/*
 * Copyright (C) 2011 The Guava Authors
 *
 * 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. See the License for the specific language governing permissions and limitations under
 * the License.
 */

package com.google.common.hash;

import static com.google.common.base.Preconditions.checkArgument;
import static com.google.common.base.Preconditions.checkNotNull;

import com.google.common.annotations.Beta;
import com.google.common.annotations.VisibleForTesting;
import com.google.common.base.Objects;
import com.google.common.base.Predicate;
import com.google.common.hash.BloomFilterStrategies.BitArray;
import com.google.common.primitives.SignedBytes;
import com.google.common.primitives.UnsignedBytes;

import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.io.Serializable;

import javax.annotation.Nullable;

/**
 * A Bloom filter for instances of {@code T}. A Bloom filter offers an approximate containment test
 * with one-sided error: if it claims that an element is contained in it, this might be in error,
 * but if it claims that an element is <i>not</i> contained in it, then this is definitely true.
 *
 * <p>If you are unfamiliar with Bloom filters, this nice
 * <a href="http://llimllib.github.com/bloomfilter-tutorial/">tutorial</a> may help you understand
 * how they work.
 *
 * <p>The false positive probability ({@code FPP}) of a bloom filter is defined as the probability
 * that {@linkplain #mightContain(Object)} will erroneously return {@code true} for an object that
 * has not actually been put in the {@code BloomFilter}.
 *
 * <p>Bloom filters are serializable. They also support a more compact serial representation via
 * the {@link #writeTo} and {@link #readFrom} methods. Both serialized forms will continue to be
 * supported by future versions of this library. However, serial forms generated by newer versions
 * of the code may not be readable by older versions of the code (e.g., a serialized bloom filter
 * generated today may <i>not</i> be readable by a binary that was compiled 6 months ago).
 *
 * @param <T> the type of instances that the {@code BloomFilter} accepts
 * @author Dimitris Andreou
 * @author Kevin Bourrillion
 * @since 11.0
 */
@Beta
public final class BloomFilter<T> implements Predicate<T>, Serializable {
  /**
   * A strategy to translate T instances, to {@code numHashFunctions} bit indexes.
   *
   * <p>Implementations should be collections of pure functions (i.e. stateless).
   */
  interface Strategy extends java.io.Serializable {

    /**
     * Sets {@code numHashFunctions} bits of the given bit array, by hashing a user element.
     *
     * <p>Returns whether any bits changed as a result of this operation.
     */
    <T> boolean put(T object, Funnel<? super T> funnel, int numHashFunctions, BitArray bits);

    /**
     * Queries {@code numHashFunctions} bits of the given bit array, by hashing a user element;
     * returns {@code true} if and only if all selected bits are set.
     */
    <T> boolean mightContain(
        T object, Funnel<? super T> funnel, int numHashFunctions, BitArray bits);

    /**
     * Identifier used to encode this strategy, when marshalled as part of a BloomFilter.
     * Only values in the [-128, 127] range are valid for the compact serial form.
     * Non-negative values are reserved for enums defined in BloomFilterStrategies;
     * negative values are reserved for any custom, stateful strategy we may define
     * (e.g. any kind of strategy that would depend on user input).
     */
    int ordinal();
  }

  /** The bit set of the BloomFilter (not necessarily power of 2!)*/
  private final BitArray bits;

  /** Number of hashes per element */
  private final int numHashFunctions;

  /** The funnel to translate Ts to bytes */
  private final Funnel<? super T> funnel;

  /**
   * The strategy we employ to map an element T to {@code numHashFunctions} bit indexes.
   */
  private final Strategy strategy;

  /**
   * Creates a BloomFilter.
   */
  private BloomFilter(BitArray bits, int numHashFunctions, Funnel<? super T> funnel,
      Strategy strategy) {
    checkArgument(numHashFunctions > 0,
        "numHashFunctions (%s) must be > 0", numHashFunctions);
    checkArgument(numHashFunctions <= 255,
        "numHashFunctions (%s) must be <= 255", numHashFunctions);
    this.bits = checkNotNull(bits);
    this.numHashFunctions = numHashFunctions;
    this.funnel = checkNotNull(funnel);
    this.strategy = checkNotNull(strategy);
  }

  /**
   * Creates a new {@code BloomFilter} that's a copy of this instance. The new instance is equal to
   * this instance but shares no mutable state.
   *
   * @since 12.0
   */
  public BloomFilter<T> copy() {
    return new BloomFilter<T>(bits.copy(), numHashFunctions, funnel, strategy);
  }

