/**
 * Copyright 2016 LinkedIn Corp. All rights reserved.
 *
 * Licensed 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.
 */
package com.github.ambry.utils;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;


public class FilterFactory {

  private static final Logger logger = LoggerFactory.getLogger(FilterFactory.class);
  private static final long BITSET_EXCESS = 20;

  public static void serialize(IFilter bf, DataOutput output) throws IOException {
    Murmur3BloomFilter.serializer.serialize((Murmur3BloomFilter) bf, output);
  }

  public static IFilter deserialize(DataInput input) throws IOException {
    return Murmur3BloomFilter.serializer.deserialize(input);
  }

  /**
   * @return A BloomFilter with the lowest practical false positive
   *         probability for the given number of elements.
   */
  public static IFilter getFilter(long numElements, int targetBucketsPerElem) {
    int maxBucketsPerElement = Math.max(1, BloomCalculations.maxBucketsPerElement(numElements));
    int bucketsPerElement = Math.min(targetBucketsPerElem, maxBucketsPerElement);
    if (bucketsPerElement < targetBucketsPerElem) {
      logger.warn(String.format("Cannot provide an optimal BloomFilter for %d elements (%d/%d buckets per element).",
          numElements, bucketsPerElement, targetBucketsPerElem));
    }
    BloomCalculations.BloomSpecification spec = BloomCalculations.computeBloomSpec(bucketsPerElement);
    return createFilter(spec.K, numElements, spec.bucketsPerElement);
  }

  /**
   * @return The smallest BloomFilter that can provide the given false
   *         positive probability rate for the given number of elements.
   *
   *         Asserts that the given probability can be satisfied using this
   *         filter.
   */
  public static IFilter getFilter(long numElements, double maxFalsePosProbability) {
    int bucketsPerElement = BloomCalculations.maxBucketsPerElement(numElements);
    BloomCalculations.BloomSpecification spec =
        BloomCalculations.computeBloomSpec(bucketsPerElement, maxFalsePosProbability);
    return createFilter(spec.K, numElements, spec.bucketsPerElement);
  }

  private static IFilter createFilter(int hash, long numElements, int bucketsPer) {
    long numBits = (numElements * bucketsPer) + BITSET_EXCESS;
    IBitSet bitset = new OpenBitSet(numBits);
    return new Murmur3BloomFilter(hash, bitset);
  }
}