原平均1762秒提升至0.035秒的TOP SQL优化处理过程

同事在日常巡检中发现一套数据库资源瓶颈严重,如下图所示:

活动会话多数在等待read by other session与CPU,根据硬件,多数为SQL问题导致,协助同事最终定位了TOP SQL,该SQL执行计划与执行效率信息如下图所示:

从执行计划中得知,最消耗资源的为TABLE ACCESS FULL全表扫描这步,该SQL有上百行,截取执行计划中此步SQL_TEXT如下:

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  2pzyb63zhr1qp, child number 0
-------------------------------------
SELECT *
FROM
  (SELECT iti.owner,
          iti.object_name,
          iti.object_type,
          iti.OBJECT_ID,
          iti.DATA_OBJECT_ID,
          iti.STATUS,
          iti.CREATED,
          iti.LAST_DDL_TIME,
          iti.SECONDARY,
          iti.NAMESPACE,
          iti.EDITION_NAME
   FROM t iti
   WHERE iti.OBJECT_ID > 88
     AND iti.object_type != 'INDEX'
   ORDER BY iti.CREATED DESC)
WHERE ROWNUM<=100

Plan hash value: 3299198703

------------------------------------------------------------------------------------------------------------------
| Id  | Operation               | Name | Starts | E-Rows | Cost (%CPU)| E-Time   | A-Rows |   A-Time   | Buffers |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT        |      |      1 |        |   657K(100)|          |    100 |00:00:07.90 |     263K|
|*  1 |  COUNT STOPKEY          |      |      1 |        |            |          |    100 |00:00:07.90 |     263K|
|   2 |   VIEW                  |      |      1 |     15M|   657K  (1)| 02:11:28 |    100 |00:00:07.90 |     263K|
|*  3 |    SORT ORDER BY STOPKEY|      |      1 |     15M|   657K  (1)| 02:11:28 |    100 |00:00:07.90 |     263K|
|*  4 |     TABLE ACCESS FULL   | T    |      1 |     15M| 71738   (1)| 00:14:21 |     17M|00:00:03.36 |     263K|
------------------------------------------------------------------------------------------------------------------

Query Block Name / Object Alias (identified by operation id):
-------------------------------------------------------------

   1 - SEL$1
   2 - SEL$2 / from$_subquery$_001@SEL$1
   3 - SEL$2
   4 - SEL$2 / iti@SEL$2

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

   1 - filter(ROWNUM<=100)
   3 - filter(ROWNUM<=100)

可以看到,该SQL用了232K个BUFFER,大约用了7秒多的时间。根据SQL逻辑,我们可以建立created,object_id两个字段索引,使得执行计划只扫描100行所需要的buffer,理想执行计划是走索引的INDEX FULL SCAN DESCENDING,A-ROWS只有100:

SQL> create index t_idx01 on t(created,object_id);

Index created.

再次测试SQL:

SQL>
SELECT *
FROM
  (SELECT iti.owner,
          iti.object_name,
          iti.object_type,
          iti.OBJECT_ID,
          iti.DATA_OBJECT_ID,
          iti.STATUS,
          iti.CREATED,
          iti.LAST_DDL_TIME,
          iti.SECONDARY,
          iti.NAMESPACE,
          iti.EDITION_NAME
   FROM t iti
   WHERE iti.OBJECT_ID > 88
     AND iti.object_type != 'INDEX'
   ORDER BY iti.CREATED DESC)
WHERE ROWNUM<=100


PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  2pzyb63zhr1qp, child number 0
-------------------------------------
SELECT *
FROM
  (SELECT iti.owner,
          iti.object_name,
          iti.object_type,
          iti.OBJECT_ID,
          iti.DATA_OBJECT_ID,
          iti.STATUS,
          iti.CREATED,
          iti.LAST_DDL_TIME,
          iti.SECONDARY,
          iti.NAMESPACE,
          iti.EDITION_NAME
   FROM t iti
   WHERE iti.OBJECT_ID > 88
     AND iti.object_type != 'INDEX'
   ORDER BY iti.CREATED DESC)
WHERE ROWNUM<=100

