How to use groupBy to collect rows into a map? - apache-spark

Question

Context
sqlContext.sql(s"""
SELECT
school_name,
name,
age
FROM my_table
""")
Ask
Given the above table, I would like to group by school name and collect name, age into a Map[String, Int]
For example - Pseudo-code
val df = sqlContext.sql(s"""
SELECT
school_name,
age
FROM my_table
GROUP BY school_name
""")
------------------------
school_name | name | age
------------------------
school A | "michael"| 7
school A | "emily" | 5
school B | "cathy" | 10
school B | "shaun" | 5
df.groupBy("school_name").agg(make_map)
------------------------------------
school_name | map
------------------------------------
school A | {"michael": 7, "emily": 5}
school B | {"cathy": 10, "shaun": 5}

Answer 1

Following will work with Spark 2.0. You can use map function available since 2.0 release to get columns as Map.
val df1 = df.groupBy(col("school_name")).agg(collect_list(map($"name",$"age")) as "map")
df1.show(false)
This will give you below output.
+-----------+------------------------------------+
|school_name|map |
+-----------+------------------------------------+
|school B |[Map(cathy -> 10), Map(shaun -> 5)] |
|school A |[Map(michael -> 7), Map(emily -> 5)]|
+-----------+------------------------------------+
Now you can use UDF to join individual Maps into single Map like below.
import org.apache.spark.sql.functions.udf
val joinMap = udf { values: Seq[Map[String,Int]] => values.flatten.toMap }
val df2 = df1.withColumn("map", joinMap(col("map")))
df2.show(false)
This will give required output with Map[String,Int].
+-----------+-----------------------------+
|school_name|map |
+-----------+-----------------------------+
|school B |Map(cathy -> 10, shaun -> 5) |
|school A |Map(michael -> 7, emily -> 5)|
+-----------+-----------------------------+
If you want to convert a column value into JSON String then Spark 2.1.0 has introduced to_json function.
val df3 = df2.withColumn("map",to_json(struct($"map")))
df3.show(false)
The to_json function will return following output.
+-----------+-------------------------------+
|school_name|map |
+-----------+-------------------------------+
|school B |{"map":{"cathy":10,"shaun":5}} |
|school A |{"map":{"michael":7,"emily":5}}|
+-----------+-------------------------------+

Answer 2

As of spark 2.4 you can use map_from_arrays function to achieve this.
val df = spark.sql(s"""
SELECT *
FROM VALUES ('s1','a',1),('s1','b',2),('s2','a',1)
AS (school, name, age)
""")
val df2 = df.groupBy("school").agg(map_from_arrays(collect_list($"name"), collect_list($"age")).as("map"))
+------+----+---+
|school|name|age|
+------+----+---+
| s1| a| 1|
| s1| b| 2|
| s2| a| 1|
+------+----+---+
+------+----------------+
|school| map|
+------+----------------+
| s2| [a -> 1]|
| s1|[a -> 1, b -> 2]|
+------+----------------+

Answer 3

df.select($"school_name",concat_ws(":",$"age",$"name").as("new_col")).groupBy($"school_name").agg(collect_set($"new_col")).show
+-----------+--------------------+
|school_name|collect_set(new_col)|
+-----------+--------------------+
| school B| [5:shaun, 10:cathy]|
| school A|[7:michael, 5:emily]|
+-----------+--------------------+