一般在生产环境中，如果某个查询中涉及一个大表，走索引扫描是显然是最值得推荐的方式，但是索引扫描有unique index scan, range scan,skip scan, full scan, fast full scan,这些索引扫描看起来好像很繁杂，但是如果掌握得当，却能够在索引扫描的基础上极速提升性能。关于索引扫描的方式，可以参考。http://blog.itpub.net/23718752/viewspace-1335358/
关于索引的使用模式
首先来看看这个问题。
开发反应这周有一个process处理数据特别慢，有很多的业务处理都受到了影响，想让我看看在数据库级别能够发现什么。
从这个反馈来说，可能数据库中是有锁了，或者是存在着一些异常的进程消耗了较多的资源，要不就是sql语句的问题。因为这个库已经运行很长时间了。涉及到的开发变更还是比较少的。所以就先查看了数据库的负载。
BEGIN_TIME------------------------- END_TIME--------------------------- ELAPSED_TIME- BTIME----- WORKLOAD_PER--------
----------------------------------- ----------------------------------- ------------- ---------- --------------------
12360 ** 11-DEC-14 01.00.06.432 AM 12361 ** 11-DEC-14 02.00.08.531 AM 60.035 103.07 171%
12361 ** 11-DEC-14 02.00.08.531 AM 12362 ** 11-DEC-14 03.00.11.099 AM 60.043 105.13 175%
12362 ** 11-DEC-14 03.00.11.099 AM 12363 ** 11-DEC-14 04.00.13.507 AM 60.040 148.71 247%
12363 ** 11-DEC-14 04.00.13.507 AM 12364 ** 11-DEC-14 05.00.17.328 AM 60.064 169.35 281%
12364 ** 11-DEC-14 05.00.17.328 AM 12365 ** 11-DEC-14 06.00.20.742 AM 60.057 89.84 149%
12365 ** 11-DEC-14 06.00.20.742 AM 12366 ** 11-DEC-14 07.00.23.766 AM 60.050 89.49 149%
12366 ** 11-DEC-14 07.00.23.766 AM 12367 ** 11-DEC-14 08.00.25.956 AM 60.037 113.92 189%
12367 ** 11-DEC-14 08.00.25.956 AM 12368 ** 11-DEC-14 09.00.28.480 AM 60.042 92.33 153%
12368 ** 11-DEC-14 09.00.28.480 AM 12369 ** 11-DEC-14 10.00.31.163 AM 60.045 180.66 300%
12369 ** 11-DEC-14 10.00.31.163 AM 12370 ** 11-DEC-14 11.00.34.040 AM 60.048 204.65 340%
12370 ** 11-DEC-14 11.00.34.040 AM 12371 ** 11-DEC-14 12.00.37.255 PM 60.054 361.93 602%
12371 ** 11-DEC-14 12.00.37.255 PM 12372 ** 11-DEC-14 01.00.40.741 PM 60.058 400.98 667%
12372 ** 11-DEC-14 01.00.40.741 PM 12373 ** 11-DEC-14 02.00.43.680 PM 60.049 225.84 376%
12373 ** 11-DEC-14 02.00.43.680 PM 12374 ** 11-DEC-14 03.00.46.353 PM 60.045 220.51 367%
12374 ** 11-DEC-14 03.00.46.353 PM 12375 ** 11-DEC-14 04.00.48.809 PM 60.041 276.56 460%
12375 ** 11-DEC-14 04.00.48.809 PM 12376 ** 11-DEC-14 05.00.51.411 PM 60.043 204.22 340%
从整体来看，负载还是可以接受的。
然后查看锁的情况，也没有发现什么延迟的锁等待。这个时候锁等待导致的延迟可能也排除了。
这个时候抓一个awr报告看看细节。
从load profile来看，cpu使用率不高。相对来说,logical reads较高。
等待事件的情况如下。
直接进入sql语句的部分。
这个时候可以很明显的看到sql语句
05g6ywz7311f6占用了83%的比例。可以看到每条语句的执行时间在0.52秒左右。看起来还是可以的，但是从报表中来看，这条语句的执行频率很高。
对应的sql语句如下：
SELECT LAST_THRESHOLD, CYCLE_MONTH, CYCLE_YEAR
FROM CRDT_LMT_NOTIFICATION
WHERE ITEM_ID = :a
AND AGREEMENT_ID = :a
AND CYCLE_CODE = :a
AND OFFER_INSTANCE = :a
AND CUSTOMER_ID = :a
AND (TO_CHAR(CYCLE_YEAR, '9999') || TO_CHAR(CYCLE_MONTH, '09')) =
(SELECT MAX(TO_CHAR(CYCLE_YEAR, '9999') || TO_CHAR(CYCLE_MONTH, '09')) --这个语句的关键就在于标黄的部分,这条语句是想得到cycle_year,cycle_month最高的值，把year,month拼接成20141209这样的形式
FROM CRDT_LMT_NOTIFICATION
