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
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
@implicitNotFound("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.")
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 and Array to contain a collection of `T`. */
  def clsTag: ClassTag[T]
}