Question

我有几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：

只有active id（例如id2），例如activeDF
只有无效的ID（例如id3），例如inactiveDF
DF都说，具有“活动”和“不活动”两种状态

如何计算 activeDF 和 inactiveDF ？

我知道 bothDF 的计算方式类似于

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 bothDF

版本：

Spark : 2.2.1
Scala : 2.11

Answer 1

最优雅的解决方案是围绕status

val counts = df
  .groupBy("id")
  .pivot("status", Seq("ACTIVE", "INACTIVE"))
  .count

或等效的直接agg

val counts = df
  .groupBy("id")
  .agg(
    count(when($"status" === "ACTIVE", true)) as "ACTIVE",
    count(when($"status" === "INACTIVE", true)) as "INACTIVE"
  )

后跟一个简单的CASE ... WHEN：

val result = counts.withColumn(
  "status",
  when($"ACTIVE" === 0, "INACTIVE")
    .when($"inactive" === 0, "ACTIVE")
    .otherwise("BOTH")
)

result.show

+---+------+--------+--------+                                                  
| id|ACTIVE|INACTIVE|  status|
+---+------+--------+--------+
|id3|     0|       2|INACTIVE|
|id1|     1|       2|    BOTH|
|id2|     1|       0|  ACTIVE|
+---+------+--------+--------+

稍后，您可以将result与filters分开，或者转储到具有支持partitionBy（How to split a dataframe into dataframes with same column values?）的源的磁盘上。

Answer 2

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，否则都是

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  |
+---+------------------+--------+

Spark：基于列值的行过滤器

2 个答案: