高水位線引起的查詢變慢解決方法

--例子中測試表占用表空間大小為：128M
SQL> SELECT a.bytes/1024/1024 || 'M' FROM user_segments a WHERE a.segment_name = 'TC_RES_PHY_EQP_PRO_MID_517';

A.BYTES/1024/1024||'M'
-----------------------------------------
128M

--查詢一條記錄成本為：4357，一致性讀為：15730 耗時 0.53 秒
SQL> set autotrace on
SQL> SELECT 1 FROM TC_RES_PHY_EQP_PRO_MID_517 a WHERE a.obj_id = 17202000000001;

 1
----------
 1


執行計劃
----------------------------------------------------------
Plan hash value: 854298875

------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 175 | 2275 | 4357 (2)| 00:00:53 |
|* 1 | TABLE ACCESS FULL| TC_RES_PHY_EQP_PRO_MID_517 | 175 | 2275 | 4357 (2)| 00:00:53 |
------------------------------------------------------------------------------------------------

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

 1 - filter("A"."OBJ_ID"=17202000000001)

Note
-----
 - dynamic sampling used for this statement (level=2)


統計信息
----------------------------------------------------------
 0 recursive calls
 0 db block gets
 15730 consistent gets
 0 physical reads
 0 redo size
 520 bytes sent via SQL*Net to client
 520 bytes received via SQL*Net from client
 2 SQL*Net roundtrips to/from client
 0 sorts (memory)
 0 sorts (disk)
 1 rows processed

--現在刪除大部分數據，只剩下一條測試數據：
SQL> DELETE FROM TC_RES_PHY_EQP_PRO_MID_517 a WHERE a.obj_id <> 17202000000001;

已刪除1172857行。

--查詢該段占用的表空間仍然為128M
SQL> set autotrace off
SQL> SELECT a.bytes/1024/1024 || 'M' FROM user_segments a WHERE a.segment_name = 'TC_RES_PHY_EQP_PRO_MID_517';

A.BYTES/1024/1024||'M'
-----------------------------------------
128M
SQL> COMMIT;

提交完成。

SQL> SELECT a.bytes/1024/1024 || 'M' FROM user_segments a WHERE a.segment_name = 'TC_RES_PHY_EQP_PRO_MID_517';

A.BYTES/1024/1024||'M'
-----------------------------------------
128M

--查詢一條記錄消耗的成本為：4316，一致性讀為：15730 耗時 0.52 秒
SQL> set autotrace on
SQL> SELECT 1 FROM TC_RES_PHY_EQP_PRO_MID_517 a WHERE a.obj_id = 17202000000001;

 1
----------
 1


執行計劃
----------------------------------------------------------
Plan hash value: 854298875

------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 13 | 4316 (1)| 00:00:52 |
|* 1 | TABLE ACCESS FULL| TC_RES_PHY_EQP_PRO_MID_517 | 1 | 13 | 4316 (1)| 00:00:52 |
------------------------------------------------------------------------------------------------

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

 1 - filter("A"."OBJ_ID"=17202000000001)

Note
-----
 - dynamic sampling used for this statement (level=2)


統計信息
----------------------------------------------------------
 0 recursive calls
 0 db block gets
 15730 consistent gets
 0 physical reads
 0 redo size
 520 bytes sent via SQL*Net to client
 520 bytes received via SQL*Net from client
 2 SQL*Net roundtrips to/from client
 0 sorts (memory)
 0 sorts (disk)
 1 rows processed

--一般情況下，表的rowid是不會變的，我們通過ALTER TABLE TABLE_NAME ENABLE ROW MOVEMENT;來打開行遷移
SQL> ALTER TABLE TC_RES_PHY_EQP_PRO_MID_517 ENABLE ROW MOVEMENT;

表已更改。

--整理碎片并回收空間
--此操作相比于ALTER TABLE MOVE：
--1.不會消耗更多的表空間
--2.可以在線執行，不會使索引失效
--3.可以使用參數CASCADE，同時收縮表上的索引
--4.ALTER TABLE MOVE之后表空間的位置肯定會發生變化，而SHRINK表空間的位置沒有發生變化
SQL> ALTER TABLE TC_RES_PHY_EQP_PRO_MID_517 SHRINK SPACE;

表已更改。
--查詢一條記錄消耗的成本為：2，一致性讀為：4 耗時 0.01 秒
SQL> SELECT 1 FROM TC_RES_PHY_EQP_PRO_MID_517 a WHERE a.obj_id = 17202000000001;

 1
----------
 1


執行計劃
----------------------------------------------------------
Plan hash value: 854298875

------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 13 | 2 (0)| 00:00:01 |
|* 1 | TABLE ACCESS FULL| TC_RES_PHY_EQP_PRO_MID_517 | 1 | 13 | 2 (0)| 00:00:01 |
------------------------------------------------------------------------------------------------

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

 1 - filter("A"."OBJ_ID"=17202000000001)

Note
-----
 - dynamic sampling used for this statement (level=2)


統計信息
----------------------------------------------------------
 0 recursive calls
 0 db block gets
 4 consistent gets
 0 physical reads
 0 redo size
 520 bytes sent via SQL*Net to client
 520 bytes received via SQL*Net from client
 2 SQL*Net roundtrips to/from client
 0 sorts (memory)
 0 sorts (disk)
 1 rows processed

--此時占用表空間只有4M

SQL> SELECT a.bytes/1024/1024 || 'M' FROM user_segments a WHERE a.segment_name = 'TC_RES_PHY_EQP_PRO_MID_517';

A.BYTES/1024/1024||'M'
-----------------------------------------
4M

You Asked
Hi Tom 
I have seen your posting on ENABLE ROW MOVEMENT which is available in 10g. It looks a 
very nice option since we can relocate and reorganize the heap tables without any outage 
since it does not invalidate indexes. But is there any performance hit or any other 
disadvantages for using this. I would like to use this in our new application.
Rgds
Anil 
and we said...
Well, the tables have to be in an ASSM (Automatic Segment Space Managment) tablespace for 
this to work (so if they are not, you have to move them there first in order to do this 
over time).
It will necessarily consume processing resources on your machine while running (it will 
read the table, it will delete/insert the rows at the bottom of the table to move them 
up, it will generate redo, it will generate undo).
I would suggest benchmarking -- collect performance metrics about the table before and 
after performing the operation. You would expect full scans to operate more efficiently 
after, you would expect index range scans to either be unchanged or "better" as you have 
more rows per block packed together (less data spread). You would be looking for that to 
happen -- statspack or the tools available in dbconsole would be useful for measuring 
that (the amount of work performed by your queries over time)

row chain：When a row is too large to fit into any block, row chaining occurs. In this case, the Oracle devide the row into smaller chunks. each chunk is stored in a block along
 with the necessary poiters to retrive and assemble the entire row.
row migration：when a row is to be updated and it cannot find the necessary free space in its block, the Oracle will move the entire row into a new block and leave a pointer from the orginal block to the new location. This process is called row migration.