Prateek Jain :
I have a particular use case where I have multiple rows for same customer where each row object looks like:
root
-c1: BigInt
-c2: String
-c3: Double
-c4: Double
-c5: Map[String, Int]
Now I have do group by column c1 and collect all the rows as list for same customer like:
c1, [Row1, Row3, Row4]
c2, [Row2, Row5]
I tried doing this ways
dataset.withColumn("combined", array("c1","c2","c3","c4","c5")).groupBy("c1").agg(collect_list("combined")) but I get an exception:
Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'array(`c1`, `c2`, `c3`, `c4`, `c5`)' due to data type mismatch: input to function array should all be the same type, but it's [bigint, string, double, double, map<string,map<string,double>>];;
Ramesh Maharjan :
Instead of array you can use struct function to combine the columns and use groupBy and collect_list aggregation function as
import org.apache.spark.sql.functions._
df.withColumn("combined", struct("c1","c2","c3","c4","c5"))
.groupBy("c1").agg(collect_list("combined").as("combined_list"))
.show(false)
so that you have grouped dataset with schema as
root
|-- c1: integer (nullable = false)
|-- combined_list: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- c1: integer (nullable = false)
| | |-- c2: string (nullable = true)
| | |-- c3: string (nullable = true)
| | |-- c4: string (nullable = true)
| | |-- c5: map (nullable = true)
| | | |-- key: string
| | | |-- value: integer (valueContainsNull = false)
I hope the answer is helpful
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