Plan hash value: 4274603649

------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                     | Name    | Starts | E-Rows | Cost (%CPU)| E-Time   | A-Rows |   A-Time   | Buffers | Reads  |
------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |         |      1 |        |     4 (100)|          |    100 |00:00:00.01 |     110 |      2 |
|*  1 |  COUNT STOPKEY                |         |      1 |        |            |          |    100 |00:00:00.01 |     110 |      2 |
|   2 |   VIEW                        |         |      1 |     15M|     4   (0)| 00:00:01 |    100 |00:00:00.01 |     110 |      2 |
|*  3 |    TABLE ACCESS BY INDEX ROWID| T       |      1 |     15M|     4   (0)| 00:00:01 |    100 |00:00:00.01 |     110 |      2 |
|*  4 |     INDEX FULL SCAN DESCENDING| T_IDX01 |      1 |      1 |     3   (0)| 00:00:01 |    100 |00:00:00.01 |      10 |      2 |
------------------------------------------------------------------------------------------------------------------------------------

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

   1 - filter(ROWNUM<=100)
   3 - filter("iti"."OBJECT_TYPE"<>'INDEX')
   4 - access("iti"."OBJECT_ID">88)
       filter("iti"."OBJECT_ID">88)

我们看到,执行计划自动选择了INDEX FULL SCAN DESCENDING,无需单独指定降序扫描,优化器会自动选择此方式;A-ROWS只有100行(需求是order by + 小于等于100 row),相对于非全表扫描后过滤出100行的方式,BUFFER GET只需要110个即可。

在生产库上创建索引后性能对比:

最终执行计划如下:


-----------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                         | Name                    | Starts | E-Rows | Cost (%CPU)| E-Time   | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                  |                         |      1 |        |  1181 (100)|          |    104 |00:00:00.02 |    2476 |
|*  1 |  FILTER                           |                         |      1 |        |            |          |    104 |00:00:00.02 |    2476 |
|   2 |   NESTED LOOPS                    |                         |      1 |    496 |  1181   (1)| 00:00:15 |    508 |00:00:00.01 |    1542 |
|   3 |    NESTED LOOPS                   |                         |      1 |     89 |   673   (0)| 00:00:09 |    100 |00:00:00.01 |     963 |
|   4 |     VIEW                          |                         |      1 |    100 |    94   (0)| 00:00:02 |    100 |00:00:00.01 |     401 |
|*  5 |      COUNT STOPKEY                |                         |      1 |        |            |          |    100 |00:00:00.01 |     401 |
|   6 |       VIEW                        |                         |      1 |    100 |    94   (0)| 00:00:02 |    100 |00:00:00.01 |     401 |
|*  7 |        TABLE ACCESS BY INDEX ROWID| ITINE                   |      1 |     22M|    94   (0)| 00:00:02 |    100 |00:00:00.01 |     401 |
|*  8 |         INDEX SKIP SCAN DESCENDING| ITIN_IDX_01             |      1 |    104 |     3   (0)| 00:00:01 |    100 |00:00:00.01 |     301 |
|*  9 |     TABLE ACCESS BY INDEX ROWID   | SPNR                    |    100 |      1 |     6   (0)| 00:00:01 |    100 |00:00:00.01 |     562 |
|* 10 |      INDEX RANGE SCAN             | SPNR_PK                 |    100 |      9 |     3   (0)| 00:00:01 |    960 |00:00:00.01 |     291 |
|* 11 |    TABLE ACCESS BY INDEX ROWID    | ITINE                   |    100 |      6 |     6   (0)| 00:00:01 |    508 |00:00:00.01 |     579 |
|* 12 |     INDEX RANGE SCAN              | ITINE_PK                |    100 |      6 |     3   (0)| 00:00:01 |    508 |00:00:00.01 |     271 |
|* 13 |   TABLE ACCESS BY INDEX ROWID     | ITINE                   |    404 |      1 |     4   (0)| 00:00:01 |      0 |00:00:00.01 |     934 |
|* 14 |    INDEX UNIQUE SCAN              | ITINE_PK                |    404 |      1 |     3   (0)| 00:00:01 |    404 |00:00:00.01 |     530 |
-----------------------------------------------------------------------------------------------------------------------------------------------

