SQL> create table t as select * from dba_objects;
Table created.
SQL> create index idx_t on t(object_id);
Index created.
SQL> BEGIN
2 DBMS_STATS.GATHER_TABLE_STATS(ownname => 'TEST',
3 tabname => 'T',
4 estimate_percent => 100,
5 method_opt => 'for all columns size auto',
6 degree => DBMS_STATS.AUTO_DEGREE,
7 cascade => TRUE);
8 END;
9 /
SQL> select leaf_blocks,blevel,clustering_factor from dba_indexes where index_name='IDX_T';
LEAF_BLOCKS BLEVEL CLUSTERING_FACTOR
----------- ---------- -----------------
165 1 1705
LEAF_BLOCKS 叶子块 165个
BLEVEL 索引高度-1
集群因子；
CLUSTERING_FACTOR =1705
SQL> select count(distinct dbms_rowid.rowid_block_number(rowid)) from T;
COUNT(DISTINCTDBMS_ROWID.ROWID_BLOCK_NUMBER(ROWID))
---------------------------------------------------
1057
存储在1057个块中
SQL> set linesize 200
SQL> select b.num_rows,
a.num_distinct,
a.num_nulls,
utl_raw.cast_to_number(high_value) high_value,
utl_raw.cast_to_number(low_value) low_value,
(b.num_rows - a.num_nulls) "NUM_ROWS-NUM_NULLS",
utl_raw.cast_to_number(high_value) -
utl_raw.cast_to_number(low_value) "HIGH_VALUE-LOW_VALUE"
from dba_tab_col_statistics a, dba_tables b
where a.owner = b.owner
and a.table_name = b.table_name
and a.owner = 'TEST'
and a.table_name = upper('T')
and a.column_name = 'OBJECT_ID'; 2 3 4 5 6 7 8 9 10 11 12 13 14
NUM_ROWS NUM_DISTINCT NUM_NULLS HIGH_VALUE LOW_VALUE NUM_ROWS-NUM_NULLS HIGH_VALUE-LOW_VALUE
---------- ------------ ---------- ---------- ---------- ------------------ --------------------
74486 74486 0 77616 2 74486 77614
SQL> explain plan for select owner from t where object_id<1000;
Explained.
SQL> select * from table(dbms_xplan.display());
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------
--------------------------------------------
Plan hash value: 1594971208
-------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 958 | 10538 | 26 (0)| 00:00:01 |
| 1 | TABLE ACCESS BY INDEX ROWID| T | 958 | 10538 | 26 (0)| 00:00:01 |
|* 2 | INDEX RANGE SCAN | IDX_T | 958 | | 4 (0)| 00:00:01 |
-------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------------------------------
--------------------------------------------
---------------------------------------------------
2 - access("OBJECT_ID"<1000)
14 rows selected.
索引扫描首先要定义到叶子块:
定位到叶子块 要扫描 多少个块？？？ 需要高度-1个块
叶子块个数 乘以 选择性
定位到叶子块 要扫描 多少个块？？？
回表和集群因子有关:
选择性(Selectivity) 列唯一键(Distinct_Keys) 与行数(Num_Rows)的比值。
这里有个概念叫有效选择性 ，< 的有效选择性为
(limit-low_value)/(high_value-low_value)
limit 是限制
1000
low_value=2
1000-2 有可能扫到的值的范围
high_value-low_value 表示总共有多少个值:
HIGH_VALUE=77616
LOW_VALUE=2
HIGH_VALUE-LOW_VALUE=77614
LEAF_BLOCKS=165
索引扫描的计算公式如下：
cost =
blevel +
celiling(leaf_blocks *effective index selectivity) +
celiling(clustering_factor * effective table selectivity)
SQL> select 1+ceil(165*(1000-2)/77614)+ceil(1705*(1000-2)/77614) from dual;
1+CEIL(165*(1000-2)/77614)+CEIL(1705*(1000-2)/77614)
----------------------------------------------------
26
为啥effective table selectivity和effective index selectivity一样？
表和索引都包含指定列的数据 两者当然一样
