PLSQL_性能优化系列17_Oracle Merge Into和Update更新效率

2015-05-21 Created By BaoXinjian

一、摘要

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以前只考虑 merge into 只是在特定场合下方便才使用的，今天才发现，merge into 竟然会比 update 在更新数据时有这么大的改进。

其实呢，merge into部分的update和update也没啥不同的，不同的地方在于使用merge into后执行计划变了。

merge方法是最简洁，效率最高的方式，在大数据量更新时优先使用这种方式。

1. 基本语法

merge into test1 using test2

on (test1.id = test2.id)

when matched then update

set test1.name = nvl2(test1.name,test2.name,test1.name);

update内联视图方式：使用这种方式必须在test2.id上有主键 (这里很好理解，必须保证每一个test1.id对应在test2里只有一条记录，如果test2中有多条对应的记录，怎么更新test1)

或者on (test1.id = test2.id, test1.name = test2.name ....)，通过多栏位对比，确认唯一记录，类似Unique Index

 

2. 使用并行，加快大量数据更新：

merge /*+parallel(test1,4)*/ into test1 using test2 on (test1.id = test2.id) when matched then update

set test1.name = nvl2(test1.name,test2.name,test1.name);

 

二、测试案例 - Update / Merge Into

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1. 创建测试数据

create table test1 as select * from dba_objects where rownum<=10000;--10000条记录



create table test2 as select * from dba_objects--73056条记录

 

2. 直接Update时间和效率

SQL> alter system flush shared_pool; System altered. SQL> alter system flush buffer_cache; System altered. SQL> set linesize 400 pagesize 400 SQL> set autot trace SQL> set timing on SQL> update test1 t1 2     set t1.object_name = (select t2.object_name

  3                             from test2 t2 4                            where t2.object_id = t1.object_id); 10000 rows updated. Elapsed: 00:06:33.35 Execution Plan

----------------------------------------------------------

   0      UPDATE STATEMENT Optimizer=ALL_ROWS (Cost=2923252 Card=10011 Bytes=790869) 1    0   UPDATE OF 'TEST1'

   2    1     TABLE ACCESS (FULL) OF 'TEST1' (TABLE) (Cost=40 Card=10011 Bytes=790869) 3    1     TABLE ACCESS (FULL) OF 'TEST2' (TABLE) (Cost=292 Card=772 Bytes=60988) Statistics

----------------------------------------------------------

        430 recursive calls 11122 db block gets 15275257 consistent gets 1175 physical reads 4058752 redo size 520  bytes sent via SQL*Net to client 668  bytes received via SQL*Net from client 3  SQL*Net roundtrips to/from client 7 sorts (memory) 0  sorts (disk) 10000  rows processed

 

3. 通过Merge Into时间和效率 

SQL> alter system flush shared_pool; System altered. Elapsed: 00:00:00.45 SQL> alter system flush buffer_cache; System altered. Elapsed: 00:00:00.71 SQL> merge into test1 t1 2 using test2 t2 3  on (t1.object_id = t2.object_id) 4  when matched then

  5    update set t1.object_name = t2.object_name; 10000 rows merged. Elapsed: 00:00:00.92 Execution Plan

----------------------------------------------------------

   0      MERGE STATEMENT Optimizer=ALL_ROWS (Cost=1243 Card=10011 Bytes=1321452) 1    0   MERGE OF 'TEST1'

   2    1     VIEW

   3    2       HASH JOIN (Cost=1243 Card=10011 Bytes=4264686) 4    3         TABLE ACCESS (FULL) OF 'TEST1' (TABLE) (Cost=40 Card=10011 Bytes=2192409) 5    3         TABLE ACCESS (FULL) OF 'TEST2' (TABLE) (Cost=292 Card=77163 Bytes=15972741) Statistics

----------------------------------------------------------

       1224 recursive calls 10279 db block gets 1586 consistent gets 1191 physical reads 2803872 redo size 526  bytes sent via SQL*Net to client 634  bytes received via SQL*Net from client 3  SQL*Net roundtrips to/from client 12 sorts (memory) 0  sorts (disk) 10000  rows processed

 

