I want a concat function for Spark Sql. I have written a udf as
sqlContext.udf.register("CONCAT",(args:String*)=>{
String out=""
for(arg<-args)
{
out+=arg
}
out
})
sqlContext.sql("select col1,col2,CONCAT(col1,col2) from testtable")
but this udf is not working and I am getting an exception. If I try with fixed number of parameters then it works. I am using spark 1.3.1 and scala 2.10.5.
Has anyone faced this issue or knows a solution for this?
You can do this using the struct function like following:
val myUDF = udf {
(r: Row) => r.toSeq.map(...) // the "r" row contains your arguments
}
val df = ....
df.select(col("col1"), myUDF(struct(col("col2"), col("col3"), col("col4"), ...)))
If all you want is to concatenate columns using raw SQL there is no need for a custom UDF at all. CONCAT function is already there:
val df = sc.parallelize(List(("a", "b", "c"))).toDF("x", "y", "z")
df.registerTempTable("df")
sqlContext.sql("SELECT CONCAT(x, y, z) AS xyz FROM df").show
// +---+
// |xyz|
// +---+
// |abc|
// +---+
Since 1.5.0 you can use concat / concat_ws functions directly:
import org.apache.spark.sql.functions.{concat, concat_ws}
df.select(concat_ws("-", $"x", $"y", $"z").alias("x-y-z")).show
// +-----+
// |x-y-z|
// +-----+
// |a-b-c|
// +-----+
df.select(concat($"x", $"y", $"z").alias("xyz")).show
// +---+
// |xyz|
// +---+
// |abc|
// +---+
See also Spark UDF with varargs