执行效率高但“低效”的SQL优化

近期整理案例时发现,同事曾咨询过一个问题,数据库服务器CPU频繁报警,同事最终定位到一个TOP SQL,但是执行效率已经”算不错“,是否还有优化的余地?

SQL_TEXT如下:

SELECT DISTINCT S.*,
                BD.*,
                SD.*,
                UD.*
FROM T S
LEFT JOIN T1 BD ON S.DATA_OBJECT_ID = BD.DATA_OBJECT_ID
AND S.OBJECT_ID = BD.OBJECT_ID
LEFT JOIN T2 SD ON S.DATA_OBJECT_ID = SD.DATA_OBJECT_ID
AND S.OBJECT_ID = SD.OBJECT_ID
LEFT JOIN T3 UD ON S.DATA_OBJECT_ID = UD.DATA_OBJECT_ID
AND S.OBJECT_ID = UD.OBJECT_ID
WHERE S.OBJECT_ID = 88
  AND 1 = 1

原执行计划如下:
Plan hash value: 1035031963

----------------------------------------------------------------------------------------------------------------
| Id  | Operation             | Name | Starts | E-Rows | Cost (%CPU)| E-Time   | A-Rows |   A-Time   | Buffers |
----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |      |      1 |        | 84081 (100)|          |      1 |00:00:00.60 |     309K|
|   1 |  HASH UNIQUE          |      |      1 |      1 | 84081   (1)| 00:16:49 |      1 |00:00:00.60 |     309K|
|*  2 |   HASH JOIN OUTER     |      |      1 |      1 | 84080   (1)| 00:16:49 |      1 |00:00:00.60 |     309K|
|*  3 |    HASH JOIN OUTER    |      |      1 |      1 | 79935   (1)| 00:16:00 |      1 |00:00:00.55 |     294K|
|*  4 |     HASH JOIN OUTER   |      |      1 |      1 | 75791   (1)| 00:15:10 |      1 |00:00:00.50 |     278K|
|*  5 |      TABLE ACCESS FULL| T    |      1 |      1 | 71647   (1)| 00:14:20 |      1 |00:00:00.44 |     263K|
|*  6 |      TABLE ACCESS FULL| T3   |      1 |    168 |  4144   (1)| 00:00:50 |      1 |00:00:00.05 |   15260 |
|*  7 |     TABLE ACCESS FULL | T2   |      1 |    168 |  4144   (1)| 00:00:50 |      1 |00:00:00.05 |   15260 |
|*  8 |    TABLE ACCESS FULL  | T1   |      1 |    168 |  4145   (1)| 00:00:50 |      1 |00:00:00.05 |   15260 |
----------------------------------------------------------------------------------------------------------------

 

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

   2 - access("S"."OBJECT_ID"="BD"."OBJECT_ID" AND "S"."DATA_OBJECT_ID"="BD"."DATA_OBJECT_ID")
   3 - access("S"."OBJECT_ID"="SD"."OBJECT_ID" AND "S"."DATA_OBJECT_ID"="SD"."DATA_OBJECT_ID")
   4 - access("S"."OBJECT_ID"="UD"."OBJECT_ID" AND "S"."DATA_OBJECT_ID"="UD"."DATA_OBJECT_ID")
   5 - filter("S"."OBJECT_ID"=88)
   6 - filter("UD"."OBJECT_ID"=88)
   7 - filter("SD"."OBJECT_ID"=88)
   8 - filter("BD"."OBJECT_ID"=88)

