CODEWITHSUNDEEP
scalaapache-sparkpysparkapache-spark-sql
ds_user
ds_user

Reputation: 2179

Duplicating the record count in apache spark

This is an extension of this question, Apache Spark group by combining types and sub types. 

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")

All good with that solution, however the expected output should be counted in different categories conditionally.

So, the output should look like,

+-------+--------+-----+
| Boston|facebook|    1|
| Boston| share1 |    2|
| Boston| share2 |    2|
| Boston| twitter|    1|
|Toronto| twitter|    1|
|Toronto| like   |    1|
| Warsaw|facebook|    2|
| Warsaw|share1  |    1|
| Warsaw|share2  |    1|
| Warsaw|like    |    1|
+-------+--------+-----+

Here if the action is share, I need to have that counted both in share1 and share2. When I count it programmatically, I use case statement and say case when action is share, share1 = share1 +1 , share2 = share2+1

But how can I do this in Scala or pyspark or sql ?

Upvotes: 0

Views: 64

Answers (1)

Ramesh Maharjan
Ramesh Maharjan

Reputation: 41957

Simple filter and unions should give you your desired output

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

val action = sales.groupBy("city", "action").count().select($"city", $"action".as("media"), $"count")

val share = action.filter($"media" === "share")

  media.union(action.filter($"media" =!= "share"))
      .union(share.withColumn("media", lit("share1")))
      .union(share.withColumn("media", lit("share2")))
      .show(false)

which should give you 

+-------+--------+-----+
|city   |media   |count|
+-------+--------+-----+
|Boston |facebook|1    |
|Boston |twitter |1    |
|Toronto|twitter |1    |
|Warsaw |facebook|2    |
|Warsaw |like    |1    |
|Toronto|like    |1    |
|Boston |share1  |2    |
|Warsaw |share1  |1    |
|Boston |share2  |2    |
|Warsaw |share2  |1    |
+-------+--------+-----+

Upvotes: 1

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