2014-12-19 Created By BaoXinjian

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAATCAIAAAAf7rriAAABHklEQVQ4jc3Tv0sCcRjH8Wc4/wL9H4T0P3CpXYQcHQpuFIQayjvXQrnulhosHDpQsBZB8Q9wuWu1QVBcbMtNyaAm3y0e/fC4Lmvo4bO++D6fB74CLJdPm0X+MRaRn2HuowziDFNMVBFhajDvsHB4GYXBMQZxRikeVBHh0WDeDo+jKzz5gJ8dXjfAU4NZm4UbEse+4uC1qVYplSgU2Ntf4aHXeXrGLBjX65TLaEXyeTIZdrYjCskEIpLdRdMwTWybOxc/3GpxfsHpCUfH5HKk0xGF5JaHi1gm9jWuP3Ycbm+o1bAsDg9Q1U9YC8b9Pt0uzSZXl/LdrOHxmF6PTodGg0oFXY8oJLzOuveyf+f1vB8si17EDFj7zz5GyPwKvwECQrZ4yvBSdAAAAABJRU5ErkJggg==" alt="" />一、摘要

在SQL语句的执行计划中，包含很多字段项和很多模块，其不同字段代表了不同的含义且在不同的情形下某些字段、模块显示或不显示，下

面的描述给出了执行计划中各字段的含义以及各模块的描述。

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1. 分析解析计划

Step1. 打开熟悉的查看工具：PL/SQL Developer - Toad。

在PL/SQL Developer中写好一段SQL代码后，按F5，PL/SQL Developer会自动打开执行计划窗口，显示该SQL的执行计划。

Step2. 查看总COST，获得资源耗费的总体印象

一般而言，执行计划第一行所对应的COST(即成本耗费)值，反应了运行这段SQL的总体估计成本，单看这个总成本没有实际意义，

但可以拿它与相同逻辑不 同执行计划的SQL的总体COST进行比较，通常COST低的执行计划要好一些。

Step3. 按照从左至右，从上至下的方法，了解执行计划的执行步骤

执行计划按照层次逐步缩进，从左至右看，缩进最多的那一步，最先执行，如果缩进量相同，则按照从上而下的方法判断执行顺序，可粗略认为上面的步骤优先执行。

每一个执行步骤都有对应的COST,可从单步COST的高低，以及单步的估计结果集（对应ROWS/基数），来分析表的访问方式，连接顺序以及连接方式是 否合理。

Step4. 分析表的访问方式

表的访问方式主要是两种：

全表扫描（TABLE ACCESS FULL）和索引扫描(INDEX SCAN)，如果表上存在选择性很好的索引，却走了全表扫描，而且是大表的全表扫描，就说明表的访问方式可能存在问题；

若大表上没有合适的索引而走了全表 扫描，就需要分析能否建立索引，或者是否能选择更合适的表连接方式和连接顺序以提高效率。

Step5. 分析表的连接方式和连接顺序

表的连接顺序：就是以哪张表作为驱动表来连接其他表的先后访问顺序。

表的连接方式：简单来讲，就是两个表获得满足条件的数据时的连接过程。

主要有三种表连接方式，嵌套循环（NESTED LOOPS）、哈希连接（HASH JOIN）和排序-合并连接（SORT MERGE JOIN）。我们常见得是嵌套循环和哈希连接。

嵌套循环：

最适用也是最简单的连接方式。类似于用两层循环处理两个游标，外层游标称作驱动表，Oracle检索驱动表的数据，一条一条的代入内层游标，查找满足WHERE条件的所有数据，因此内层游标表中可用索引的选择性越好，嵌套循环连接的性能就越高。

哈希连接：

先将驱动表的数据按照条件字段以散列的方式放入内存，然后在内存中匹配满足条件的行。

哈希连接需要有合适的内存，而且必须在CBO优化模式下，连接两表的WHERE条件有等号的情况下才可以使用。哈希连接在表的数据量较大，表中没有合适的索引可用时比嵌套循环的效率要高。

2. 总结两点：

2.1 这里看到的执行计划，只是SQL运行前可能的执行方式，实际运行时可能因为软硬件环境的不同，而有所改变，而且cost高的执行计划，不一定在实际运行起来，速度就一定差，我们平时需要结合执行计划，和实际测试的运行时间，来确定一个执行计划的好坏。

2.2 对于表的连接顺序，多数情况下使用的是嵌套循环，尤其是在索引可用性好的情况下，使用嵌套循环式最好的，但当ORACLE发现需要访问的数据表较大，索引 的成本较高或者没有合适的索引可用时，会考虑使用哈希连接，以提高效率。排序合并连接的性能最差，但在存在排序需求，或者存在非等值连接无法使用哈希连接 的情况下，排序合并的效率，也可能比哈希连接或嵌套循环要好。

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1. 基本字段(总是可用的)

• Id            执行计划中每一个操作(行)的标识符。如果数字前面带有星号，意味着将在随后提供这行包含的谓词信息
• Operation  对应执行的操作。也叫行源操作
• Name        操作的对象名称

2. 查询优化器评估信息

• Rows(E-Rows)     预估操作返回的记录条数
• Bytes(E-Bytes)    预估操作返回的记录字节数
• TempSpc            预估操作使用临时表空间的大小
• Cost(%CPU)        预估操作所需的开销。在括号中列出了CPU开销的百分比。注意这些值是通过执行计划计算出来的。换句话说，父操作的开销包含子操作的开销
• Time                  预估执行操作所需要的时间(HH:MM:SS)

3. 分区(仅当访问分区表时下列字段可见)

• Pstart   访问的第一个分区。如果解析时不知道是哪个分区就设为KEY,KEY(I),KEY(MC),KEY(OR),KEY(SQ)
• Pstop    访问的最后一个分区。如果解析时不知道是哪个分区就设为KEY,KEY(I),KEY(MC),KEY(OR),KEY(SQ)

4. 并行和分布式处理(仅当使用并行或分布式操作时下列字段可见)

• Inst         在分布式操作中，指操作使用的数据库链接的名字
• TQ          在并行操作中，用于从属线程间通信的表队列
• IN-OUT    并行或分布式操作间的关系
• PQ Distrib 在并行操作中，生产者为发送数据给消费者进行的分配

5. 运行时统计(当设定参数statistics_level为all或使用gather_plan_statistics提示时，下列字段可见)

• Starts       指定操作执行的次数
• A-Rows     操作返回的真实记录数
• A-Time     操作执行的真实时间(HH:MM:SS.FF)

6. I/O 统计(当设定参数statistics_level为all或使用gather_plan_statistics提示时，下列字段可见)

• Buffers     执行期间进行的逻辑读操作数量
• Reads      执行期间进行的物理读操作数量
• Writes      执行期间进行的物理写操作数量

7. 内存使用统计

• OMem        最优执行所需内存的预估值
• 1Mem        一次通过(one-pass)执行所需内存的预估值
• 0/1/M        最优/一次通过/多次通过(multipass)模式操作执行的次数
• Used-Mem  最后一次执行时操作使用的内存量
• Used-Tmp  最后一次执行时操作使用的临时空间大小。这个字段必须扩大1024倍才能和其他衡量内存的字段一致(比如，32k意味着32MB)
• Max-Tmp   操作使用的最大临时空间大小。这个字段必须扩大1024倍才能和其他衡量内存的字段一致(比如，32k意味着32MB)

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1. 执行前，系统预估解析计划，Explain Plan

SQL> explain plan for

02.  2  select * from emp e,dept d

03.  3  where e.deptno=d.deptno

04.  4  and e.ename='SMITH';

05.

06.Explained.                                                                                                               
15.

16.SQL> set linesize 180

17.SQL> set pagesize 0

18.SQL> select * from table(dbms_xplan.display(null,null,'advanced'));   --使用dbms_xplan.display函数获得语句的执行计划

19.Plan hash value: 351108634                                            --SQL语句的哈希植

20.

21.----------------------------------------------------------------------------------------   /*执行计划部分*/

22.| Id  | Operation                    | Name    | Rows  | Bytes | Cost (%CPU)| Time     |

23.----------------------------------------------------------------------------------------

24.|   0 | SELECT STATEMENT             |         |     1 |   117 |     4   (0)| 00:00:01 |

25.|   1 |  NESTED LOOPS                |         |     1 |   117 |     4   (0)| 00:00:01 |

26.|*  2 |   TABLE ACCESS FULL          | EMP     |     1 |    87 |     3   (0)| 00:00:01 |

27.|   3 |   TABLE ACCESS BY INDEX ROWID| DEPT    |     1 |    30 |     1   (0)| 00:00:01 |

28.|*  4 |    INDEX UNIQUE SCAN         | PK_DEPT |     1 |       |     0   (0)| 00:00:01 |

29.----------------------------------------------------------------------------------------

30.

31.Query Block Name / Object Alias (identified by operation id):  --这部分显示的为查询块名和对象别名

32.-------------------------------------------------------------

33.

34.   1 - SEL$1                    --SEL$为select 的缩写，位于块1,相应的还有DEL$,INS$,UPD$等

35.   2 - SEL$1 / E@SEL$1          --E@SEL$1，对应到执行计划中的操作ID为2上，即在表E上的查询，E为别名，下面类同

36.   3 - SEL$1 / D@SEL$1

37.   4 - SEL$1 / D@SEL$1

38.

39.Outline Data                    --提纲部分，这部分将执行计划中的图形化方式以文本形式来呈现，即转换为提示符方式

40.-------------

41.