SQL涉及四张表T,T1,T2,T3,数据量均在100万+。OBJECT_ID选择率非常好(相当于主键),因为缺乏多个索引,执行计划选择全表扫描+HASH_JOIN的方式针对此SQL是严重低效的,即使执行效率还能接受,平均单次执行时间需要0.237秒,但是309K的BUFFER GET。SQL执行效率高不等于一定是高效率的。

优化建议:T表谓词创建索引,返回行数少(驱动表),剩余表连接列上创建索引(被驱动表),使得SQL走Nested Loop关联方式,小结果集驱动大结果集,最终只需要4000多个BUFFER即可。

模拟案例SQL:

create index t_idx01 on t(object_id);
create index t1_idx01 on t1(data_object_id);
create index t2_idx01 on t2(data_object_id);
create index t3_idx01 on t3(data_object_id);

Plan hash value: 3722872203

------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation			| Name	   | Starts | E-Rows | Cost (%CPU)| E-Time   | A-Rows |   A-Time   | Buffers |
------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT		|	   |	  1 |	     |	 782 (100)|	     |	    1 |00:00:00.01 |	4018 |
|   1 |  HASH UNIQUE			|	   |	  1 |	   1 |	 782   (1)| 00:00:10 |	    1 |00:00:00.01 |	4018 |
|   2 |   NESTED LOOPS OUTER		|	   |	  1 |	   1 |	 781   (0)| 00:00:10 |	    1 |00:00:00.01 |	4018 |
|   3 |    NESTED LOOPS OUTER		|	   |	  1 |	   1 |	 522   (0)| 00:00:07 |	    1 |00:00:00.01 |	2680 |
|   4 |     NESTED LOOPS OUTER		|	   |	  1 |	   1 |	 263   (0)| 00:00:04 |	    1 |00:00:00.01 |	1342 |
|   5 |      TABLE ACCESS BY INDEX ROWID| T	   |	  1 |	   1 |	   4   (0)| 00:00:01 |	    1 |00:00:00.01 |	   4 |
|*  6 |       INDEX RANGE SCAN		| T_IDX01  |	  1 |	   1 |	   3   (0)| 00:00:01 |	    1 |00:00:00.01 |	   3 |
|*  7 |      TABLE ACCESS BY INDEX ROWID| T1	   |	  1 |	   1 |	 259   (0)| 00:00:04 |	    1 |00:00:00.01 |	1338 |
|*  8 |       INDEX RANGE SCAN		| T1_IDX01 |	  1 |	 258 |	   2   (0)| 00:00:01 |	 4608 |00:00:00.01 |	  12 |
|*  9 |     TABLE ACCESS BY INDEX ROWID | T2	   |	  1 |	   1 |	 259   (0)| 00:00:04 |	    1 |00:00:00.01 |	1338 |
|* 10 |      INDEX RANGE SCAN		| T2_IDX01 |	  1 |	 258 |	   2   (0)| 00:00:01 |	 4608 |00:00:00.01 |	  12 |
|* 11 |    TABLE ACCESS BY INDEX ROWID	| T3	   |	  1 |	   1 |	 259   (0)| 00:00:04 |	    1 |00:00:00.01 |	1338 |
|* 12 |     INDEX RANGE SCAN		| T3_IDX01 |	  1 |	 258 |	   2   (0)| 00:00:01 |	 4608 |00:00:00.01 |	  12 |
------------------------------------------------------------------------------------------------------------------------------

很多时候,执行效率浪费资源严重的TOP SQL往往被日益逐渐增强的系统资源所掩盖,记得一位前辈说过的话,TOP SQL不要看单纯的执行效率,要看所谓的”TOP”来综合判断是否有优化的余地,例如参考平均buffer get与平均返回行数等其他SQL性能指标,是否是合适,还是“血亏”。

ROW CACHE LOCK导致数据库CPU高案例分析

近期整理案例,发现前不久同事反馈一个数据库服务器CPU居高不下,无法找到根因过来询问,详情如下:

EVENT                                      COUNT(*)
---------------------------------------- ----------
row cache lock                                 1007
                                                213
buffer busy waits                               165
log file switch (checkpoint incomplete)         137
free buffer waits                               114
db file async I/O submit                         75
log file sync                                    73
db file sequential read                          62
read by other session                            36
log file parallel write                          15
resmgr:cpu quantum                               15
enq: TX - row lock contention                    13
enq: HW - contention                              9
direct path read                                  8
enq: SQ - contention                              4
latch: undo global data                           3
write complete waits                              2
log file sequential read                          1
null event                                        1
db file scattered read                            1
Log archive I/O                                   1
LNS wait on SENDREQ                               1
enq: US - contention                              1

从ASH中看到,CPU高的时间段内,等待事件最多的为ROW CACHE LOCK,

TIME                              USER_ID SQL_ID        EVENT                                            P1
------------------------------ ---------- ------------- ---------------------------------------- ----------
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 c25ar03n0p5sz row cache lock                                   13
20200929 01:11:25                      91 c25ar03n0p5sz row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 c25ar03n0p5sz row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 c25ar03n0p5sz row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 11xs00r4qzb7n row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13
20200929 01:11:25                      91 7265mcg5kwyc3 row cache lock                                   13

确认该时间段内的具体SQL,发现均存在SEQUENCE调用

SQL_ID          COUNT(*)
------------- ----------
7265mcg5kwyc3        357
11xs00r4qzb7n        341
c25ar03n0p5sz        277
f9ym730zzf1s2         12
9dnygr7asqmy9         12
dksd9txy8vuk6          6
9qzwmgp034kx2          2

SQL> SELECT DISTINCT sql_id,
                sql_text
FROM v$sql
WHERE sql_id IN ('dksd9txy8vuk6',
                 '11xs00r4qzb7n',
                 '9qzwmgp034kx2',
                 '9dnygr7asqmy9',
                 '7265mcg5kwyc3',
                 'c25ar03n0p5sz',
                 'f9ym730zzf1s2');

SQL_ID        SQL_TEXT
------------- ------------------------------------------------------------------------------------------------------------------------
9qzwmgp034kx2 INSERT INTO opfqresultdata VALUES(OPFQRESULTDATA_SEQUENCE_ID.Nextval,:1 ,:2 ,:3 ,:4 ,:5 ,:6 ,:7 ,:8 )
f9ym730zzf1s2 select SUBSFLT_HISTORY_SEQUENCE_ID.nextval from dual
7265mcg5kwyc3 INSERT INTO pnrresultdata VALUES(PNRRESULTDATA_SEQUENCE_ID.NEXTVAL,:1 ,:2 ,:3 ,:4 ,:5 ,:6 ,:7 ,:8 ,:9 )
c25ar03n0p5sz INSERT INTO etresultdata VALUES(ETRESULTDATA_SEQUENCE_ID.Nextval,:1 ,:2 ,:3 ,:4 ,:5 ,:6 ,:7 ,:8 ,:9 ,:10 ,:11 )
9dnygr7asqmy9 select SUBSFLTREQUEST_SEQUENCE_ID.nextval from dual
11xs00r4qzb7n INSERT INTO unknowdata VALUES(UNKNOWDATA_SEQUENCE_ID.Nextval,:1 ,:2 ,:3 )
dksd9txy8vuk6 INSERT INTO fltresultdata VALUES(FLTRESULTDATA_SEQUENCE_ID.Nextval,:1 ,:2 ,:3 ,:4 ,:5 ,:6 ,:7 ,:8 ,:9 ,:10 ,:11 ,:12 ,:1

SQL> select DISTINCT P1
FROM
  (SELECT to_char(SAMPLE_TIME,'YYYYMMDD hh24:mi:ss') time,
          USER_ID,
          SQL_ID,
          EVENT,
          P1
   FROM dba_hist_active_sess_history
   WHERE SAMPLE_TIME>to_date('20200929 01:00:00','YYYYMMDD hh24:mi:ss')
     AND SAMPLE_TIME<to_date('20200929 01:15:00','YYYYMMDD hh24:mi:ss')
     AND EVENT = 'row cache lock');


