多次spark dataframe groupby

Question

val df = (Seq((1, "a", "10"),(1,"b", "12"),(1,"c", "13"),(2, "a", "14"),
              (2,"c", "11"),(1,"b","12" ),(2, "c", "12"),(3,"r", "11")).
          toDF("col1", "col2", "col3"))

所以我有一个有3列的spark dataframe。

+----+----+----+
|col1|col2|col3|
+----+----+----+
|   1|   a|  10|
|   1|   b|  12|
|   1|   c|  13|
|   2|   a|  14|
|   2|   c|  11|
|   1|   b|  12|
|   2|   c|  12|
|   3|   r|  11|
+----+----+----+

我怎么能做到这一点，有人能解释一下吗？

spark:1.6.2Scala:2.10

Answer 1

一种选择是分别做这两个和，然后将它们合并回来：

(df.groupBy("col1", "col2").agg(sum($"col3").as("sum_level2")).
    join(df.groupBy("col1").agg(sum($"col3").as("sum_level1")), Seq("col1")).show)

+----+----+----------+----------+
|col1|col2|sum_level2|sum_level1|
+----+----+----------+----------+
|   2|   c|      23.0|      37.0|
|   2|   a|      14.0|      37.0|
|   1|   c|      13.0|      47.0|
|   1|   b|      24.0|      47.0|
|   3|   r|      11.0|      11.0|
|   1|   a|      10.0|      47.0|
+----+----+----------+----------+

另一个选择是使用窗口函数，考虑到level1_sum是由col1分组的level2_sum的总和：

import org.apache.spark.sql.expressions.Window
val w = Window.partitionBy($"col1")

(df.groupBy("col1", "col2").agg(sum($"col3").as("sum_level2")).
    withColumn("sum_level1", sum($"sum_level2").over(w)).show)

+----+----+----------+----------+
|col1|col2|sum_level2|sum_level1|
+----+----+----------+----------+
|   1|   c|      13.0|      47.0|
|   1|   b|      24.0|      47.0|
|   1|   a|      10.0|      47.0|
|   3|   r|      11.0|      11.0|
|   2|   c|      23.0|      37.0|
|   2|   a|      14.0|      37.0|
+----+----+----------+----------+