42.  /*+

43.      BEGIN_OUTLINE_DATA

44.      USE_NL(@"SEL$1" "D"@"SEL$1")                           --使用USE_NL提示，即嵌套循环

45.      LEADING(@"SEL$1" "E"@"SEL$1" "D"@"SEL$1")              --指明前导表

46.      INDEX_RS_ASC(@"SEL$1" "D"@"SEL$1" ("DEPT"."DEPTNO"))   --指明对于D上的访问方式为使用索引

47.      FULL(@"SEL$1" "E"@"SEL$1")                             --指明对于E上的访问方式为全表扫描

48.      OUTLINE_LEAF(@"SEL$1")

49.      ALL_ROWS

50.      OPTIMIZER_FEATURES_ENABLE('10.2.0.3')

51.      IGNORE_OPTIM_EMBEDDED_HINTS

52.      END_OUTLINE_DATA

53.  */

54.

55.Predicate Information (identified by operation id): --谓词信息部分，在执行计划中ID带有星号的每一行均对应到下面中的一行

56.---------------------------------------------------

57.

58.   2 - filter("E"."ENAME"='SMITH')

59.   4 - access("E"."DEPTNO"="D"."DEPTNO")

60.

61.Column Projection Information (identified by operation id):  --执行时每一步骤所返回的列，下面的不同步骤返回了不同的列

62.-----------------------------------------------------------

63.

64.   1 - (#keys=0) "E"."EMPNO"[NUMBER,22], "E"."ENAME"[VARCHAR2,10],

65.       "E"."JOB"[VARCHAR2,9], "E"."MGR"[NUMBER,22], "E"."HIREDATE"[DATE,7],

66.       "E"."SAL"[NUMBER,22], "E"."COMM"[NUMBER,22], "E"."DEPTNO"[NUMBER,22],

67.       "D"."DEPTNO"[NUMBER,22], "D"."DNAME"[VARCHAR2,14], "D"."LOC"[VARCHAR2,13]

68.   2 - "E"."EMPNO"[NUMBER,22], "E"."ENAME"[VARCHAR2,10], "E"."JOB"[VARCHAR2,9],

69.       "E"."MGR"[NUMBER,22], "E"."HIREDATE"[DATE,7], "E"."SAL"[NUMBER,22],

70.       "E"."COMM"[NUMBER,22], "E"."DEPTNO"[NUMBER,22]

71.   3 - "D"."DEPTNO"[NUMBER,22], "D"."DNAME"[VARCHAR2,14], "D"."LOC"[VARCHAR2,13]

72.   4 - "D".ROWID[ROWID,10], "D"."DEPTNO"[NUMBER,22]

73.

74.Note    --注释与描述部分，下面的描述中给出了本次SQL语句使用了动态采样功能

75.-----

76.   - dynamic sampling used for this statement

77.

78.58 rows selected. 

2. 执行后，系统实际解析计划，Explain Plan

SQL> select /*+ gather_plan_statistics */ *          --注意此处增加了提示gather_plan_statistics并且该语句被执行

02.  2  from emp e,dept d

03.  3  where e.deptno=d.deptno

04.  4  and e.ename='SMITH';

05.

06.      7369 SMITH      CLERK           7902 17-DEC-80        800                    20         20 RESEARCH       DALLAS

07.

08.SQL> select * from table(dbms_xplan.display_cursor(null,null,'iostats last')); --使用display_cursor获取实际的执行计划

09.

10.SQL_ID  fpx7zw59f405d, child number 0              --这部分给出了SQL语句的SQL_ID,子游标号以及原始的SQL语句

11.-------------------------------------

12.select /*+ gather_plan_statistics */ * from emp e,dept d where e.deptno=d.deptno and

13.e.ename='SMITH'

14.

15.Plan hash value: 351108634              --SQL 语句的哈希值

16.                                        --SQL语句的执行计划，可以看到下面显示的字段一部分不同于预估执行计划中的字段

17.-----------------------------------------------------------------------------------------------------------

18.| Id  | Operation                    | Name    | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |

19.-----------------------------------------------------------------------------------------------------------

20.|   1 |  NESTED LOOPS                |         |      1 |      1 |      1 |00:00:00.01 |      10 |      1 |

21.|*  2 |   TABLE ACCESS FULL          | EMP     |      1 |      1 |      1 |00:00:00.01 |       8 |      0 |

22.|   3 |   TABLE ACCESS BY INDEX ROWID| DEPT    |      1 |      1 |      1 |00:00:00.01 |       2 |      1 |

23.|*  4 |    INDEX UNIQUE SCAN         | PK_DEPT |      1 |      1 |      1 |00:00:00.01 |       1 |      1 |

24.-----------------------------------------------------------------------------------------------------------

25.

26.Predicate Information (identified by operation id):

27.---------------------------------------------------

28.

29.   2 - filter("E"."ENAME"='SMITH')

30.   4 - access("E"."DEPTNO"="D"."DEPTNO")

31.

32.Note

33.-----

34.   - dynamic sampling used for this statement

35.

36.

37.26 rows selected.

Thank and Regards

转载：乐沙弥 - http://blog.csdn.net/leshami/article/details/6860007

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" 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