读懂Oracle 10053事件

我们可以通过10046事件看到一个SQL的执行的统计信息，以及执行计划，但是我们只看到了CBO最终告诉我们的执行结果，却并不知道CBO为什么要这么选择，那么就可以通过10053事件来生成SQL分析的整个过程到trace文件中，通俗点讲10053跟踪选路过程，10046产生结果。

关于10046事件参考我的blog：

http://5073392.blog.51cto.com/5063392/1308195

10053包含2个级别：

Level 2：2级是1级的一个子集，它包含以下内容：

Column statistics

Single Access Paths

Join Costs

Table Joins Considered

Join Methods Considered (NL/MS/HA)

Level 1：1级比2级更详细，它包含2级的所有内容，在加如下内容：

Parameters used by the optimizer

Index statistics

产生一个10053的trace文件

SQL> create table t as select rownum id from dba_objects;

Table created.

SQL> create index idx_t on t(id);

Index created.

SQL> exec dbms_stats.gather_table_stats('sys','t',cascade=>true);

PL/SQL procedure successfully completed.

SQL> create table t1 as select id,'T1' name from t where id<1000;

Table created.

SQL> select tracefile from v$process where addr=(select paddr from v$session where sid=(select distinct sid from v$mystat));

TRACEFILE

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

/u01/app/oracle/diag/rdbms/rbksafari/RBKSAFARI/trace/RBKSAFARI_ora_9558.trc

SQL> alter session set events '10053 trace name context forever, level 1';

Session altered.

SQL> explain plan for select t1.* from t,t1 where t.id<100 and t.id=t1.id;

Explained.

SQL> alter session set events '10053 trace name context off';

Session altered.

10053和10046事件的用法是一样的，需要注意的是这个trace文件不能用tkprof工具处理，tkprof工具只能处理10046和sql_trace文件，下面是刚才生成的trace文件内容

这里从BASE STATISTICAL INFORMATION开始看

***************************************

BASE STATISTICAL INFORMATION

***********************

Table Stats::

Table: T1 Alias: T1 (NOT ANALYZED)

#Rows: 164 #Blks: 2 AvgRowLen: 100.00

***********************

Table Stats::

Table: T Alias: T

#Rows: 73924 #Blks: 113 AvgRowLen: 5.00

Index Stats::

Index: IDX_T Col#: 1

LVLS: 1 #LB: 164 #DK: 73924 LB/K: 1.00 DB/K: 1.00 CLUF: 113.00

Access path analysis for T

***************************************

这一部分是sql语句中用到的对象基本信息，包括关联表和索引信息，我们看到这里列出了关于这个查询的三个对象信息，表T,T1和索引IDX_T，表信息包含了表的行数（#Rows），数据块数（#Blks），平均行长（AvgRowLen）索引项部分列出了索引叶块数（#LB），每个索引键值占据的数据块数（LB/K），每个索引键值对应的表中数据块数（DB/K）索引的聚合因子（CLUF）,当索引的键值对应的数据库越大，索引的聚合因子就越大，越不利用索引的使用。

接下来的部分是CBO计算的每个对象单独访问的代价

***************************************

SINGLE TABLE ACCESS PATH

Single Table Cardinality Estimation for T[T]

Table: T Alias: T

Card: Original: 73924.000000 Rounded: 99 Computed: 99.00 Non Adjusted: 99.00

Access Path: TableScan

Cost: 32.49 Resp: 32.49 Degree: 0

Cost_io: 32.00 Cost_cpu: 15589523

Resp_io: 32.00 Resp_cpu: 15589523

Access Path: index (index (FFS))

Index: IDX_T

resc_io: 46.00 resc_cpu: 13734996

ix_sel: 0.000000 ix_sel_with_filters: 1.000000

Access Path: index (FFS)

Cost: 46.43 Resp: 46.43 Degree: 1

Cost_io: 46.00 Cost_cpu: 13734996

Resp_io: 46.00 Resp_cpu: 13734996

Access Path: index (IndexOnly)