WHERE ITEM_ID = :a
AND AGREEMENT_ID = :a
AND CYCLE_CODE = :a
AND OFFER_INSTANCE = :a
AND CUSTOMER_ID = :a)
对应的索引如下：
INDEX_NAME TABLESPACE INDEX_TYPE UNIQUENES PAR COLUMN_LIST TABLE_TYPE STATUS NUM_ROWS LAST_ANAL G
------------------------------ ---------- ---------- --------- --- ------------------------------ ---------- ------ ---------- --------- - -------------------- ---------- ------ ---------- --------- --------------------- ---------- ------ ---------- --------- --------------------- --------
CRDT_LMT_NOTIFICATION_PK NORMAL UNIQUE YES CYCLE_CODE,CYCLE_MONTH,CYCLE_Y EAR,CUSTOMER_ID,AGREEMENT_ID,OFFER_INSTANCE,ITEM_ID TABLE N/A 5457339 03-DEC-14 N
查看对应的执行计划。
-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 | 74 | 4281 (1)| 00:00:52 | | |
| 1 | PARTITION RANGE ITERATOR | | 2 | 74 | 2141 (1)| 00:00:26 | KEY | KEY |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID| CRDT_LMT_NOTIFICATION | 2 | 74 | 2141 (1)| 00:00:26 | KEY | KEY |
|* 3 | INDEX RANGE SCAN | CRDT_LMT_NOTIFICATION_PK | 1 | | 2141 (1)| 00:00:26 | KEY | KEY |
| 4 | SORT AGGREGATE | | 1 | 34 | | | | |
| 5 | PARTITION RANGE ITERATOR | | 7 | 238 | 2141 (1)| 00:00:26 | KEY | KEY |
|* 6 | INDEX RANGE SCAN | CRDT_LMT_NOTIFICATION_PK | 7 | 238 | 2141 (1)| 00:00:26 | KEY | KEY |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A AND
TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')= (SELECT /*+ PUSH_SUBQ OPT_ESTIMATE (TABLE
"CRDT_LMT_NOTIFICATION" SCALE_ROWS=1016.803110 ) OPT_ESTIMATE (INDEX_FILTER "CRDT_LMT_NOTIFICATION"
"CRDT_LMT_NOTIFICATION_PK" SCALE_ROWS=440.696164 ) INDEX ("CRDT_LMT_NOTIFICATION" "CRDT_LMT_NOTIFICATION_PK")
*/ MAX(TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')) FROM "CRDT_LMT_NOTIFICATION"
"CRDT_LMT_NOTIFICATION" WHERE "CYCLE_CODE"=:A AND "OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND
"ITEM_ID"=:A))
6 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A)
我们使用sql_profile来看看调优的建议。这里的不同之处是原本的range scan变成了skip scan. 资源消耗一下子小了几十倍。
-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 | 74 | 6 (67)| 00:00:01 | | |
| 1 | PARTITION RANGE ITERATOR | | 2 | 74 | 3 (67)| 00:00:01 | KEY | KEY |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID| CRDT_LMT_NOTIFICATION_PK | 2 | 74 | 3 (67)| 00:00:01 | KEY | KEY |
|* 3 | INDEX SKIP SCAN | CRDT_LMT_NOTIFICATION_PK_PK | 1 | | 3 (67)| 00:00:01 | KEY | KEY |
| 4 | SORT AGGREGATE | | 1 | 34 | | | | |
| 5 | PARTITION RANGE ITERATOR | | 7 | 238 | 3 (67)| 00:00:01 | KEY | KEY |
|* 6 | INDEX SKIP SCAN | CRDT_LMT_NOTIFICATION_PK_PK | 7 | 238 | 3 (67)| 00:00:01 | KEY | KEY |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A AND
TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')= (SELECT /*+ OPT_ESTIMATE (TABLE "CRDT_LMT_NOTIFICATION_PK"