  /**
   * Returns {@code true} if the element <i>might</i> have been put in this Bloom filter,
   * {@code false} if this is <i>definitely</i> not the case.
   */
  public boolean mightContain(T object) {
    return strategy.mightContain(object, funnel, numHashFunctions, bits);
  }

  /**
   * @deprecated Provided only to satisfy the {@link Predicate} interface; use {@link #mightContain}
   *     instead.
   */
  @Deprecated
  @Override
  public boolean apply(T input) {
    return mightContain(input);
  }

  /**
   * Puts an element into this {@code BloomFilter}. Ensures that subsequent invocations of
   * {@link #mightContain(Object)} with the same element will always return {@code true}.
   *
   * @return true if the bloom filter's bits changed as a result of this operation. If the bits
   *     changed, this is <i>definitely</i> the first time {@code object} has been added to the
   *     filter. If the bits haven't changed, this <i>might</i> be the first time {@code object}
   *     has been added to the filter. Note that {@code put(t)} always returns the
   *     <i>opposite</i> result to what {@code mightContain(t)} would have returned at the time
   *     it is called."
   * @since 12.0 (present in 11.0 with {@code void} return type})
   */
  public boolean put(T object) {
    return strategy.put(object, funnel, numHashFunctions, bits);
  }

  /**
   * Returns the probability that {@linkplain #mightContain(Object)} will erroneously return
   * {@code true} for an object that has not actually been put in the {@code BloomFilter}.
   *
   * <p>Ideally, this number should be close to the {@code fpp} parameter
   * passed in {@linkplain #create(Funnel, int, double)}, or smaller. If it is
   * significantly higher, it is usually the case that too many elements (more than
   * expected) have been put in the {@code BloomFilter}, degenerating it.
   *
   * @since 14.0 (since 11.0 as expectedFalsePositiveProbability())
   */
  public double expectedFpp() {
    // You down with FPP? (Yeah you know me!) Who's down with FPP? (Every last homie!)
    return Math.pow((double) bits.bitCount() / bitSize(), numHashFunctions);
  }

  /**
   * Returns the number of bits in the underlying bit array.
   */
  @VisibleForTesting long bitSize() {
    return bits.bitSize();
  }

  /**
   * Determines whether a given bloom filter is compatible with this bloom filter. For two
   * bloom filters to be compatible, they must:
   *
   * <ul>
   * <li>not be the same instance
   * <li>have the same number of hash functions
   * <li>have the same bit size
   * <li>have the same strategy
   * <li>have equal funnels
   * <ul>
   *
   * @param that The bloom filter to check for compatibility.
   * @since 15.0
   */
  public boolean isCompatible(BloomFilter<T> that) {
    checkNotNull(that);
    return (this != that) &&
        (this.numHashFunctions == that.numHashFunctions) &&
        (this.bitSize() == that.bitSize()) &&
        (this.strategy.equals(that.strategy)) &&
        (this.funnel.equals(that.funnel));
  }

  /**
   * Combines this bloom filter with another bloom filter by performing a bitwise OR of the
   * underlying data. The mutations happen to <b>this</b> instance. Callers must ensure the
   * bloom filters are appropriately sized to avoid saturating them.
   *
   * @param that The bloom filter to combine this bloom filter with. It is not mutated.
   * @throws IllegalArgumentException if {@code isCompatible(that) == false}
   *
   * @since 15.0
   */
  public void putAll(BloomFilter<T> that) {
    checkNotNull(that);
    checkArgument(this != that, "Cannot combine a BloomFilter with itself.");
    checkArgument(this.numHashFunctions == that.numHashFunctions,
        "BloomFilters must have the same number of hash functions (%s != %s)",
        this.numHashFunctions, that.numHashFunctions);
    checkArgument(this.bitSize() == that.bitSize(),
        "BloomFilters must have the same size underlying bit arrays (%s != %s)",
        this.bitSize(), that.bitSize());
    checkArgument(this.strategy.equals(that.strategy),
        "BloomFilters must have equal strategies (%s != %s)",
        this.strategy, that.strategy);
    checkArgument(this.funnel.equals(that.funnel),
        "BloomFilters must have equal funnels (%s != %s)",
        this.funnel, that.funnel);
    this.bits.putAll(that.bits);
  }

