Scala Spark Join Dataframe in loop - scala

Question

I am trying to join DataFrames on the fly in loop. I am using a properties file to get the column details to use in the final data frame.
Properties file -
a01=status:single,perm_id:multi
a02=status:single,actv_id:multi
a03=status:single,perm_id:multi,actv_id:multi
............................
............................
For each row in the properties file, I need to create a DataFrame and save it in a file. Loading the properties file using PropertiesReader. if the mode is single then I need to get only the column value from the table. But if multi, then I need to get the list of values.
val propertyColumn = properties.get("a01") //a01 value we are getting as an argument. This might be a01,a02 or a0n
val columns = propertyColumn.toString.split(",").map(_.toString)
act_det table -
+-------+--------+-----------+-----------+-----------+------------+
|id |act_id |status |perm_id |actv_id | debt_id |
+-------+--------+-----------+-----------+-----------+------------+
| 1 |1 | 4 | 1 | 10 | 1 |
+-------+--------+-----------+-----------+-----------+------------+
| 2 |1 | 4 | 2 | 20 | 2 |
+-------+--------+-----------+-----------+-----------+------------+
| 3 |1 | 4 | 3 | 30 | 1 |
+-------+--------+-----------+-----------+-----------+------------+
| 4 |2 | 4 | 5 | 10 | 3 |
+-------+--------+-----------+-----------+-----------+------------+
| 5 |2 | 4 | 6 | 20 | 1 |
+-------+--------+-----------+-----------+-----------+------------+
| 6 |2 | 4 | 7 | 30 | 1 |
+-------+--------+-----------+-----------+-----------+------------+
| 7 |3 | 4 | 1 | 10 | 3 |
+-------+--------+-----------+-----------+-----------+------------+
| 8 |3 | 4 | 5 | 20 | 1 |
+-------+--------+-----------+-----------+-----------+------------+
| 9 |3 | 4 | 2 | 30 | 3 |
+-------+--------+-----------+-----------+------------+-----------+
Main DataFrame -
val data = sqlContext.sql("select * from act_det")
I want the following output -
For a01 -
+-------+--------+-----------+
|act_id |status |perm_id |
+-------+--------+-----------+
| 1 | 4 | [1,2,3] |
+-------+--------+-----------+
| 2 | 4 | [5,6,7] |
+-------+--------+-----------+
| 3 | 4 | [1,5,2] |
+-------+--------+-----------+
For a02 -
+-------+--------+-----------+
|act_id |status |actv_id |
+-------+--------+-----------+
| 1 | 4 | [10,20,30]|
+-------+--------+-----------+
| 2 | 4 | [10,20,30]|
+-------+--------+-----------+
| 3 | 4 | [10,20,30]|
+-------+--------+-----------+
For a03 -
+-------+--------+-----------+-----------+
|act_id |status |perm_id |actv_id |
+-------+--------+-----------+-----------+
| 1 | 4 | [1,2,3] |[10,20,30] |
+-------+--------+-----------+-----------+
| 2 | 4 | [5,6,7] |[10,20,30] |
+-------+--------+-----------+-----------+
| 3 | 4 | [1,5,2] |[10,20,30] |
+-------+--------+-----------+-----------+
But the data frame creation process should be dynamic.
I have tried below code but I am not able to implement the join logic for the DataFrames in loop.
val finalDF:DataFrame = ??? //empty dataframe
for {
column <- columns
} yeild {
val eachColumn = column.toString.split(":").map(_.toString)
val columnName = eachColumn(0)
val mode = eachColumn(1)
if(mode.equalsIgnoreCase("single")) {
data.select($"act_id", $"status").distinct
//I want to join finalDF with data.select($"act_id", $"status").distinct
} else if(mode.equalsIgnoreCase("multi")) {
data.groupBy($"act_id").agg(collect_list($"perm_id").as("perm_id"))
//I want to join finalDF with data.groupBy($"act_id").agg(collect_list($"perm_id").as("perm_id"))
}
}
Any advice or guidance would be greatly appreciated.

Answer 1

Check below code.
scala> df.show(false)
+---+------+------+-------+-------+-------+
|id |act_id|status|perm_id|actv_id|debt_id|
+---+------+------+-------+-------+-------+
|1 |1 |4 |1 |10 |1 |
|2 |1 |4 |2 |20 |2 |
|3 |1 |4 |3 |30 |1 |
|4 |2 |4 |5 |10 |3 |
|5 |2 |4 |6 |20 |1 |
|6 |2 |4 |7 |30 |1 |
|7 |3 |4 |1 |10 |3 |
|8 |3 |4 |5 |20 |1 |
|9 |3 |4 |2 |30 |3 |
+---+------+------+-------+-------+-------+
Defining primary keys
scala> val primary_key = Seq("act_id").map(col(_))
primary_key: Seq[org.apache.spark.sql.Column] = List(act_id)
Configs
scala> configs.foreach(println)
/*
(a01,status:single,perm_id:multi)
(a02,status:single,actv_id:multi)
(a03,status:single,perm_id:multi,actv_id:multi)
*/
Constructing Expression.
scala>
val columns = configs
.map(c => {
c._2
.split(",")
.map(c => {
val cc = c.split(":");
if(cc.tail.contains("single"))
first(col(cc.head)).as(cc.head)
else
collect_list(col(cc.head)).as(cc.head)
}
)
})
/*
columns: scala.collection.immutable.Iterable[Array[org.apache.spark.sql.Column]] = List(
Array(first(status, false) AS `status`, collect_list(perm_id) AS `perm_id`),
Array(first(status, false) AS `status`, collect_list(actv_id) AS `actv_id`),
Array(first(status, false) AS `status`, collect_list(perm_id) AS `perm_id`, collect_list(actv_id) AS `actv_id`)
)
*/
Final Result
scala> columns.map(c => df.groupBy(primary_key:_*).agg(c.head,c.tail:_*)).map(_.show(false))
+------+------+---------+
|act_id|status|perm_id |
+------+------+---------+
|3 |4 |[1, 5, 2]|
|1 |4 |[1, 2, 3]|
|2 |4 |[5, 6, 7]|
+------+------+---------+
+------+------+------------+
|act_id|status|actv_id |
+------+------+------------+
|3 |4 |[10, 20, 30]|
|1 |4 |[10, 20, 30]|
|2 |4 |[10, 20, 30]|
+------+------+------------+
+------+------+---------+------------+
|act_id|status|perm_id |actv_id |
+------+------+---------+------------+
|3 |4 |[1, 5, 2]|[10, 20, 30]|
|1 |4 |[1, 2, 3]|[10, 20, 30]|
|2 |4 |[5, 6, 7]|[10, 20, 30]|
+------+------+---------+------------+