Oracle Dimension 下

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》