Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/9190#discussion_r42702909
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -0,0 +1,398 @@
+/*
+ * 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 org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.encoders._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.execution.QueryExecution
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A [[Dataset]] is a strongly typed collection of objects that can be
transformed in parallel
+ * using functional or relational operations.
+ *
+ * A [[Dataset]] differs from an [[RDD]] in the following ways:
+ * - Internally, a [[Dataset]] is represented by a Catalyst logical plan
and the data is stored
+ * in the encoded form. This representation allows for additional
logical operations and
+ * enables many operations (sorting, shuffling, etc.) to be performed
without deserializing to
+ * an object.
+ * - The creation of a [[Dataset]] requires the presence of an explicit
[[Encoder]] that can be
+ * used to serialize the object into a binary format. Encoders are
also capable of mapping the
+ * schema of a given object to the Spark SQL type system. In contrast,
RDDs rely on runtime
+ * reflection based serialization. Operations that change the type of
object stored in the
+ * dataset also need an encoder for the new type.
+ *
+ * A [[Dataset]] can be thought of as a specialized DataFrame, where the
elements map to a specific
+ * JVM object type, instead of to a generic [[Row]] container. A DataFrame
can be transformed into
+ * specific Dataset by calling `df.as[ElementType]`. Similarly you can
transform a strongly-typed
+ * [[Dataset]] to a generic DataFrame by calling `ds.toDF()`.
+ *
+ * COMPATIBILITY NOTE: Long term we plan to make [[DataFrame]] extend
`Dataset[Row]`. However,
+ * making this change to che class hierarchy would break the function
signatures for the existing
+ * functional operations (map, flatMap, etc). As such, this class should
be considered a preview
+ * of the final API. Changes will be made to the interface after Spark
1.6.
+ *
+ * @since 1.6.0
+ */
+@Experimental
+class Dataset[T] private[sql](
+ @transient val sqlContext: SQLContext,
+ @transient val queryExecution: QueryExecution)(
+ implicit val encoder: Encoder[T]) extends Serializable {
+
+ private implicit def classTag = encoder.clsTag
+
+ private[sql] def this(sqlContext: SQLContext, plan:
LogicalPlan)(implicit encoder: Encoder[T]) =
+ this(sqlContext, new QueryExecution(sqlContext, plan))
+
+ /** Returns the schema of the encoded form of the objects in this
[[Dataset]]. */
+ def schema: StructType = encoder.schema
+
+ /* ************* *
+ * Conversions *
+ * ************* */
+
+ /**
+ * Returns a new `Dataset` where each record has been mapped on to the
specified type.
+ * TODO: should bind here...
+ * TODO: document binding rules
+ * @since 1.6.0
+ */
+ def as[U : Encoder]: Dataset[U] = new Dataset(sqlContext,
queryExecution)(implicitly[Encoder[U]])
+
+ /**
+ * Applies a logical alias to this [[Dataset]] that can be used to
disambiguate columns that have
+ * the same name after two Datasets have been joined.
+ */
+ def as(alias: String): Dataset[T] = withPlan(Subquery(alias, _))
+
+ /**
+ * Converts this strongly typed collection of data to generic Dataframe.
In contrast to the
+ * strongly typed objects that Dataset operations work on, a Dataframe
returns generic [[Row]]
+ * objects that allow fields to be accessed by ordinal or name.
+ */
+ def toDF(): DataFrame = DataFrame(sqlContext, logicalPlan)
+
+
+ /**
+ * Returns this Dataset.
+ * @since 1.6.0
+ */
+ def toDS(): Dataset[T] = this
+
+ /**
+ * Converts this Dataset to an RDD.
+ * @since 1.6.0
+ */
+ def rdd: RDD[T] = {
+ val tEnc = implicitly[Encoder[T]]
+ val input = queryExecution.analyzed.output
+ queryExecution.toRdd.mapPartitions { iter =>
+ val bound = tEnc.bind(input)
+ iter.map(bound.fromRow)
+ }
+ }
+
+ /* *********************** *
+ * Functional Operations *
+ * *********************** */
+
+ /**
+ * Concise syntax for chaining custom transformations.
+ * {{{
+ * def featurize(ds: Dataset[T]) = ...
+ *
+ * dataset
+ * .transform(featurize)
+ * .transform(...)
+ * }}}
+ *
+ * @since 1.6.0
+ */
+ def transform[U](t: Dataset[T] => Dataset[U]): Dataset[U] = t(this)
+
+ /**
+ * Returns a new [[Dataset]] that only contains elements where `func`
returns `true`.
+ * @since 1.6.0
+ */
+ def filter(func: T => Boolean): Dataset[T] =
mapPartitions(_.filter(func))
+
+ /**
+ * Returns a new [[Dataset]] that only contains elements where `func`
returns `false`.
+ * @since 1.6.0
+ */
+ def filterNot(func: T => Boolean): Dataset[T] =
mapPartitions(_.filterNot(func))
+
+ /**
+ * Returns a new [[Dataset]] that contains the result of applying `func`
to each element.
+ * @since 1.6.0
+ */
+ def map[U : Encoder](func: T => U): Dataset[U] =
mapPartitions(_.map(func))
+
+ /**
+ * Returns a new [[Dataset]] that contains the result of applying `func`
to each element.
+ * @since 1.6.0
+ */
+ def mapPartitions[U : Encoder](func: Iterator[T] => Iterator[U]):
Dataset[U] = {
+ new Dataset(
+ sqlContext,
+ MapPartitions[T, U](
+ func,
+ implicitly[Encoder[T]],
+ implicitly[Encoder[U]],
+ implicitly[Encoder[U]].schema.toAttributes,
+ logicalPlan))
+ }
+
+ def flatMap[U : Encoder](func: T => TraversableOnce[U]): Dataset[U] =
+ mapPartitions(_.flatMap(func))
+
+ /* ************** *
+ * Side effects *
+ * ************** */
+
+ /**
+ * Runs `func` on each element of this Dataset.
+ * @since 1.6.0
+ */
+ def foreach(func: T => Unit): Unit = rdd.foreach(func)
+
+ /**
+ * Runs `func` on each partition of this Dataset.
+ * @since 1.6.0
+ */
+ def foreachPartition(func: Iterator[T] => Unit): Unit =
rdd.foreachPartition(func)
+
+ /* ************* *
+ * Aggregation *
+ * ************* */
+
+ /**
+ * Reduces the elements of this Dataset using the specified binary
function. The given function
+ * must be commutative and associative or the result may be
non-deterministic.
+ * @since 1.6.0
+ */
+ def reduce(func: (T, T) => T): T = rdd.reduce(func)
+
+ /**
+ * Aggregates the elements of each partition, and then the results for
all the partitions, using a
+ * given associative and commutative function and a neutral "zero value".
+ *
+ * This behaves somewhat differently than the fold operations
implemented for non-distributed
+ * collections in functional languages like Scala. This fold operation
may be applied to
+ * partitions individually, and then those results will be folded into
the final result.
+ * If op is not commutative, then the result may differ from that of a
fold applied to a
+ * non-distributed collection.
+ * @since 1.6.0
+ */
+ def fold(zeroValue: T)(op: (T, T) => T): T = rdd.fold(zeroValue)(op)
+
+ /**
+ * Returns a [[GroupedDataset]] where the data is grouped by the given
key function.
+ * @since 1.6.0
+ */
+ def groupBy[K : Encoder](func: T => K): GroupedDataset[K, T] = {
--- End diff --
should we have a group by that takes a column name or expression?
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