Spark:基于列值的行过滤器

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我有数百万行这样的数据框:

val df = Seq(("id1", "ACTIVE"), ("id1", "INACTIVE"), ("id1", "INACTIVE"), ("id2", "ACTIVE"), ("id3", "INACTIVE"), ("id3", "INACTIVE")).toDF("id", "status")

scala> df.show(false)
+---+--------+
|id |status  |
+---+--------+
|id1|ACTIVE  |
|id1|INACTIVE|
|id1|INACTIVE|
|id2|ACTIVE  |
|id3|INACTIVE|
|id3|INACTIVE|
+---+--------+

现在,我想将此数据分为三个单独的dataFrame,如下所示:

  1. 仅使用active id(例如id2),例如activeDF
  2. 仅使用无效ID(例如ID3),例如inactiveDF
  3. bothDF表示同时具有活动和不活动状态

如何计算activeDFinactiveDF

我知道bothDF可以像

df.select("id").distinct.except(activeDF).except(inactiveDF)

,但这将涉及改组(因为“区别”操作需要相同)。有没有更好的方法来计算两者

版本:

Spark : 2.2.1
Scala : 2.11
CSReddy贪婪地

另一种方式-groupBy,按集合收集,然后如果集合的大小为1,则它仅是活动的或不活动的,否则两者

scala> val df = Seq(("id1", "ACTIVE"), ("id1", "INACTIVE"), ("id1", "INACTIVE"), ("id2", "ACTIVE"), ("id3", "INACTIVE"), ("id3", "INACTIVE"), ("id4", "ACTIVE"), ("id5", "ACTIVE"), ("id6", "INACTIVE"), ("id7", "ACTIVE"), ("id7", "INACTIVE")).toDF("id", "status")
df: org.apache.spark.sql.DataFrame = [id: string, status: string]

scala> df.show(false)
+---+--------+
|id |status  |
+---+--------+
|id1|ACTIVE  |
|id1|INACTIVE|
|id1|INACTIVE|
|id2|ACTIVE  |
|id3|INACTIVE|
|id3|INACTIVE|
|id4|ACTIVE  |
|id5|ACTIVE  |
|id6|INACTIVE|
|id7|ACTIVE  |
|id7|INACTIVE|
+---+--------+


scala> val allstatusDF = df.groupBy("id").agg(collect_set("status") as "allstatus")
allstatusDF: org.apache.spark.sql.DataFrame = [id: string, allstatus: array<string>]

scala> allstatusDF.show(false)
+---+------------------+
|id |allstatus         |
+---+------------------+
|id7|[ACTIVE, INACTIVE]|
|id3|[INACTIVE]        |
|id5|[ACTIVE]          |
|id6|[INACTIVE]        |
|id1|[ACTIVE, INACTIVE]|
|id2|[ACTIVE]          |
|id4|[ACTIVE]          |
+---+------------------+


scala> allstatusDF.withColumn("status", when(size($"allstatus") === 1, $"allstatus".getItem(0)).otherwise("BOTH")).show(false)
+---+------------------+--------+
|id |allstatus         |status  |
+---+------------------+--------+
|id7|[ACTIVE, INACTIVE]|BOTH    |
|id3|[INACTIVE]        |INACTIVE|
|id5|[ACTIVE]          |ACTIVE  |
|id6|[INACTIVE]        |INACTIVE|
|id1|[ACTIVE, INACTIVE]|BOTH    |
|id2|[ACTIVE]          |ACTIVE  |
|id4|[ACTIVE]          |ACTIVE  |
+---+------------------+--------+

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