为什么用了大牌工具后报表开发依然头痛
JAVA小咖秀
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·
2022-08-01 07:52
没完没了的报表加剧头疼
集算器做数据准备写的快算的快
![](https://filescdn.proginn.com/6a3b64d44a4c391e5835c960f2bc8919/8fb7e380d9de012e64179717e0f21c48.webp)
select code,max(risenum)-1 maxRiseDays from
( select code,count(1) risenum from
(
select code,changeSign,sum(changeSign) over(partition by code order by ddate) unRiseDays from
(
select
code,
ddate,
case when price>=lag(price) over(partition by code order by ddate)
then 0 else 1 end changeSign
from stock_record
)
)
group by code,unRiseDays
)
group by code
having max(risenum) > 5
用开源的集算器去写则简单很多
A | ||
1 | =connect@l("orcl").query@x("select * from stock_record order by ddate") | |
2 | =A1.group(code) | |
3 | =A2.new(code,~.group@i(price < price[-1]).max(~.len())-1:maxrisedays) | 计算每只股票的连续上涨天数 |
4 | =A3.select(maxrisedays>=5) | 选出符合条件的记录 |
2 列出每一个用户最近一次登录间隔
WITH TT AS
(SELECT RANK() OVER(PARTITION BY uid ORDER BY logtime DESC) rk, T.* FROM t_loginT)
SELECT uid,(SELECT TT.logtime FROM TT where TT.uid=TTT.uid and TT.rk=1)
-(SELET TT.logtim FROM TT WHERE TT.uid=TTT.uid and TT.rk=2) interval
FROM t_loginTTTT GROUP BY uid
开源集算器的写法
A | ||
1 | =t_login.groups(uid;top(2,-logtime)) | 最后2个登录记录 |
2 | =A1.new(uid,#2(1).logtime-#2(2).logtime:interval) | 计算间隔 |
一两个难的可能省不了多少时间,常年累月做项目,那么多复杂的计算场景如果都用开源的集算器,能省下多少时间呢
完全工具化应对没完没了
直接用润乾报表更便利
报表直接使用集算器结果做数据集,无缝对接
![](https://filescdn.proginn.com/920762e248c4c0fd73d2cbb4d20e1ca0/7eda9a9abb4e44a613b0a37e8528255c.webp)
![](https://filescdn.proginn.com/1e5851b3b4ac070491499e30601a9d93/af44d396390b1ed1ac4b3b0886814a41.webp)
报表中还能使用集算器函数,提升开发效率
大报表功能,提升报表性能
![](https://filescdn.proginn.com/9989a3bbe18d80e6f6ef4a8bd3042e7e/df9eeb6eb1e4ab9a7d38f6861b9803d6.webp)
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