spork-master/src/org/apache/pig/backend/hadoop/executionengine/spark/converter/LimitConverter.java
package org.apache.pig.backend.hadoop.executionengine.spark.converter;

import java.io.IOException;
import java.util.Iterator;
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.SparkUtil;
import org.apache.pig.data.Tuple;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.rdd.RDD;

@SuppressWarnings({ "serial" })
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);
        RDD<Tuple> rdd2 = rdd.coalesce(1, false, null);
        return rdd2.toJavaRDD().mapPartitions(limitFunction, false).rdd();
    }

    private static class LimitFunction implements FlatMapFunction<Iterator<Tuple>, Tuple> {

        private final POLimit poLimit;

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

        @Override
        public Iterable<Tuple> call(final Iterator<Tuple> tuples) {

            return new Iterable<Tuple>() {

                public Iterator<Tuple> iterator() {
                    return new POOutputConsumerIterator(tuples) {

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

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