SCALE_ROWS=1016.803110 ) OPT_ESTIMATE (INDEX_FILTER "CRDT_LMT_NOTIFICATION_PK" "CRDT_LMT_NOTIFICATION_PK_PK"
SCALE_ROWS=440.696164 ) */ MAX(TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')) FROM
"PRDUSG3O"."CRDT_LMT_NOTIFICATION_PK" "CRDT_LMT_NOTIFICATION_PK" WHERE "CYCLE_CODE"=:A AND "OFFER_INSTANCE"=:A AND
"AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A))
6 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A)
在和开发确认之后，这条语句是关键的语句，是在一个新开发的需求中新加的。因为情况紧急，压力一下子堆在了我身上，大家希望我来对这条语句进行调优，能从0.5秒进行更高效的调优。
今天章节开篇先来介绍一下问题的情况，明天来详细的分析一下处理的思路，各种方案的对比和最终的建议。
首先来看看这个问题。
开发反应这周有一个process处理数据特别慢，有很多的业务处理都受到了影响，想让我看看在数据库级别能够发现什么。
从这个反馈来说，可能数据库中是有锁了，或者是存在着一些异常的进程消耗了较多的资源，要不就是sql语句的问题。因为这个库已经运行很长时间了。涉及到的开发变更还是比较少的。所以就先查看了数据库的负载。
BEGIN_TIME------------------------- END_TIME--------------------------- ELAPSED_TIME- BTIME----- WORKLOAD_PER--------
----------------------------------- ----------------------------------- ------------- ---------- --------------------
12360 ** 11-DEC-14 01.00.06.432 AM 12361 ** 11-DEC-14 02.00.08.531 AM 60.035 103.07 171%
12361 ** 11-DEC-14 02.00.08.531 AM 12362 ** 11-DEC-14 03.00.11.099 AM 60.043 105.13 175%
12362 ** 11-DEC-14 03.00.11.099 AM 12363 ** 11-DEC-14 04.00.13.507 AM 60.040 148.71 247%
12363 ** 11-DEC-14 04.00.13.507 AM 12364 ** 11-DEC-14 05.00.17.328 AM 60.064 169.35 281%
12364 ** 11-DEC-14 05.00.17.328 AM 12365 ** 11-DEC-14 06.00.20.742 AM 60.057 89.84 149%
12365 ** 11-DEC-14 06.00.20.742 AM 12366 ** 11-DEC-14 07.00.23.766 AM 60.050 89.49 149%
12366 ** 11-DEC-14 07.00.23.766 AM 12367 ** 11-DEC-14 08.00.25.956 AM 60.037 113.92 189%
12367 ** 11-DEC-14 08.00.25.956 AM 12368 ** 11-DEC-14 09.00.28.480 AM 60.042 92.33 153%
12368 ** 11-DEC-14 09.00.28.480 AM 12369 ** 11-DEC-14 10.00.31.163 AM 60.045 180.66 300%
12369 ** 11-DEC-14 10.00.31.163 AM 12370 ** 11-DEC-14 11.00.34.040 AM 60.048 204.65 340%
12370 ** 11-DEC-14 11.00.34.040 AM 12371 ** 11-DEC-14 12.00.37.255 PM 60.054 361.93 602%
12371 ** 11-DEC-14 12.00.37.255 PM 12372 ** 11-DEC-14 01.00.40.741 PM 60.058 400.98 667%
12372 ** 11-DEC-14 01.00.40.741 PM 12373 ** 11-DEC-14 02.00.43.680 PM 60.049 225.84 376%
12373 ** 11-DEC-14 02.00.43.680 PM 12374 ** 11-DEC-14 03.00.46.353 PM 60.045 220.51 367%
12374 ** 11-DEC-14 03.00.46.353 PM 12375 ** 11-DEC-14 04.00.48.809 PM 60.041 276.56 460%
12375 ** 11-DEC-14 04.00.48.809 PM 12376 ** 11-DEC-14 05.00.51.411 PM 60.043 204.22 340%
从整体来看，负载还是可以接受的。
然后查看锁的情况，也没有发现什么延迟的锁等待。这个时候锁等待导致的延迟可能也排除了。
这个时候抓一个awr报告看看细节。
| Snap Id | Snap Time | Sessions | Cursors/Session | |
|---|---|---|---|---|
| Begin Snap: | 12314 | 09-Dec-14 03:00:07 | 253 | 4.4 |
| End Snap: | 12315 | 09-Dec-14 04:00:09 | 248 | 4.5 |
| Elapsed: | 60.04 (mins) | |||
| DB Time: | 86.64 (mins) |