Index: IDX_T

resc_io: 2.00 resc_cpu: 34243

ix_sel: 0.001339 ix_sel_with_filters: 0.001339

Cost: 2.00 Resp: 2.00 Degree: 1

Best:: AccessPath: IndexRange

Index: IDX_T

Cost: 2.00 Degree: 1 Resp: 2.00 Card: 99.00 Bytes: 0

Access path analysis for T1

***************************************

这里有两个指标对于我们分析执行计划比较重要：

Card: Original: 73924.000000

原纪录数，也就是操作数据原的输入记录数，在这里就是表的实际行数73294

Card: Rounded： 99

输出的记录数，CBO计算出通过条件过滤，预计得到的行数99

通过这里我们可以看出对于T表CBO给我们计算出了3种访问方式

全表扫描

Access Path: TableScan 开销：Cost: 32.49

索引快速扫描

Access Path: index (index (FFS)) 开销：Cost: 46.43

单独访问索引

Access Path: index (IndexOnly) 开销：Cost: 2.00

可以看出，单独访问索引的方式是代价最低的，因此CBO得出下来结论

Best:: AccessPath: IndexRange

Index: IDX_T

Cost: 2.00 Degree: 1 Resp: 2.00 Card: 99.00 Bytes: 0

这里我不明白上面写的是IndexOnly，为什么最后结论改写成IndexRange。为何oracle不直接将Access Path写成IndexRange

下面是对T1表访问方式的描述

Table: T1 Alias: T1

Card: Original: 999.000000 Rounded: 99 Computed: 99.00 Non Adjusted: 99.00

Access Path: TableScan

Cost: 2.01 Resp: 2.01 Degree: 0

Cost_io: 2.00 Cost_cpu: 216023

Resp_io: 2.00 Resp_cpu: 216023

Best:: AccessPath: TableScan

Cost: 2.01 Degree: 1 Resp: 2.01 Card: 99.00 Bytes: 0

由于我们没有在T1上创建索引因此对于T1表的访问只有TableScan全表扫描一种方式

下面是T与T1表的关联统计最终分析出关联最小的开销作为最终的执行计划

OPTIMIZER STATISTICS AND COMPUTATIONS

***************************************

GENERAL PLANS

***************************************

Considering cardinality-based initial join order.

Permutations for Starting Table :0

Join order[1]: T1[T1]#0 T[T]#1

***************

Now joining: T[T]#1

***************

NL Join （一）

Outer table: Card: 99.00 Cost: 2.01 Resp: 2.01 Degree: 1 Bytes: 17

Access path analysis for T

Inner table: T Alias: T

Access Path: TableScan

NL Join: Cost: 3082.41 Resp: 3082.41 Degree: 1

Cost_io: 3034.00 Cost_cpu: 1543578772

Resp_io: 3034.00 Resp_cpu: 1543578772

Access Path: index (index (FFS))

Index: IDX_T

resc_io: 44.43 resc_cpu: 13734996

ix_sel: 0.000000 ix_sel_with_filters: 1.000000

Inner table: T Alias: T

Access Path: index (FFS)

NL Join: Cost: 4443.65 Resp: 4443.65 Degree: 1

Cost_io: 4401.00 Cost_cpu: 1359980643

Resp_io: 4401.00 Resp_cpu: 1359980643

kkofmx: index filter:"T"."ID"<100

Access Path: index (AllEqJoinGuess)

Index: IDX_T

resc_io: 1.00 resc_cpu: 8171

ix_sel: 0.000014 ix_sel_with_filters: 0.000000

***** Logdef predicate Adjustment ******

Final IO cst 0.00 , CPU cst 50.00

***** End Logdef Adjustment ******

NL Join : Cost: 101.03 Resp: 101.03 Degree: 1

Cost_io: 101.00 Cost_cpu: 1029945

Resp_io: 101.00 Resp_cpu: 1029945

Best NL cost: 101.03 --nested loops join 代价是101.03

resc: 101.03 resc_io: 101.00 resc_cpu: 1029945

resp: 101.03 resp_io: 101.00 resc_cpu: 1029945

Join Card: 98.011326 = = outer (99.000000) * inner (99.001339) * sel (0.010000)

