I would like to sum (or perform other aggregate functions too) on the array column using SparkSQL.
I have a table as
+-------+-------+-
A possible approach it to use explode() on your Array column and consequently aggregate the output by unique key. For example:
import sqlContext.implicits._
import org.apache.spark.sql.functions._
(mytable
.withColumn("emp_sum",
explode($"emp_details"))
.groupBy("dept_nm")
.agg(sum("emp_sum")).show)
+-------+------------+
|dept_nm|sum(emp_sum)|
+-------+------------+
|Finance| 1500|
| IT| 180|
+-------+------------+
To select only specific values in your array, we can work with the answer from the linked question and apply it with a slight modification:
val slice = udf((array : Seq[Int], from : Int, to : Int) => array.slice(from,to))
(mytable
.withColumn("slice",
slice($"emp_details",
lit(0),
lit(3)))
.withColumn("emp_sum",
explode($"slice"))
.groupBy("dept_nm")
.agg(sum("emp_sum")).show)
+-------+------------+
|dept_nm|sum(emp_sum)|
+-------+------------+
|Finance| 600|
| IT| 80|
+-------+------------+
Data:
val data = Seq((10, "Finance", Array(100,200,300,400,500)),
(20, "IT", Array(10,20,50,100)))
val mytable = sc.parallelize(data).toDF("dept_id", "dept_nm","emp_details")