org.apache.spark.sql.Encoder.scala Maven / Gradle / Ivy
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql
import scala.annotation.implicitNotFound
import scala.reflect.ClassTag
import org.apache.spark.annotation.{Experimental, InterfaceStability}
import org.apache.spark.sql.types._
/**
* :: Experimental ::
* 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 data = Arrays.asList("abc", "abc", "xyz");
* Dataset ds = context.createDataset(data, Encoders.STRING());
* }}}
*
* Encoders can be composed into tuples:
*
* {{{
* Encoder> encoder2 = Encoders.tuple(Encoders.INT(), Encoders.STRING());
* List> data2 = Arrays.asList(new scala.Tuple2(1, "a");
* Dataset> 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
*/
@Experimental
@InterfaceStability.Evolving
@implicitNotFound("Unable to find encoder for type ${T}. An implicit Encoder[${T}] is needed to " +
"store ${T} instances 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.")
trait Encoder[T] extends Serializable {
/** Returns the schema of encoding this type of object as a Row. */
def schema: StructType
/**
* A ClassTag that can be used to construct an Array to contain a collection of `T`.
*/
def clsTag: ClassTag[T]
}