三、解析计划

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1. 通过Update的解析计划

SQL> set autot off SQL> update /*+gather_plan_statistics*/ test1 t1 2     set t1.object_name = (select t2.object_name

  3                             from test2 t2 4                            where t2.object_id = t1.object_id); 10000 rows updated. Elapsed: 00:04:32.81 SQL> select * from table(dbms_xplan.display_cursor(null,null,'iostats')); PLAN_TABLE_OUTPUT --------------------------------------------------------------------------------------------

SQL_ID  c8qt9a54qgmqg, child number 0

-------------------------------------

update /*+gather_plan_statistics*/ test1 t1    set t1.object_name = (select t2.object_name                            from test2 t2 where t2.object_id = t1.object_id) Plan hash value: 3883393169



--------------------------------------------------------------------------------------

| Id  | Operation          | Name  | Starts | E-Rows | A-Rows |   A-Time   | Buffers |

--------------------------------------------------------------------------------------

|   0 | UPDATE STATEMENT   |       |      1 |        |      0 |00:04:32.73 |      10M|

|   1 |  UPDATE            | TEST1 |      1 |        |      0 |00:04:32.73 |      10M|

|   2 |   TABLE ACCESS FULL| TEST1 |      1 |  10011 |  10000 |00:00:00.17 |     133 |

|*  3 |   TABLE ACCESS FULL| TEST2 |  10000 |    772 |  10000 |00:04:31.51 |      10M|

--------------------------------------------------------------------------------------

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



   3 - filter("T2"."OBJECT_ID"=:B1) Note -----

   - dynamic sampling used for this statement (level=2) 26 rows selected. Elapsed: 00:00:01.38

 

2. 通过Merge Into的解析计划

SQL> merge /*+gather_plan_statistics*/

  2  into test1 t1 3 using test2 t2 4  on (t1.object_id = t2.object_id) 5  when matched then

  6    update set t1.object_name = t2.object_name; 10000 rows merged. Elapsed: 00:00:00.52 SQL> select * from table(dbms_xplan.display_cursor(null,null,'iostats')); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------------------

SQL_ID  9n4tc6tvwaj9c, child number 0

-------------------------------------

merge /*+gather_plan_statistics*/ into test1 t1 using test2 t2 on (t1.object_id = t2.object_id) when matched then   update set t1.object_name = t2.object_name



Plan hash value: 818823782



----------------------------------------------------------------------------------------

| Id  | Operation            | Name  | Starts | E-Rows | A-Rows |   A-Time   | Buffers |

----------------------------------------------------------------------------------------

|   0 | MERGE STATEMENT      |       |      1 |        |      0 |00:00:00.47 |   11458 |

|   1 |  MERGE               | TEST1 |      1 |        |      0 |00:00:00.47 |   11458 |

|   2 |   VIEW               |       |      1 |        |  10000 |00:00:00.33 |    1179 |

|*  3 |    HASH JOIN         |       |      1 |  10011 |  10000 |00:00:00.25 |    1179 |

|   4 |     TABLE ACCESS FULL| TEST1 |      1 |  10011 |  10000 |00:00:00.08 |     133 |

|   5 |     TABLE ACCESS FULL| TEST2 |      1 |  77163 |  73056 |00:00:00.26 |    1046 |

----------------------------------------------------------------------------------------

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



   3 - access("T1"."OBJECT_ID"="T2"."OBJECT_ID") Note -----

   - dynamic sampling used for this statement (level=2) 28 rows selected. Elapsed: 00:00:00.15

 

四、结果分析

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1. 测试结果对比：update和merge into 都更新1w条记录，

update耗时6分钟，逻辑读消耗15275257；

merge into 耗时6秒钟，消耗逻辑读1586，相差太大了。

 

2. 其实看着执行计划，这个结果也很容易理解：

update采用的类似nested loop的方式，对更新的每一行，都会对查询的表扫描一次；

merge into这里选择的是hash join，则针对每张表都是做了一次 full table scan，对每张表都只是扫描一次。

 

3. Oracle官方建议，在大数据更新过程中，也是通过使用Merge Into代替Update

 

Thanks and Regards

参考： http://blog.csdn.net/xiexbb/article/details/4242063