通过更新同列实现滞后函数

Question

我要更新条形码（offset=1）的滞后值到条形码

case 
  when ( lag(barcode,1) over (order by barcode ) 
        and  Datediff(SS, eventdate,lag(next_eventdate,1) over (order by barcode)) < 3*3600 ) 
  THEN 1 
  ELSE 0 
END as FLAG

我已经在 pyspark 上实现了它，但是给我一个错误

from pyspark.sql.functions import col, unix_timestamp
timeDiff = unix_timestamp('eventdate', format="ss")- unix_timestamp(F.lag('next_eventdate', 1), format="ss")
ww= Window.orderBy("barcode") 
Tgt_df_tos = Tgt_df_7.withColumn('FLAG',F.when((F.lag('barcode', 1)) & ( timeDiff <= 10800),"1").otherwise('0'))

我收到错误

AnalysisException: "cannot resolve '(lag(`barcode`, 1, NULL) AND ((unix_timestamp(`eventdate`, 'ss') - unix_timestamp(lag(`next_eventdate`, 1, NULL), 'ss')) <= CAST(10800 AS BIGINT)))' due to data type mismatch: differing types in '(lag(`barcode`, 1, NULL) AND ((unix_timestamp(`eventdate`, 'ss') - unix_timestamp(lag(`next_eventdate`, 1, NULL), 'ss')) <= CAST(10800 AS BIGINT)))' (int and boolean).

Answer 1

我不熟悉 pyspark，但在我看来问题出在 CASE 语句中。

CASE WHEN (
        LAG(barcode,1) OVER (ORDER BY barcode ) 
    AND
        DATEDIFF(SS, eventdate, LAG(next_eventdate, 1) OVER(ORDER BY barcode)) < 3*3600
)

有两种表达方式： "LAG(barcode,1) OVER (ORDER BY barcode )" 计算结果为 INTEGER。

"DATEDIFF(SS, eventdate, LAG(next_eventdate, 1) OVER(ORDER BY barcode)) < 3*3600" 计算结果为布尔值（因为不等式）。

这些表达式与通常用于组合两个布尔表达式的 AND 运算符组合。我相信这是错误的原因。

LAG(barcode,1) OVER (ORDER BY barcode ) 计算结果为整数而非布尔值。

所以表达式看起来像这样：

CASE WHEN (324857 AND True) THEN 1 ELSE 0 END as FLAG

AnalysisException: "cannot resolve .... (int and boolean).

通过更新同列实现滞后函数

Implementation of lag function by updating the same column

tsql

lag

lead

pyspark

pyspark-sql