how to concat multiple columns in spark while getting the column names to be concatenated from another table (different for each row) - scala

Question

I am trying to concat multiple columns in spark using concat function.
For example below is the table for which I have to add new concatenated column
table - **t**
+---+----+
| id|name|
+---+----+
| 1| a|
| 2| b|
+---+----+
and below is the table which has the information about which columns are to be concatenated for given id (for id 1 column id and name needs to be concatenated and for id 2 only id)
table - **r**
+---+-------+
| id| att |
+---+-------+
| 1|id,name|
| 2| id |
+---+-------+
if I join the two tables and do something like below, I am able to concat but not based on the table r (as the new column is having 1,a for first row but for second row it should be 2 only)
t.withColumn("new",concat_ws(",",t.select("att").first.mkString.split(",").map(c => col(c)): _*)).show
+---+----+-------+---+
| id|name| att |new|
+---+----+-------+---+
| 1| a|id,name|1,a|
| 2| b| id |2,b|
+---+----+-------+---+
I have to apply filter before the select in the above query, but I am not sure how to do that in withColumn for each row.
Something like below, if that is possible.
t.withColumn("new",concat_ws(",",t.**filter**("id="+this.id).select("att").first.mkString.split(",").map(c => col(c)): _*)).show
As it will require to filter each row based on the id.
scala> t.filter("id=1").select("att").first.mkString.split(",").map(c => col(c))
res90: Array[org.apache.spark.sql.Column] = Array(id, name)
scala> t.filter("id=2").select("att").first.mkString.split(",").map(c => col(c))
res89: Array[org.apache.spark.sql.Column] = Array(id)
Below is the final required result.
+---+----+-------+---+
| id|name| att |new|
+---+----+-------+---+
| 1| a|id,name|1,a|
| 2| b| id |2 |
+---+----+-------+---+

Answer 1

We can use UDF
Requirements for this logic to work.
The column name of your table t should be in same order as it comes in col att of table r
scala> input_df_1.show
+---+----+
| id|name|
+---+----+
| 1| a|
| 2| b|
+---+----+
scala> input_df_2.show
+---+-------+
| id| att|
+---+-------+
| 1|id,name|
| 2| id|
+---+-------+
scala> val join_df = input_df_1.join(input_df_2,Seq("id"),"inner")
join_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 1 more field]
scala> val req_cols = input_df_1.columns
req_cols: Array[String] = Array(id, name)
scala> def new_col_udf = udf((cols : Seq[String],row : String,attr : String) => {
| val row_values = row.split(",")
| val attrs = attr.split(",")
| val req_val = attrs.map{at =>
| val index = cols.indexOf(at)
| row_values(index)
| }
| req_val.mkString(",")
| })
new_col_udf: org.apache.spark.sql.expressions.UserDefinedFunction
scala> val intermediate_df = join_df.withColumn("concat_column",concat_ws(",",'id,'name)).withColumn("new_col",new_col_udf(lit(req_cols),'concat_column,'att))
intermediate_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 3 more fields]
scala> val result_df = intermediate_df.select('id,'name,'att,'new_col)
result_df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 2 more fields]
scala> result_df.show
+---+----+-------+-------+
| id|name| att|new_col|
+---+----+-------+-------+
| 1| a|id,name| 1,a|
| 2| b| id| 2|
+---+----+-------+-------+
Hope it answers your question.

Answer 2

This may be done in a UDF:
val cols: Seq[Column] = dataFrame.columns.map(x => col(x)).toSeq
val indices: Seq[String] = dataFrame.columns.map(x => x).toSeq
val generateNew = udf((values: Seq[Any]) => {
val att = values(indices.indexOf("att")).toString.split(",")
val associatedIndices = indices.filter(x => att.contains(x))
val builder: StringBuilder = StringBuilder.newBuilder
values.filter(x => associatedIndices.contains(values.indexOf(x)))
values.foreach{ v => builder.append(v).append(";") }
builder.toString()
})
val dfColumns = array(cols:_*)
val dNew = dataFrame.withColumn("new", generateNew(dfColumns))
This is just a sketch, but the idea is that you can pass a sequence of items to the user defined function, and select the ones that are needed dynamically.
Note that there are additional types of collection/maps that you can pass - for example How to pass array to UDF