QDBase SPL

SELECT MAX(ContinuousDays)
FROM (SELECT COUNT(*) ContinuousDays
    FROM (SELECT SUM(UpDownTag) OVER ( ORDER BY TradeDate) NoRisingDays
        FROM (SELECT TradeDate,
            	CASE WHEN Price>LAG(price) OVER ( ORDER BY TradeDate)
                	THEN 0 ELSE 1 END UpDownTag
            FROM Stock ) )
    GROUP BY NoRisingDays )

1、复杂有序计算：用户行为转换漏斗分析

• 计算每个事件（页面浏览、搜索、加购物车、下单、付款等）后的用户流失率
• 多个事件在指定时间窗口内完成、按指定次序发生才有效，SQL难以实现，更难优化

2、多步骤大数据量跑批

• 复杂业务需求难以直接用SQL完成，游标读数计算慢，且难以并行，浪费计算资源
• 存储过程实现要几千行数十步，伴随中间结果反复落地，跑批时间窗口内完不成

3、大数据上多指标计算，反复用关联多

• 一次完成数百个指标的计算，多次使用明细数据，期间还涉及关联，SQL需要反复遍历
• 大表关联、条件过滤、分组汇总、去重计数混合运算，伴随高并发实时计算

with e1 as (
    select uid,1 as step1,min(etime) as t1
    from event
    where etime>= to_date('2021-01-10') and etime < to_date('2021-01-25')
         and eventtype='eventtype1' and …
    group by 1),
e2 as (
    select uid,1 as step2,min(e1.t1) as t1,min(e2.etime) as t2
    from event as e2
    inner join e1 on e2.uid = e1.uid
    where e2.etime>= to_date('2021-01-10') and e2.etime < to_date('2021-01-25') 
         and e2.etime > t1 and e2.etime < t1 + 7
         and eventtype='eventtype2' and …
    group by 1),
e3 as (
    select uid,1 as step3,min(e2.t1) as t1,min(e3.etime) as t3
    from event as e3
    inner join e2 on e3.uid = e2.uid
    where e3.etime>= to_date('2021-01-10') and e3.etime < to_date('2021-01-25')
         and e3.etime > t2 and e3.etime < t1 + 7
         and eventtype='eventtype3' and …
    group by 1)
select
    sum(step1) as step1,
    sum(step2) as step2,
    sum(step3) as step3
from e1
    left join e2 on e1.uid = e2.uid
    left join e3 on e2.uid = e3.uid

QDBase 技术架构

专门设计的语法体系

SPL特别适合复杂过程运算

超强集成性

QDBase采用Java开发，可独立运行，也可无缝集成到应用