        P1
----------
        13  --正是dc_sequences

SQL> select parameter,gets,getmisses,MODIFICATIONS from v$rowcache where cache#=13;

PARAMETER                              GETS  GETMISSES MODIFICATIONS
-------------------------------- ---------- ---------- -------------
dc_sequences                      766929021       5065     766929020

发现此sequence中的cache size均为0,出现row cache lock与cpu高便不足为奇

将频繁调用的sequence cache size调整至500后,等待与cpu高的现象随之解决,不再复现。

SQL执行计划突变案例分析

前几日,同事发现一套库数据库的活动会话数激增,大量活动会话集中等待gc buffer busy acquire上,如下图所示

经过更一步的查看,发现这些等待均在sql_id:f7hnmtx8bd5q3上,利用如下sql进行查看发现:

SQL_TEXT如下:
SELECT *
FROM
  (SELECT p.*
   FROM t p,
        TD_PLAN_LEG d
   WHERE p.THR_DEP = d.THR_DEP
     AND p.THR_ARR = d.THR_ARR
     AND p.FLTNO = d._FLTNO
     AND pDATE = d.DATE
     AND (p.THR_DEP = :1
          OR p.THR_ARR = :2)
     AND nvl(d.STATUS_inner, ' ') != '??'
     AND d.LT_ID IS NULL
     AND nvl(d.STATUS_inner, ' ') != '??'
     AND p.OP_TM > :3
   ORDER BY p.OP_TM)
WHERE ROWNUM <= :4


SQL历史执行效率:
TIME      PLAN_HASH_VALUE EXECU_D        BG_D        DR_D      ET_D      CT_D IO_TIME CLUS_TIME AP_TIME CC_TIME GET_ONETIME   ROWS_PRO ROWS_ONETIME ET_MS_ONCE
------------ --------------- ------- ----------- ----------- --------- --------- ------- --------- ------- ------- ----------- ---------- ------------ ----------
2020092201        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092201        2495209816    3333  1351590946           0      2403      2409       0         0       0       0      405518        261            0        721
2020092202        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092202        2495209816    3238  1381884655           0      2449      2477       0         0       0       0      426771         82            0        756
2020092203        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092203        2495209816    3228  1380940555           0      2464      2475       0         0       0       0      427801         12            0        763
2020092204        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092204        2495209816    3249           0           0      2469      2486       0         0       0       0           0          0            0        760
2020092205        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092205        2495209816    3239  1401077673           0      2496      2510       0         0       0       0      432565          0            0        771
2020092206        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092206        2495209816    3240  1374013765           0      2449      2467       0         0       0       0      424078          0            0        756
2020092207        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092207        2495209816    3319           0           0      2584      2589       0         0       0       0           0        181            0        778
2020092208        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092208        2495209816    3656  1628720696           0      2827      2826       0         0       0       0      445493       1439            0        773
2020092209        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
2020092209        2495209816    3238  1434887002           0      2610      2611       0         0       0       0      443140       2253            1        806
2020092210        1636324798       0           0           0         0         0       0         0       0       0           0          0            0          0
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2020093009        1636324798     291  1070234826    36419287     65024      9485    4331     46952       0      42     3677783       3606           12     223449
2020093009        2495209816       0           0           0         0         0       0         0       0       0           0          0            0          0
2020093010        1636324798     297  1060264318    35236356     65779      9293    4500     47678       0      43     3569914       3473           12     221476
2020093010        2495209816       0           0           0         0         0       0         0       0       0           0          0            0          0

404 rows selected.

从DBA_HIST_SQLSTAT中看到,该SQL有两个执行计划:

PLAH_HASH_VALUE:(1) 1636324798 (2)2495209816其中2495209816效率相对较高,1636324798一直存在但是在9月29日之前并没有启用过,一直在用2495209816,直到2020092902(2点)开始启用,并且在2020092903(3点)完全使用。但是ORACLE为何突然使用了差的执行计划了?