Join Card - Rounded: 98 Computed: 98.01

Outer table: T1 Alias: T1

resc: 2.01 card 99.00 bytes: 17 deg: 1 resp: 2.01

Inner table: T Alias: T

resc: 2.00 card: 99.00 bytes: 5 deg: 1 resp: 2.00

using dmeth: 2 #groups: 1

SORT ressource Sort statistics

Sort width: 179 Area size: 157696 Max Area size: 31666176

Degree: 1

Blocks to Sort: 1 Row size: 29 Total Rows: 99

Initial runs: 1 Merge passes: 0 IO Cost / pass: 0

Total IO sort cost: 0 Total CPU sort cost: 31913716

Total Temp space used: 0

SORT ressource Sort statistics

Sort width: 179 Area size: 157696 Max Area size: 31666176

Degree: 1

Blocks to Sort: 1 Row size: 16 Total Rows: 99

Initial runs: 1 Merge passes: 0 IO Cost / pass: 0

Total IO sort cost: 0 Total CPU sort cost: 31913716

Total Temp space used: 0

SM join: Resc: 6.01 Resp: 6.01 [multiMatchCost=0.00]

SM Join （二）

SM cost: 6.01 --Sort merge join 的代价是6.01

resc: 6.01 resc_io: 4.00 resc_cpu: 64077698

resp: 6.01 resp_io: 4.00 resp_cpu: 64077698

Outer table: T1 Alias: T1

resc: 2.01 card 99.00 bytes: 17 deg: 1 resp: 2.01

Inner table: T Alias: T

resc: 2.00 card: 99.00 bytes: 5 deg: 1 resp: 2.00

using dmeth: 2 #groups: 1

Cost per ptn: 0.50 #ptns: 1

hash_area: 124 (max=7731) buildfrag: 1 probefrag: 1 ppasses: 1

hash_area: 124 (max=7731) buildfrag: 1 probefrag: 1 ppasses: 1

Hash join: Resc: 4.51 Resp: 4.51 [multiMatchCost=0.00]

HA Join （三）

HA cost: 4.51 --Hash join的代价是4.51

resc: 4.51 resc_io: 4.00 resc_cpu: 16217089

resp: 4.51 resp_io: 4.00 resp_cpu: 16217089

Best:: JoinMethod: Hash --第一种关联花费最小的是Hash join

Cost: 4.51 Degree: 1 Resp: 4.51 Card: 98.01 Bytes: 22

***********************

Best so far: Table#: 0 cost: 2.0068 card: 99.0000 bytes: 1683

Table#: 1 cost: 4.5086 card: 98.0113 bytes: 2156

***********************

Join order[2]: T[T]#1 T1[T1]#0

***************

Now joining: T1[T1]#0

***************

NL Join （一）

Outer table: Card: 99.00 Cost: 2.00 Resp: 2.00 Degree: 1 Bytes: 5

Access path analysis for T1

Inner table: T1 Alias: T1

Access Path: TableScan

NL Join: Cost: 57.67 Resp: 57.67 Degree: 1

Cost_io: 57.00 Cost_cpu: 21420508

Resp_io: 57.00 Resp_cpu: 21420508

Best NL cost: 57.67

resc: 57.67 resc_io: 57.00 resc_cpu: 21420508

resp: 57.67 resp_io: 57.00 resc_cpu: 21420508

Join Card: 98.011326 = = outer (99.001339) * inner (99.000000) * sel (0.010000)

Join Card - Rounded: 98 Computed: 98.01

Outer table: T Alias: T

resc: 2.00 card 99.00 bytes: 5 deg: 1 resp: 2.00

Inner table: T1 Alias: T1

resc: 2.01 card: 99.00 bytes: 17 deg: 1 resp: 2.01

using dmeth: 2 #groups: 1

SORT ressource Sort statistics

Sort width: 179 Area size: 157696 Max Area size: 31666176

Degree: 1

Blocks to Sort: 1 Row size: 29 Total Rows: 99

Initial runs: 1 Merge passes: 0 IO Cost / pass: 0

Total IO sort cost: 0 Total CPU sort cost: 31913716

Total Temp space used: 0

SM join: Resc: 5.01 Resp: 5.01 [multiMatchCost=0.00]

