// The type of your business objects
case class Person(id: Long, name: String)
// The encoder for Person objects
import org.apache.spark.sql.Encoders
val personEncoder = Encoders.product[Person]
// The expression encoder for Person objects
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
val personExprEncoder = personEncoder.asInstanceOf[ExpressionEncoder[Person]]
// Convert Person objects to InternalRow
scala> val row = personExprEncoder.toRow(Person(0, "Jacek"))
row: org.apache.spark.sql.catalyst.InternalRow = [0,0,1800000005,6b6563614a]
// How many fields are available in Person's InternalRow?
scala> row.numFields
res0: Int = 2
// Are there any NULLs in this InternalRow?
scala> row.anyNull
res1: Boolean = false
// You can create your own InternalRow objects
import org.apache.spark.sql.catalyst.InternalRow
scala> val ir = InternalRow(5, "hello", (0, "nice"))
ir: org.apache.spark.sql.catalyst.InternalRow = [5,hello,(0,nice)]InternalRow — Internal Binary Row Format
There are methods to create InternalRow objects using the factory methods in the InternalRow object.
import org.apache.spark.sql.catalyst.InternalRow
scala> InternalRow.empty
res0: org.apache.spark.sql.catalyst.InternalRow = [empty row]
scala> InternalRow(0, "string", (0, "pair"))
res1: org.apache.spark.sql.catalyst.InternalRow = [0,string,(0,pair)]
scala> InternalRow.fromSeq(Seq(0, "string", (0, "pair")))
res2: org.apache.spark.sql.catalyst.InternalRow = [0,string,(0,pair)]UnsafeRow
UnsafeRow is a mutable internal row that is Externalizable and KryoSerializable.
|
Caution
|
FIXME What does being Externalizable and KryoSerializable mean? What’s the protocol to follow?
|