| Per Second | Per Transaction | Per Exec | Per Call | |
|---|---|---|---|---|
| DB Time(s): | 1.4 | 1.4 | 0.00 | 0.00 |
| DB CPU(s): | 1.4 | 1.4 | 0.00 | 0.00 |
| Redo size: | 81,566.1 | 77,546.7 | ||
| Logical reads: | 121,122.2 | 115,153.6 | ||
| Block changes: | 393.3 | 373.9 | ||
| Physical reads: | 9.7 | 9.2 | ||
| Physical writes: | 16.6 | 15.8 | ||
| User calls: | 534.7 | 508.4 | ||
| Parses: | 3.8 | 3.6 | ||
| Hard parses: | 0.1 | 0.1 | ||
| W/A MB processed: | 0.1 | 0.1 | ||
| Logons: | 0.1 | 0.1 | ||
| Executes: | 291.1 | 276.8 | ||
| Rollbacks: | 0.0 | 0.0 | ||
| Transactions: | 1.1 |
| Event | Waits | Time(s) | Avg wait (ms) | % DB time | Wait Class |
|---|---|---|---|---|---|
| DB CPU | 5,124 | 98.56 | |||
| db file sequential read | 34,433 | 65 | 2 | 1.24 | User I/O |
| log file sync | 3,515 | 16 | 5 | 0.30 | Commit |
| control file sequential read | 34,785 | 10 | 0 | 0.20 | System I/O |
| SQL*Net message to client | 1,751,834 | 1 | 0 | 0.03 | Network |
| Elapsed Time (s) | Executions | Elapsed Time per Exec (s) | %Total | %CPU | %IO | SQL Id | SQL Module | SQL Text |
|---|---|---|---|---|---|---|---|---|
| 4,352.98 | 8,375 | 0.52 | 83.73 | 99.95 | 0.01 | 05g6ywz7311f6 | m1EnvelopeMT@ccbdbpr5 (TNS V1-V3) | /* */ SELECT LAST_THRESHOLD, C... |
| 250.14 | 4,942 | 0.05 | 4.81 | 99.94 | 0.00 | 06pyusmmaz7bn | m1EnvelopeMT@ccbdbpr5 (TNS V1-V3) | /* */ select count (1) from RP... |
| 158.68 | 4,810 | 0.03 | 3.05 | 99.89 | 0.00 | d59u1a0r9xa9c | m1EnvelopeMT@ccbdbpr5 (TNS V1-V3) | /* */ SELECT IDENTIFIER FROM (... |
对应的sql语句如下：
SELECT LAST_THRESHOLD, CYCLE_MONTH, CYCLE_YEAR
FROM CRDT_LMT_NOTIFICATION
WHERE ITEM_ID = :a
AND AGREEMENT_ID = :a
AND CYCLE_CODE = :a
AND OFFER_INSTANCE = :a
AND CUSTOMER_ID = :a
AND (TO_CHAR(CYCLE_YEAR, '9999') || TO_CHAR(CYCLE_MONTH, '09')) =
(SELECT MAX(TO_CHAR(CYCLE_YEAR, '9999') || TO_CHAR(CYCLE_MONTH, '09')) --这个语句的关键就在于标黄的部分,这条语句是想得到cycle_year,cycle_month最高的值，把year,month拼接成20141209这样的形式
FROM CRDT_LMT_NOTIFICATION
WHERE ITEM_ID = :a
AND AGREEMENT_ID = :a
AND CYCLE_CODE = :a
AND OFFER_INSTANCE = :a
AND CUSTOMER_ID = :a)
对应的索引如下：
INDEX_NAME TABLESPACE INDEX_TYPE UNIQUENES PAR COLUMN_LIST TABLE_TYPE STATUS NUM_ROWS LAST_ANAL G
------------------------------ ---------- ---------- --------- --- ------------------------------ ---------- ------ ---------- --------- - -------------------- ---------- ------ ---------- --------- --------------------- ---------- ------ ---------- --------- --------------------- --------
CRDT_LMT_NOTIFICATION_PK NORMAL UNIQUE YES CYCLE_CODE,CYCLE_MONTH,CYCLE_Y EAR,CUSTOMER_ID,AGREEMENT_ID,OFFER_INSTANCE,ITEM_ID TABLE N/A 5457339 03-DEC-14 N
查看对应的执行计划。
-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 | 74 | 4281 (1)| 00:00:52 | | |
