package org.apache.pig.backend.hadoop.executionengine.spark.converter;

import java.io.IOException;
import java.io.Serializable;
import java.util.List;

import org.apache.pig.backend.executionengine.ExecException;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.Result;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators.POLimit;
import org.apache.pig.backend.hadoop.executionengine.spark.ScalaUtil;
import org.apache.pig.backend.hadoop.executionengine.spark.SparkUtil;
import org.apache.pig.data.Tuple;
import org.apache.spark.rdd.RDD;

import scala.collection.Iterator;
import scala.collection.JavaConversions;
import scala.runtime.AbstractFunction1;

public class LimitConverter implements POConverter<Tuple, Tuple, POLimit> {

    @Override
    public RDD<Tuple> convert(List<RDD<Tuple>> predecessors, POLimit poLimit)
            throws IOException {
        SparkUtil.assertPredecessorSize(predecessors, poLimit, 1);
        RDD<Tuple> rdd = predecessors.get(0);
        
        LimitFunction limitFunction = new LimitFunction(poLimit);
        return rdd.mapPartitions(limitFunction, false, ScalaUtil.getClassTag(Tuple.class));
    }

    @SuppressWarnings("serial")
	private static class LimitFunction extends AbstractFunction1<Iterator<Tuple>, Iterator<Tuple>> implements Serializable {

        private final POLimit poLimit;

        public LimitFunction(POLimit poLimit) {
            this.poLimit = poLimit;
        }

        @Override
        public Iterator<Tuple> apply(Iterator<Tuple> i) {
            final java.util.Iterator<Tuple> tuples = JavaConversions.asJavaIterator(i);

            return JavaConversions.asScalaIterator(new POOutputConsumerIterator(tuples) {

                protected void attach(Tuple tuple) {
                    poLimit.setInputs(null);
                    poLimit.attachInput(tuple);
                }

                protected Result getNextResult() throws ExecException {
                    return poLimit.getNextTuple();
                }
            });
        }

    }

}