public interface Encoder<T>
extends scala.Serializable
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.
| 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. | 
StructType schema()
scala.reflect.ClassTag<T> clsTag()
T.