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