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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.flink.api.java.sampling;

import org.apache.flink.annotation.Internal;

import java.util.Iterator;
import java.util.PriorityQueue;

/**
 * For sampling with fraction, the sample algorithms are natively distributed, while it's not
 * true for fixed size sample algorithms. The fixed size sample algorithms require two-phases
 * sampling (according to our current implementation). In the first phase, each distributed
 * partition is sampled independently. The partial sampling results are handled by a central
 * coordinator. The central coordinator combines the partial sampling results to form the final
 * result.
 *
 * @param  The input data type.
 */
@Internal
public abstract class DistributedRandomSampler extends RandomSampler {

	protected final int numSamples;

	public DistributedRandomSampler(int numSamples) {
		this.numSamples = numSamples;
	}

	protected final Iterator> EMPTY_INTERMEDIATE_ITERABLE =
		new SampledIterator>() {
			@Override
			public boolean hasNext() {
				return false;
			}

			@Override
			public IntermediateSampleData next() {
				return null;
			}
		};

	/**
	 * Sample algorithm for the first phase. It operates on a single partition.
	 *
	 * @param input The DataSet input of each partition.
	 * @return Intermediate sample output which will be used as the input of the second phase.
	 */
	public abstract Iterator> sampleInPartition(Iterator input);

	/**
	 * Sample algorithm for the second phase. This operation should be executed as the UDF of
	 * an all reduce operation.
	 *
	 * @param input The intermediate sample output generated in the first phase.
	 * @return The sampled output.
	 */
	public Iterator sampleInCoordinator(Iterator> input) {
		if (numSamples == 0) {
			return EMPTY_ITERABLE;
		}

		// This queue holds fixed number elements with the top K weight for the coordinator.
		PriorityQueue> reservoir = new PriorityQueue>(numSamples);
		int index = 0;
		IntermediateSampleData smallest = null;
		while (input.hasNext()) {
			IntermediateSampleData element = input.next();
			if (index < numSamples) {
				// Fill the queue with first K elements from input.
				reservoir.add(element);
				smallest = reservoir.peek();
			} else {
				// If current element weight is larger than the smallest one in queue, remove the element
				// with the smallest weight, and append current element into the queue.
				if (element.getWeight() > smallest.getWeight()) {
					reservoir.remove();
					reservoir.add(element);
					smallest = reservoir.peek();
				}
			}
			index++;
		}
		final Iterator> itr = reservoir.iterator();

		return new Iterator() {
			@Override
			public boolean hasNext() {
				return itr.hasNext();
			}

			@Override
			public T next() {
				return itr.next().getElement();
			}

			@Override
			public void remove() {
				itr.remove();
			}
		};
	}

	/**
	 * Combine the first phase and second phase in sequence, implemented for test purpose only.
	 *
	 * @param input Source data.
	 * @return Sample result in sequence.
	 */
	@Override
	public Iterator sample(Iterator input) {
		return sampleInCoordinator(sampleInPartition(input));
	}
}