通过组合类型和子类型的Apache Spark组

问题描述

我有这个数据集，

val sales = Seq(
  ("Warsaw", 2016, "facebook","share",100),
  ("Warsaw", 2017, "facebook","like",200),
  ("Boston", 2015,"twitter","share",50),
  ("Boston", 2016,"facebook","share",150),
  ("Toronto", 2017,"twitter","like",50)
).toDF("city", "year","media","action","amount")

我现在可以按城市和类似媒体将其分组，

I can now group this by city and media like this,

val groupByCityAndYear = sales
  .groupBy("city", "media")
  .count()
groupByCityAndYear.show()

+-------+--------+-----+
|   city|   media|count|
+-------+--------+-----+
| Boston|facebook|    1|
| Boston| twitter|    1|
|Toronto| twitter|    1|
| Warsaw|facebook|    2|
+-------+--------+-----+

但是，我如何将媒体和动作结合在一栏中，所以预期的输出应该是

But, how can I do combine media and action together in one column, so the expected output should be,

+-------+--------+-----+
| Boston|facebook|    1|
| Boston| share  |    2|
| Boston| twitter|    1|
|Toronto| twitter|    1|
|Toronto| like   |    1|
| Warsaw|facebook|    2|
| Warsaw|share   |    1|
| Warsaw|like    |    1|
+-------+--------+-----+

推荐答案

合并 media 和 action 列为 array 列，爆炸，然后执行 groupBy count :

Combine media and action columns as array column, explode it, then do groupBy count:

sales.select(
    $"city", explode(array($"media", $"action")).as("mediaAction")
).groupBy("city", "mediaAction").count().show()

+-------+-----------+-----+
|   city|mediaAction|count|
+-------+-----------+-----+
| Boston|      share|    2|
| Boston|   facebook|    1|
| Warsaw|      share|    1|
| Boston|    twitter|    1|
| Warsaw|       like|    1|
|Toronto|    twitter|    1|
|Toronto|       like|    1|
| Warsaw|   facebook|    2|
+-------+-----------+-----+

或者假设 media 和 action 不相交(这两列没有共同的元素):

Or assuming media and action doesn't intersect (the two columns don't have common elements):

sales.groupBy("city", "media").count().union(
    sales.groupBy("city", "action").count()
).show
+-------+--------+-----+
|   city|   media|count|
+-------+--------+-----+
| Boston|facebook|    1|
| Boston| twitter|    1|
|Toronto| twitter|    1|
| Warsaw|facebook|    2|
| Boston|   share|    2|
| Warsaw|   share|    1|
| Warsaw|    like|    1|
|Toronto|    like|    1|
+-------+--------+-----+

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