这就是我的想法,但我不确定它是否适合视图。
select
the_date,
sum(decode(the_type,'S',the_count,0)) samples,
sum(decode(the_type,'R',the_count,0)) receipts,
sum(decode(the_type,'C',the_count,0)) completions,
sum(decode(the_type,'A',the_count,0)) authorizations
from(
select
trunc(sampled_on,'HH24') the_date,
'S' the_type,
count(1) the_count
FROM sample
group by trunc(sampled_on,'HH24')
union all
select
trunc(received_on,'HH24'),
'R',
count(1)
FROM sample
group by trunc(received_on,'HH24')
union all
select
trunc(completed_on,'HH24'),
'C',
count(1)
FROM sample
group by trunc(completed_on,'HH24')
union all
select
trunc(authorized_on,'HH24'),
'A',
count(1)
FROM sample
group by trunc(authorized_on,'HH24')
)
group by the_date
然后,要查询,您可以使用正常的日期结构进行查询:
select * from magic_view where the_date > sysdate-1;
编辑
好的,所以我创建了一个示例表并做了一些指标:
create table sample (
sample_id number primary key,
sampled_on date,
received_on date,
completed_on date,
authorized_on date
);
insert into sample (
select
level,
trunc(sysdate) + dbms_random.value(0,2),
trunc(sysdate) + dbms_random.value(0,2),
trunc(sysdate) + dbms_random.value(0,2),
trunc(sysdate) + dbms_random.value(0,2),
from dual
connect by level <= 1000
);
解释计划是:
---------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|
---------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 4000 | 97K| 25 (20)|
| 1 | HASH GROUP BY | | 4000 | 97K| 25 (20)|
| 2 | VIEW | | 4000 | 97K| 24 (17)|
| 3 | UNION-ALL | | | | |
| 4 | HASH GROUP BY | | 1000 | 9000 | 6 (17)|
| 5 | TABLE ACCESS FULL| SAMPLE | 1000 | 9000 | 5 (0)|
| 6 | HASH GROUP BY | | 1000 | 9000 | 6 (17)|
| 7 | TABLE ACCESS FULL| SAMPLE | 1000 | 9000 | 5 (0)|
| 8 | HASH GROUP BY | | 1000 | 9000 | 6 (17)|
| 9 | TABLE ACCESS FULL| SAMPLE | 1000 | 9000 | 5 (0)|
| 10 | HASH GROUP BY | | 1000 | 9000 | 6 (17)|
| 11 | TABLE ACCESS FULL| SAMPLE | 1000 | 9000 | 5 (0)|
---------------------------------------------------------------------
在我的机器上,过去 24 小时内针对此视图的查询在 23 毫秒内完成。不错,但它只有 1,000 行。在打折 4 个单独的查询之前,您需要对各个解决方案进行性能分析。