| 1 | PARTITION RANGE ITERATOR | | 2 | 74 | 2141 (1)| 00:00:26 | KEY | KEY |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID| CRDT_LMT_NOTIFICATION | 2 | 74 | 2141 (1)| 00:00:26 | KEY | KEY |
|* 3 | INDEX RANGE SCAN | CRDT_LMT_NOTIFICATION_PK | 1 | | 2141 (1)| 00:00:26 | KEY | KEY |
| 4 | SORT AGGREGATE | | 1 | 34 | | | | |
| 5 | PARTITION RANGE ITERATOR | | 7 | 238 | 2141 (1)| 00:00:26 | KEY | KEY |
|* 6 | INDEX RANGE SCAN | CRDT_LMT_NOTIFICATION_PK | 7 | 238 | 2141 (1)| 00:00:26 | KEY | KEY |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A AND
TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')= (SELECT /*+ PUSH_SUBQ OPT_ESTIMATE (TABLE
"CRDT_LMT_NOTIFICATION" SCALE_ROWS=1016.803110 ) OPT_ESTIMATE (INDEX_FILTER "CRDT_LMT_NOTIFICATION"
"CRDT_LMT_NOTIFICATION_PK" SCALE_ROWS=440.696164 ) INDEX ("CRDT_LMT_NOTIFICATION" "CRDT_LMT_NOTIFICATION_PK")
*/ MAX(TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')) FROM "CRDT_LMT_NOTIFICATION"
"CRDT_LMT_NOTIFICATION" WHERE "CYCLE_CODE"=:A AND "OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND
"ITEM_ID"=:A))
6 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A)
我们使用sql_profile来看看调优的建议。这里的不同之处是原本的range scan变成了skip scan. 资源消耗一下子小了几十倍。
-----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 | 74 | 6 (67)| 00:00:01 | | |
| 1 | PARTITION RANGE ITERATOR | | 2 | 74 | 3 (67)| 00:00:01 | KEY | KEY |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID| CRDT_LMT_NOTIFICATION_PK | 2 | 74 | 3 (67)| 00:00:01 | KEY | KEY |
|* 3 | INDEX SKIP SCAN | CRDT_LMT_NOTIFICATION_PK_PK | 1 | | 3 (67)| 00:00:01 | KEY | KEY |
| 4 | SORT AGGREGATE | | 1 | 34 | | | | |
| 5 | PARTITION RANGE ITERATOR | | 7 | 238 | 3 (67)| 00:00:01 | KEY | KEY |
|* 6 | INDEX SKIP SCAN | CRDT_LMT_NOTIFICATION_PK_PK | 7 | 238 | 3 (67)| 00:00:01 | KEY | KEY |
-----------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A AND
TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')= (SELECT /*+ OPT_ESTIMATE (TABLE "CRDT_LMT_NOTIFICATION_PK"
SCALE_ROWS=1016.803110 ) OPT_ESTIMATE (INDEX_FILTER "CRDT_LMT_NOTIFICATION_PK" "CRDT_LMT_NOTIFICATION_PK_PK"
SCALE_ROWS=440.696164 ) */ MAX(TO_CHAR("CYCLE_YEAR",'9999')||TO_CHAR("CYCLE_MONTH",'09')) FROM
"PRDUSG3O"."CRDT_LMT_NOTIFICATION_PK" "CRDT_LMT_NOTIFICATION_PK" WHERE "CYCLE_CODE"=:A AND "OFFER_INSTANCE"=:A AND
"AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A))
6 - access("CYCLE_CODE"=:A AND "CUSTOMER_ID"=:A AND "AGREEMENT_ID"=:A AND "OFFER_INSTANCE"=:A AND "ITEM_ID"=:A)
filter("OFFER_INSTANCE"=:A AND "AGREEMENT_ID"=:A AND "CUSTOMER_ID"=:A AND "ITEM_ID"=:A)
在和开发确认之后，这条语句是关键的语句，是在一个新开发的需求中新加的。因为情况紧急，压力一下子堆在了我身上，大家希望我来对这条语句进行调优，能从0.5秒进行更高效的调优。
今天章节开篇先来介绍一下问题的情况，明天来详细的分析一下处理的思路，各种方案的对比和最终的建议。
