Why is "Unable to find encoder for type stored in a Dataset" when creating a dataset of custom case class?

Spark 2.0 (final) with Scala 2.11.8. The following super simple code yields the compilation error Error:(17, 45) Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for serializing other types will be added in future releases.

import org.apache.spark.sql.SparkSession

case class SimpleTuple(id: Int, desc: String)

object DatasetTest {
  val dataList = List(
    SimpleTuple(5, "abc"),
    SimpleTuple(6, "bcd")
  )

  def main(args: Array[String]): Unit = {
    val sparkSession = SparkSession.builder.
      master("local")
      .appName("example")
      .getOrCreate()

    val dataset = sparkSession.createDataset(dataList)
  }
}

Solution 1:

Spark Datasets require Encoders for data type which is about to be stored. For common types (atomics, product types) there is a number of predefined encoders available but you have to import these first from SparkSession.implicits to make it work:

val sparkSession: SparkSession = ???
import sparkSession.implicits._
val dataset = sparkSession.createDataset(dataList)

Alternatively you can provide directly an explicit

import org.apache.spark.sql.{Encoder, Encoders}

val dataset = sparkSession.createDataset(dataList)(Encoders.product[SimpleTuple])

or implicit

implicit val enc: Encoder[SimpleTuple] = Encoders.product[SimpleTuple]
val dataset = sparkSession.createDataset(dataList)

Encoder for the stored type.

Note that Encoders also provide a number of predefined Encoders for atomic types, and Encoders for complex ones, can derived with ExpressionEncoder.

Further reading:

• For custom objects which are not covered by built-in encoders see How to store custom objects in Dataset?
• For Row objects you have to provide Encoder explicitly as shown in Encoder error while trying to map dataframe row to updated row
• For debug cases, case class must be defined outside of the Main https://stackoverflow.com/a/34715827/3535853

Solution 2:

For other users (yours is correct), note that you it's also important that the case class is defined outside of the object scope. So:

Fails:

object DatasetTest {
  case class SimpleTuple(id: Int, desc: String)

  val dataList = List(
    SimpleTuple(5, "abc"),
    SimpleTuple(6, "bcd")
  )

  def main(args: Array[String]): Unit = {
    val sparkSession = SparkSession.builder
      .master("local")
      .appName("example")
      .getOrCreate()
    val dataset = sparkSession.createDataset(dataList)
  }
}

Add the implicits, still fails with the same error:

object DatasetTest {
  case class SimpleTuple(id: Int, desc: String)

  val dataList = List(
    SimpleTuple(5, "abc"),
    SimpleTuple(6, "bcd")
  )

  def main(args: Array[String]): Unit = {
    val sparkSession = SparkSession.builder
      .master("local")
      .appName("example")
      .getOrCreate()

    import sparkSession.implicits._
    val dataset = sparkSession.createDataset(dataList)
  }
}

Works:

case class SimpleTuple(id: Int, desc: String)

object DatasetTest {   
  val dataList = List(
    SimpleTuple(5, "abc"),
    SimpleTuple(6, "bcd")
  )

  def main(args: Array[String]): Unit = {
    val sparkSession = SparkSession.builder
      .master("local")
      .appName("example")
      .getOrCreate()

    import sparkSession.implicits._
    val dataset = sparkSession.createDataset(dataList)
  }
}

Here's the relevant bug: https://issues.apache.org/jira/browse/SPARK-13540, so hopefully it will be fixed in the next release of Spark 2.

(Edit: Looks like that bugfix is actually in Spark 2.0.0... So I'm not sure why this still fails).