owner and nt.table_name=s.segment_name)
         when segment_type = 'CLUSTER'
            then (select min(owner||'.'||table_name) from dba_tables t where t.owner=s.owner and t.cluster_name=s.segment_name and rownum=1)
       end table_name
      ,blocks
      ,bytes
     from seg s
  )
 ,so as (
     select
       segs.owner
      ,substr(segs.table_name,instr(segs.table_name,'.')+1) TABLE_NAME
      ,sum(segs.bytes)  total_bytes
      ,sum(segs.blocks) total_blocks
      ,sum(case when segs.segment_type in ('TABLE','TABLE PARTITION','TABLE SUBPARTITION','NESTED TABLE','CLUSTER') then segs.bytes end) tab_size
      ,sum(case when segs.segment_type in ('INDEX','INDEX PARTITION','INDEX SUBPARTITION','CTX INDEX') then segs.bytes end) ind_size
      ,sum(case when segs.segment_type in ('CTX INDEX') then segs.bytes end) ctx_size
      ,sum(case when segs.segment_type in ('LOBSEGMENT','LOBINDEX','LOB PARTITION','LOB SUBPARTITION') then segs.bytes end) lob_size
     from segs
     group by owner,table_name
  )
 ,tops as (
     select 
           dense_rank()over (order by total_bytes desc) rnk
          ,so.*
     from so
  )
select *
from tops
where rnk<=50 -- top 50
/