Oracle Dimension 下 (ZT)
我们再创建一张customer_hierarchy表，用于存储客户代码、邮政编码和地区的关系，然后我们将按不同邮编或地区来查询各自的月度、季度或者年度销量信息。
Roby@XUE> create table customer_hierarchy
2 ( cust_id primary key, zip_code, region )
3 organization index
4 as
5 select cust_id,
6 mod( rownum, 6 ) || to_char(mod( rownum, 1000 ), 'fm0000') zip_code,
7 mod( rownum, 6 ) region
8 from ( select distinct cust_id from sales)
9 /
Table created.
Roby@XUE> analyze table customer_hierarchy compute statistics;
Table analyzed.
改写物化视图，查询方案中添加按不同邮编的月度统计销量。
Roby@XUE> drop materialized view mv_sales;
Materialized view dropped.
Roby@XUE> create materialized view mv_sales
2 build immediate
3 refresh on demand
4 enable query rewrite
5 as
6 select customer_hierarchy.zip_code,
7 time_hierarchy.mmyyyy,
8 sum(sales.sales_amount) sales_amount
9 from sales, time_hierarchy, customer_hierarchy
10 where sales.trans_date = time_hierarchy.day
11 and sales.cust_id = customer_hierarchy.cust_id
12 group by customer_hierarchy.zip_code, time_hierarchy.mmyyyy
13 /
Materialized view created.
Roby@XUE> set autotrace traceonly
Roby@XUE> select customer_hierarchy.zip_code,
2 time_hierarchy.mmyyyy,
3 sum(sales.sales_amount) sales_amount
4 from sales, time_hierarchy, customer_hierarchy
5 where sales.trans_date = time_hierarchy.day
6 and sales.cust_id = customer_hierarchy.cust_id
7 group by customer_hierarchy.zip_code, time_hierarchy.mmyyyy
8 /
1216 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=2 Card=409 Bytes=20450)
1 0 TABLE ACCESS (FULL) OF 'MV_SALES' (Cost=2 Card=409 Bytes=20450)
Statistics
----------------------------------------------------------
28 recursive calls
0 db block gets
116 consistent gets
5 physical reads
可以看到如果按不同邮编、不同月度来统计查询的话，优化器将会查询物化视图中的查询方案，性能也是比较可观的。假如我们查不同地区年度的统计销量信息，结果又会是怎样？
Roby@XUE> select customer_hierarchy.region,
2 time_hierarchy.yyyy,
3 sum(sales.sales_amount) sales_amount
4 from sales, time_hierarchy, customer_hierarchy
5 where sales.trans_date = time_hierarchy.day
6 and sales.cust_id = customer_hierarchy.cust_id
7 group by customer_hierarchy.region, time_hierarchy.yyyy
8 /
9 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=1681 Card=9 Bytes=261)
1 0 SORT (GROUP BY) (Cost=1681 Card=9 Bytes=261)
2 1 NESTED LOOPS (Cost=35 Card=426672 Bytes=12373488)
3 2 NESTED LOOPS (Cost=35 Card=426672 Bytes=8106768)
4 3 TABLE ACCESS (FULL) OF 'SALES' (Cost=35 Card=426672
5 3 INDEX (UNIQUE SCAN) OF 'SYS_IOT_TOP_7833' (UNIQUE)
6 2 INDEX (UNIQUE SCAN) OF 'SYS_IOT_TOP_7828' (UNIQUE)
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
428047 consistent gets
745 physical reads
可以看到查询性能大有影响。接下我们同样创建dimension sales_dimension，用于说明客户代码和邮编、地区间的关系：
Roby@XUE> drop dimension time_hierarchy_dim
2 /
Dimension dropped.
Roby@XUE> create dimension sales_dimension
2 level cust_id is customer_hierarchy.cust_id
3 level zip_code is customer_hierarchy.zip_code
4 level region is customer_hierarchy.region
5 level day is time_hierarchy.day
6 level mmyyyy is time_hierarchy.mmyyyy
7 level qtr_yyyy is time_hierarchy.qtr_yyyy
8 level yyyy is time_hierarchy.yyyy
9 hierarchy cust_rollup
10 (
11 cust_id child of
12 zip_code child of
13 region
14 )
15 hierarchy time_rollup
16 (
17 day child of
18 mmyyyy child of
19 qtr_yyyy child of
20 yyyy
21 )
22 attribute mmyyyy
23 determines mon_yyyy;
Dimension created.
再回到原来的查询，我们可以看到查询性能有了大幅的提升：
Roby@XUE> set autotrace on
Roby@XUE> select customer_hierarchy.region,
2 time_hierarchy.yyyy,
3 sum(sales.sales_amount) sales_amount
4 from sales, time_hierarchy, customer_hierarchy
5 where sales.trans_date = time_hierarchy.day
6 and sales.cust_id = customer_hierarchy.cust_id
7 group by customer_hierarchy.region, time_hierarchy.yyyy
8 /
REGION YYYY SALES_AMOUNT
---------- ---------- ------------
0 2006 7.3144E+11
0 2007 4484956329
1 2006 7.8448E+11
2 2006 7.7257E+11
2 2007 4684418980
3 2006 7.7088E+11
4 2006 7.8004E+11
4 2007 3127953246
5 2006 7.3273E+11
9 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=15 Card=9 Bytes=576)
1 0 SORT (GROUP BY) (Cost=15 Card=9 Bytes=576)
2 1 HASH JOIN (Cost=10 Card=598 Bytes=38272)
3 2 VIEW (Cost=3 Card=100 Bytes=700)
4 3 SORT (UNIQUE) (Cost=3 Card=100 Bytes=700)
5 4 INDEX (FULL SCAN) OF 'SYS_IOT_TOP_7833' (UNIQUE)
6 2 HASH JOIN (Cost=7 Card=598 Bytes=34086)
7 6 VIEW (Cost=4 Card=19 Bytes=133)
8 7 SORT (UNIQUE) (Cost=4 Card=19 Bytes=133)
9 8 INDEX (FAST FULL SCAN) OF 'SYS_IOT_TOP_7828'
10 6 TABLE ACCESS (FULL) OF 'MV_SALES' (Cost=2 Card=409
Statistics
----------------------------------------------------------
364 recursive calls
0 db block gets
88 consistent gets
0 physical reads
Roby@XUE> set autot trace
Roby@XUE> select customer_hierarchy.region,
2 time_hierarchy.qtr_yyyy,
3 sum(sales.sales_amount) sales_amount
4 from sales, time_hierarchy, customer_hierarchy
5 where sales.trans_date = time_hierarchy.day
6 and sales.cust_id = customer_hierarchy.cust_id
7 group by customer_hierarchy.region, time_hierarchy.qtr_yyyy;
27 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=23 Card=22 Bytes=154
1 0 SORT (GROUP BY) (Cost=23 Card=22 Bytes=1540)
2 1 HASH JOIN (Cost=11 Card=1447 Bytes=101290)
3 2 VIEW (Cost=3 Card=100 Bytes=700)
4 3 SORT (UNIQUE) (Cost=3 Card=100 Bytes=700)
5 4 INDEX (FULL SCAN) OF 'SYS_IOT_TOP_7833' (UNIQUE) (
6 2 HASH JOIN (Cost=7 Card=1447 Bytes=91161)
7 6 VIEW (Cost=4 Card=46 Bytes=598)
8 7 SORT (UNIQUE) (Cost=4 Card=46 Bytes=598)
9 8 INDEX (FAST FULL SCAN) OF 'SYS_IOT_TOP_7828' (UN
10 6 TABLE ACCESS (FULL) OF 'MV_SALES' (Cost=2 Card=409 B
Statistics
----------------------------------------------------------
10 recursive calls
0 db block gets
19 consistent gets
0 physical reads
Roby@XUE> select customer_hierarchy.region,
2 time_hierarchy.mon_yyyy,
3 sum(sales.sales_amount) sales_amount
4 from sales, time_hierarchy, customer_hierarchy
5 where sales.trans_date = time_hierarchy.day
6 and sales.cust_id = customer_hierarchy.cust_id
7 group by customer_hierarchy.region, time_hierarchy.mon_yyyy;
75 rows selected.
Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE (Cost=41 Card=56 Bytes=386
1 0 SORT (GROUP BY) (Cost=41 Card=56 Bytes=3864)
2 1 HASH JOIN (Cost=11 Card=3775 Bytes=260475)
3 2 VIEW (Cost=4 Card=120 Bytes=1440)
4 3 SORT (UNIQUE) (Cost=4 Card=120 Bytes=1440)
5 4 INDEX (FAST FULL SCAN) OF 'SYS_IOT_TOP_7828' (UNIQ
6 2 HASH JOIN (Cost=6 Card=409 Bytes=23313)
7 6 VIEW (Cost=3 Card=100 Bytes=700)
8 7 SORT (UNIQUE) (Cost=3 Card=100 Bytes=700)
9 8 INDEX (FULL SCAN) OF 'SYS_IOT_TOP_7833' (UNIQUE)
10 6 TABLE ACCESS (FULL) OF 'MV_SALES' (Cost=2 Card=409 B
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
14 consistent gets
0 physical reads
参考：Tomates Kyte 《Expert One-on-One Oracle》
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