You can simply use DataFrame API:
import org.apache.spark.sql.functions.{when, lower}
val df = Seq(
(2012, "Tesla", "S"), (1997, "Ford", "E350"),
(2015, "Chevy", "Volt")
).toDF("year", "make", "model")
df.withColumn("make", when(lower($"make") === "tesla", "S").otherwise($"make"))
Use statically typed Dataset in order to use map you should :
import spark.implicits._
case class Record(year: Int, make: String, model: String)
df.as[Record].map {
case tesla if tesla.make.toLowerCase == "tesla" => tesla.copy(make = "S")
case rec => rec
}
In order to map over Dataset[Row], you need to provide required encoder.
Here is how I did it:
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row
// Yup, it would be possible to reuse df.schema here
val schema = StructType(Seq(
StructField("year", IntegerType),
StructField("make", StringType),
StructField("model", StringType)
))
val encoder = RowEncoder(schema)
df.map {
case Row(year, make: String, model) if make.toLowerCase == "tesla" =>
Row(year, "S", model)
case row => row
} (encoder)