问题:
我想要做的事情的结构是这样的
--Query1 // that returns some fields based on a timerange(ex : Year1 - Year2 )
--Query2 // I need to manipulate this query to output records based on an input
// metric
--Query3 // That joins output from Query1 and Query2
**Existing query:**
WITH const as (
select
/* Constants */
'Formula' as *costfunction* -- Formula is a string which can take
-- the below formulae mentioned above
-- (Formula1 / Formula2/ etc)
),
stats_analysis (
/* fields to return */
)AS(
/* Main select query for Query1 */
),
--Query2 // Basically extracts out top 5 students based on average
top_students
(
stud_id,
metric_value,
metric_name
) AS (
SELECT
stud_id
, /*Calculation for metric*/AS metric_value
, 'marks' AS metric_name
FROM stats_analysis
GROUP BY stud_id
ORDER BY metric_value DESC
limit 5
)
--Query 3
Uses Query1 and Query2 to display final result
尝试实现:基本上,我试图对需要基于不同度量标准(costFunction)执行的查询进行“大小写” 。
P.S:我已在代码中标记*** ***以表示为什么需要 不同的查询
top_students
(
stud_id,
metric_value,
metric_name
)
AS (
SELECT CASE const.costFunction
When 'Formula1' THEN
(
stud_id,
metric_value,
metric_name
) AS (
SELECT
stud_id
, /***Calculation for Formula1***/ AS metric_value
, 'marks' AS metric_name
FROM stats_analysis CROSS JOIN const
GROUP BY stud_id
ORDER BY metric_value ***DESC***
limit 5
)
When 'Formula2' THEN
(
stud_id,
metric_value,
metric_name
) AS (
SELECT
stud_id
, /***Calculation for Formula2***/ AS metric_value
, 'marks' AS metric_name
FROM stats_analysis CROSS JOIN const
GROUP BY stud_id
ORDER BY metric_value ***ASC***
limit 5
)
When 'Formula3' THEN
(
stud_id,
metric_value,
metric_name
) AS (
SELECT
stud_id
, /***Calculation for Formula3***/ AS metric_value
, 'marks' AS metric_name
FROM stats_analysis CROSS JOIN const
GROUP BY stud_id
ORDER BY metric_value ***DESC***
limit 5
)
)
这会使CASE内的AS引发语法错误。我是PG的新手,因此我也愿意采用任何更好的方法来构造此查询。谢谢!
编辑:
样本数据
VideoID | StartTime | EndTime |Views|TotalTime |MinTime | MaxTime
17276 |2018-09-26 20:33:43| 2018-09-26 20:48:43| 90 |554.2757137| 1.104655658| 25.59161658
17276 |2018-09-26 20:48:43| 2018-09-26 21:03:43| 418|3160.102025| 0.973088008| 167.0388009
17276 |2018-09-26 21:18:44| 2018-09-26 21:33:44| 14 |112.5031557| 0.997863734| 29.2182703
29083 |2018-09-26 20:48:43| 2018-09-26 21:03:43| 419|3552.922446| 0.964971822 | 152.9819936
29083 |2018-09-26 20:33:43| 2018-09-26 20:48:43| 90 |541.1001533| 1.316958002| 27.36436251
29083 |2018-09-26 21:33:44| 2018-09-26 21:48:44| 314|758.0945074| 0.013669366| 1.663391002
29083 |2018-09-26 21:33:44| 2018-09-26 21:48:44| 450|3029.140526| 0.969670667| 139.6291586
预期的输出:将根据聚合类型显示 top N条记录,这些记录按VideoId分组,并按照例子。提供的参数:记录数(整数),聚合类型(字符串)
Ex1 : Input = (2,avg)
VideoId | MetricValue
17276 7.33 // Calculated by Sum(Total Time)/Sum(Views)
29083 6.19
Explanation : top 2 by average would mean top 2 with highest avg. i.e:DESC
Ex2 : Input = (1,max)
VideoId | MetricValue
29083 1.31 // Calculated by Max(MaxTime) after grouping by ID
Explanation : top 1 by max would mean top 1 with highest MaxTime. i.e:DESC
Ex3 : Input = (1,min)
VideoId | MetricValue
29083 0.013669366 // Calculated by Min(MinTime) after grouping by ID
Explanation : top 1 by min would mean top 1 with lowest MinTime. i.e:ASC