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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.execution

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._

/**
 * For lazy computing, be sure the generator.terminate() called in the very last
 * TODO reusing the CompletionIterator?
 */
private[execution] sealed case class LazyIterator(func: () => TraversableOnce[InternalRow])
  extends Iterator[InternalRow] {

  lazy val results = func().toIterator
  override def hasNext: Boolean = results.hasNext
  override def next(): InternalRow = results.next()
}

/**
 * Applies a [[Generator]] to a stream of input rows, combining the
 * output of each into a new stream of rows.  This operation is similar to a `flatMap` in functional
 * programming with one important additional feature, which allows the input rows to be joined with
 * their output.
 * @param generator the generator expression
 * @param join  when true, each output row is implicitly joined with the input tuple that produced
 *              it.
 * @param outer when true, each input row will be output at least once, even if the output of the
 *              given `generator` is empty. `outer` has no effect when `join` is false.
 * @param output the output attributes of this node, which constructed in analysis phase,
 *               and we can not change it, as the parent node bound with it already.
 */
case class Generate(
    generator: Generator,
    join: Boolean,
    outer: Boolean,
    output: Seq[Attribute],
    child: SparkPlan)
  extends UnaryNode {

  val boundGenerator = BindReferences.bindReference(generator, child.output)

  protected override def doExecute(): RDD[InternalRow] = {
    // boundGenerator.terminate() should be triggered after all of the rows in the partition
    if (join) {
      child.execute().mapPartitionsInternal { iter =>
        val generatorNullRow = InternalRow.fromSeq(Seq.fill[Any](generator.elementTypes.size)(null))
        val joinedRow = new JoinedRow

        iter.flatMap { row =>
          // we should always set the left (child output)
          joinedRow.withLeft(row)
          val outputRows = boundGenerator.eval(row)
          if (outer && outputRows.isEmpty) {
            joinedRow.withRight(generatorNullRow) :: Nil
          } else {
            outputRows.map(or => joinedRow.withRight(or))
          }
        } ++ LazyIterator(() => boundGenerator.terminate()).map { row =>
          // we leave the left side as the last element of its child output
          // keep it the same as Hive does
          joinedRow.withRight(row)
        }
      }
    } else {
      child.execute().mapPartitionsInternal { iter =>
        iter.flatMap(row => boundGenerator.eval(row)) ++
        LazyIterator(() => boundGenerator.terminate())
      }
    }
  }
}