Experimental

org.apache.spark.sql

Interface Encoder<T>

All Superinterfaces:

java.io.Serializable

public interface Encoder<T>
extends scala.Serializable

Used to convert a JVM object of type T to and from the internal Spark SQL representation.

== Scala == Encoders are generally created automatically through implicits from a SparkSession, or can be explicitly created by calling static methods on Encoders.

   import spark.implicits._

   val ds = Seq(1, 2, 3).toDS() // implicitly provided (spark.implicits.newIntEncoder)
 

== Java == Encoders are specified by calling static methods on Encoders.

   List<String> data = Arrays.asList("abc", "abc", "xyz");
   Dataset<String> ds = context.createDataset(data, Encoders.STRING());
 

Encoders can be composed into tuples:

   Encoder<Tuple2<Integer, String>> encoder2 = Encoders.tuple(Encoders.INT(), Encoders.STRING());
   List<Tuple2<Integer, String>> data2 = Arrays.asList(new scala.Tuple2(1, "a");
   Dataset<Tuple2<Integer, String>> ds2 = context.createDataset(data2, encoder2);
 

Or constructed from Java Beans:

   Encoders.bean(MyClass.class);
 

== Implementation == - Encoders are not required to be thread-safe and thus they do not need to use locks to guard against concurrent access if they reuse internal buffers to improve performance.

Since:

1.6.0

Method Summary

Modifier and Type	Method and Description
scala.reflect.ClassTag<T>	clsTag() A ClassTag that can be used to construct an Array to contain a collection of T.
StructType	schema() Returns the schema of encoding this type of object as a Row.

Method Detail

clsTag

scala.reflect.ClassTag<T> clsTag()

A ClassTag that can be used to construct an Array to contain a collection of T.

Returns:

(undocumented)

schema

StructType schema()

Returns the schema of encoding this type of object as a Row.