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 should be thread-safe.
scala.reflect.ClassTag<T>
A ClassTag that can be used to construct an Array to contain a collection of T.
Returns the schema of encoding this type of object as a Row.
()
A ClassTag that can be used to construct an Array to contain a collection of T.
Returns the schema of encoding this type of object as a Row.
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