如何在spark中使用窗口函数过滤数据 [英] How to filter data using window functions in spark

问题描述

我有以下数据:

rowid uid time code
   1  1      5    a
   2  1      6    b
   3  1      7    c
   4  2      8    a
   5  2      9    c
   6  2      9    c
   7  2     10    c
   8  2     11    a
   9  2     12    c

现在我想以这样的方式过滤数据，以便我可以删除第 6 行和第 7 行，因为对于特定的 uid，我只想在代码中保留一行值为 'c' 的行

Now I wanted to filter the data in such a way that I can remove the rows 6 and 7 as for a particular uid i want to keep just one row with value 'c' in code

所以预期的数据应该是:

So the expected data should be :

rowid uid time code
   1  1      5    a
   2  1      6    b
   3  1      7    c
   4  2      8    a
   5  2      9    c
   8  2     11    a
   9  2     12    c

我正在使用这样的窗口函数:

I'm using window function something like this :

val window = Window.partitionBy("uid").orderBy("time")
val change = ((lag("code", 1).over(window) <=> "c")).cast("int")

这将帮助我们用代码c"标识每一行.我可以扩展它以过滤掉行以获得预期的数据

This would help us identify each row with a code 'c'. Can i extend this to filter out rows to get the expected data

推荐答案

如果您只想删除 code = "c" 的行(每个 uid 的第一个除外)，您可以尝试以下操作:

If you want to remove only the lines where code = "c" (except the first one for each uid) you could try the following:

val window = Window.partitionBy("uid", "code").orderBy("time")
val result = df
  .withColumn("rank", row_number().over(window))
  .where(
    (col("code") !== "c") ||
    col("rank") === 1
  )
  .drop("rank")

根据新信息进行

val window = Window.partitionBy("uid").orderBy("time")
val result = df
  .withColumn("lagValue", coalesce(lag(col("code"), 1).over(window), lit("")))
  .where(
    (col("code") !== "c") ||
    (col("lagValue") !== "c")
  )
  .drop("lagValue")

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