/*
 * 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.{PartitionEvaluator, PartitionEvaluatorFactory}
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Predicate}
import org.apache.spark.sql.execution.metric.SQLMetric

class FilterEvaluatorFactory(
    condition: Expression,
    childOutput: Seq[Attribute],
    numOutputRows: SQLMetric) extends PartitionEvaluatorFactory[InternalRow, InternalRow] {

  override def createEvaluator(): PartitionEvaluator[InternalRow, InternalRow] = {
    new FilterPartitionEvaluator
  }

  class FilterPartitionEvaluator extends PartitionEvaluator[InternalRow, InternalRow] {
    override def eval(
        partitionIndex: Int,
        inputs: Iterator[InternalRow]*): Iterator[InternalRow] = {
      assert(inputs.length == 1)
      val predicate = Predicate.create(condition, childOutput)
      predicate.initialize(partitionIndex)
      inputs.head.filter { row =>
        val r = predicate.eval(row)
        if (r) numOutputRows += 1
        r
      }
    }
  }
}