SM Join （二）

SM cost: 5.01

resc: 5.01 resc_io: 4.00 resc_cpu: 32163982

resp: 5.01 resp_io: 4.00 resp_cpu: 32163982

Outer table: T Alias: T

resc: 2.00 card 99.00 bytes: 5 deg: 1 resp: 2.00

Inner table: T1 Alias: T1

resc: 2.01 card: 99.00 bytes: 17 deg: 1 resp: 2.01

using dmeth: 2 #groups: 1

Cost per ptn: 0.50 #ptns: 1

hash_area: 124 (max=7731) buildfrag: 1 probefrag: 1 ppasses: 1

Hash join: Resc: 4.51 Resp: 4.51 [multiMatchCost=0.00]

HA Join （三）

HA cost: 4.51

resc: 4.51 resc_io: 4.00 resc_cpu: 16217089

resp: 4.51 resp_io: 4.00 resp_cpu: 16217089

Join order aborted: cost > best plan cost

***********************

2中关联方式统计如下：

T1关联T

nested join： 101.03 resc_cpu: 1029945

sort merge join：6.01 resc_cpu: 64077698

Hash join：4.51 resc_cpu: 16217089

T关联T1

nested join：57.67resc_cpu: 21420508

nested join：5.01resc_cpu: 32163982

nested join：4.51resc_cpu: 16217089

下面是最终关联计算开销的结果Best join order: 1，从上面的结果看出2种关联中最优的都是Hash join，碰巧这个两个hash join开销一样，oracle选择第一种关联方式

Number of join permutations tried: 2

*********************************

Consider using bloom filter between T1[T1] and T[T]

kkoBloomFilter: join (lcdn:99 rcdn:99 jcdn:98 limit:4901)

Computing bloom ndv for creator:T1[T1] ccdn:99.0 and user:T[T] ucdn:99.0

kkopqComputeBloomNdv: predicate (bndv:73924 ndv:100) and (bndv:999 ndv:99)

kkopqComputeBloomNdv: pred cnt:2 ndv:99 reduction:1

kkoBloomFilter: join ndv:0 reduction:0.999986 (limit:0.500000) rejected because distinct value ratio

(newjo-save) [0 1 ]

Trying or-Expansion on query block SEL$1 (#0)

Transfer Optimizer annotations for query block SEL$1 (#0)

id=0 frofand predicate="T1"."ID"<100

id=0 frofkksm[i] (sort-merge/hash) predicate="T"."ID"="T1"."ID"

id=0 frosand (sort-merge/hash) predicate="T"."ID"="T1"."ID"

id=0 frofkke[i] (index stop key) predicate="T"."ID"<100

Final cost for query block SEL$1 (#0) - All Rows Plan:

Best join order: 1

Cost: 4.5086 Degree: 1 Card: 98.0000 Bytes: 2156

Resc: 4.5086 Resc_io: 4.0000 Resc_cpu: 16217089

Resp: 4.5086 Resp_io: 4.0000 Resc_cpu: 16217089

kkoqbc-subheap (delete addr=0x7ff58e2dfa50, in-use=29224, alloc=41296)

kkoqbc-end:

:

call(in-use=19752, alloc=82024), compile(in-use=67328, alloc=68488), execution(in-use=89616, alloc=93504)

kkoqbc: finish optimizing query block SEL$1 (#0)

下面是最终算出的执行计划结果

============

Plan Table

============

--------------------------------------+-----------------------------------+

| Id | Operation | Name | Rows | Bytes | Cost | Time |

--------------------------------------+-----------------------------------+

| 0 | SELECT STATEMENT | | | | 5 | |

| 1 | HASH JOIN | | 98 | 2156 | 5 | 00:00:01 |

| 2 | TABLE ACCESS FULL | T1 | 99 | 1683 | 2 | 00:00:01 |

| 3 | INDEX RANGE SCAN | IDX_T | 99 | 495 | 2 | 00:00:01 |

--------------------------------------+-----------------------------------+

Predicate Information:

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

1 - access("T"."ID"="T1"."ID")

2 - filter("T1"."ID"<100)

3 - access("T"."ID"<100)

注：trace文件头部和尾部省略