Inst: 1   Child: 0    Plan hash value: 2495209816

                      --------------------------------------------------------------------------------------------------------------------------------------------
                      | Id  | Operation                       | Name                         | E-Rows |E-Bytes| Cost (%CPU)| E-Time   |  OMem |  1Mem | Used-Mem |
                      --------------------------------------------------------------------------------------------------------------------------------------------
                      |   0 | SELECT STATEMENT                |                              |        |       |   372K(100)|          |       |       |          |
                      |*  1 |  COUNT STOPKEY                  |                              |        |       |            |          |       |       |          |
                      |   2 |   VIEW                          |                              |      1 |   176 |   372K  (1)| 01:14:32 |       |       |          |
                      |*  3 |    SORT ORDER BY STOPKEY        |                              |      1 |   125 |   372K  (1)| 01:14:32 |  1024 |  1024 |          |
                      |   4 |     NESTED LOOPS                |                              |      1 |   125 |   372K  (1)| 01:14:32 |       |       |          |
                      |   5 |      NESTED LOOPS               |                              |    120K|   125 |   372K  (1)| 01:14:32 |       |       |          |
                      |*  6 |       TABLE ACCESS FULL         | T1                           |    120K|  4482K| 10211   (1)| 00:02:03 |       |       |          |
                      |*  7 |       INDEX RANGE SCAN          | T_POSITIONS_KEY              |      1 |       |     3   (0)| 00:00:01 |       |       |          |
                      |*  8 |      TABLE ACCESS BY INDEX ROWID| T                            |      1 |    87 |     3   (0)| 00:00:01 |       |       |          |
                      --------------------------------------------------------------------------------------------------------------------------------------------

Inst: 1   Child: 38   Plan hash value: 1636324798

                      ----------------------------------------------------------------------------------------------------------------------------------
                      | Id  | Operation               | Name               | E-Rows |E-Bytes|E-Temp | Cost (%CPU)| E-Time   |  OMem |  1Mem | Used-Mem |
                      ----------------------------------------------------------------------------------------------------------------------------------
                      |   0 | SELECT STATEMENT        |                    |        |       |       |  1039K(100)|          |       |       |          |
                      |*  1 |  COUNT STOPKEY          |                    |        |       |       |            |          |       |       |          |
                      |   2 |   VIEW                  |                    |    118K|    19M|       |  1039K  (1)| 03:27:50 |       |       |          |
                      |*  3 |    SORT ORDER BY STOPKEY|                    |    118K|    14M|    16M|  1039K  (1)| 03:27:50 |  1024 |  1024 |          |
                      |*  4 |     HASH JOIN           |                    |    118K|    14M|  6024K|  1035K  (1)| 03:27:10 |  9516K|  3276K|   10M (0)|
                      |*  5 |      TABLE ACCESS FULL  | T1                 |    123K|  4576K|       | 10211   (1)| 00:02:03 |       |       |          |
                      |*  6 |      TABLE ACCESS FULL  | T                  |    118K|     9M|       |  1024K  (1)| 03:24:57 |       |       |          |
                      ----------------------------------------------------------------------------------------------------------------------------------

经过分析发现,此SQL涉及对象在9月28日时触发了统计信息收集,并且因此游标滚动失效,9月29日零时进行了硬解析。因数据库中绑定变量窥探关闭(_optim_peek_user_binds,被设定为FALSE),无法”窥视“直方图,根据数据分布与bind值分析,因数据倾斜严重+关闭“窥探直方图”情况下被认为平均分布,优化器选择了选择了”CBO认为效率高,实际效率差”的执行计划。针对此情形,可以利用SQL_PROFILE进行绑定(借助coe_load_sql_profile.sql)脚本绑定后

数据库GC类等待消失,SQL效率恢复至毫秒级别。