From the Spark perspective value
is just a byte sequence. It has no knowledge about the serialization format or content. To be able to extract the filed you have to parse it first.
If data is serialized as a JSON string you have two options. You can cast
value
to StringType
and use from_json
and provide a schema:
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions.from_json
val schema: StructType = StructType(Seq(
StructField("column1", ???),
StructField("column2", ???)
))
rawKafkaDF.select(from_json($"value".cast(StringType), schema))
or cast
to StringType
, extract fields by path using get_json_object
:
import org.apache.spark.sql.functions.get_json_object
val columns: Seq[String] = ???
val exprs = columns.map(c => get_json_object($"value", s"$$.$c"))
rawKafkaDF.select(exprs: _*)
and cast
later to the desired types.