Spark Dataframe Window 基于多列的滞后函数

Question

val df = sc.parallelize(Seq((201601, 100.5),
  (201602, 120.6),
  (201603, 450.2),
  (201604, 200.7),
  (201605, 121.4))).toDF("date", "volume")

val w = org.apache.spark.sql.expressions.Window.orderBy("date")    
val leadDf = df.withColumn("new_col", lag("volume", 1, 0).over(w))
leadDf.show()

+------+------+-------+
|  date|volume|new_col|
+------+------+-------+
|201601| 100.5|    0.0|
|201602| 120.6|  100.5|
|201603| 450.2|  120.6|
|201604| 200.7|  450.2|
|201605| 121.4|  200.7|
+------+------+-------+

这工作正常。

但是如果我像下面这样多一栏作为领土。

val df = sc.parallelize(Seq((201601, ter1, 10.1),
  (201601, ter2, 10.6),
  (201602, ter1, 10.7),
  (201603, ter3, 10.8),
  (201603, ter4, 10.8),
  (201603, ter3, 10.8),
  (201604, ter4, 10.9))).toDF("date", "territory", "volume")

我的要求是同一地区，我想找到上个月的交易量（如果存在）如果不存在就赋值0.0

Answer 1

如果我没理解错的话，您需要同一地区的前一个日期的值。

如果是这样，那么只需添加 partitionBy，即重新定义您的 window 规范如下：

val w = org.apache.spark.sql.expressions.Window.partitionBy("territory").orderBy("date")

Spark Dataframe Window 基于多列的滞后函数

Spark Dataframe Window lag function based on multiple columns

window-functions

apache-spark

apache-spark-sql

spark-dataframe