  @Override
  public boolean equals(@Nullable Object object) {
    if (object == this) {
      return true;
    }
    if (object instanceof BloomFilter) {
      BloomFilter<?> that = (BloomFilter<?>) object;
      return this.numHashFunctions == that.numHashFunctions
          && this.funnel.equals(that.funnel)
          && this.bits.equals(that.bits)
          && this.strategy.equals(that.strategy);
    }
    return false;
  }

  @Override
  public int hashCode() {
    return Objects.hashCode(numHashFunctions, funnel, strategy, bits);
  }

  private static final Strategy DEFAULT_STRATEGY =
      BloomFilterStrategies.MURMUR128_MITZ_64;

  /**
   * Creates a {@link BloomFilter BloomFilter<T>} with the expected number of
   * insertions and expected false positive probability.
   *
   * <p>Note that overflowing a {@code BloomFilter} with significantly more elements
   * than specified, will result in its saturation, and a sharp deterioration of its
   * false positive probability.
   *
   * <p>The constructed {@code BloomFilter<T>} will be serializable if the provided
   * {@code Funnel<T>} is.
   *
   * <p>It is recommended that the funnel be implemented as a Java enum. This has the
   * benefit of ensuring proper serialization and deserialization, which is important
   * since {@link #equals} also relies on object identity of funnels.
   *
   * @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use
   * @param expectedInsertions the number of expected insertions to the constructed
   *     {@code BloomFilter<T>}; must be positive
   * @param fpp the desired false positive probability (must be positive and less than 1.0)
   * @return a {@code BloomFilter}
   */
  public static <T> BloomFilter<T> create(
      Funnel<? super T> funnel, int expectedInsertions /* n */, double fpp) {
    return create(funnel, expectedInsertions, fpp, DEFAULT_STRATEGY);
  }

  @VisibleForTesting
  static <T> BloomFilter<T> create(
      Funnel<? super T> funnel, int expectedInsertions /* n */, double fpp, Strategy strategy) {
    checkNotNull(funnel);
    checkArgument(expectedInsertions >= 0, "Expected insertions (%s) must be >= 0",
        expectedInsertions);
    checkArgument(fpp > 0.0, "False positive probability (%s) must be > 0.0", fpp);
    checkArgument(fpp < 1.0, "False positive probability (%s) must be < 1.0", fpp);
    checkNotNull(strategy);

    if (expectedInsertions == 0) {
      expectedInsertions = 1;
    }
    /*
     * TODO(user): Put a warning in the javadoc about tiny fpp values,
     * since the resulting size is proportional to -log(p), but there is not
     * much of a point after all, e.g. optimalM(1000, 0.0000000000000001) = 76680
     * which is less than 10kb. Who cares!
     */
    long numBits = optimalNumOfBits(expectedInsertions, fpp);
    int numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, numBits);
    try {
      return new BloomFilter<T>(new BitArray(numBits), numHashFunctions, funnel, strategy);
    } catch (IllegalArgumentException e) {
      throw new IllegalArgumentException("Could not create BloomFilter of " + numBits + " bits", e);
    }
  }

  /**
   * Creates a {@link BloomFilter BloomFilter<T>} with the expected number of
   * insertions and a default expected false positive probability of 3%.
   *
   * <p>Note that overflowing a {@code BloomFilter} with significantly more elements
   * than specified, will result in its saturation, and a sharp deterioration of its
   * false positive probability.
   *
   * <p>The constructed {@code BloomFilter<T>} will be serializable if the provided
   * {@code Funnel<T>} is.
   *
   * @param funnel the funnel of T's that the constructed {@code BloomFilter<T>} will use
   * @param expectedInsertions the number of expected insertions to the constructed
   *     {@code BloomFilter<T>}; must be positive
   * @return a {@code BloomFilter}
   */
  public static <T> BloomFilter<T> create(
      Funnel<? super T> funnel, int expectedInsertions /* n */) {
    return create(funnel, expectedInsertions, 0.03); // FYI, for 3%, we always get 5 hash functions
  }

  /*
   * Cheat sheet:
   *
   * m: total bits
   * n: expected insertions
   * b: m/n, bits per insertion
   * p: expected false positive probability
   *
   * 1) Optimal k = b * ln2
   * 2) p = (1 - e ^ (-kn/m))^k
   * 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
   * 4) For optimal k: m = -nlnp / ((ln2) ^ 2)
   */

  /**
   * Computes the optimal k (number of hashes per element inserted in Bloom filter), given the
   * expected insertions and total number of bits in the Bloom filter.
   *
   * See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg for the formula.
   *
   * @param n expected insertions (must be positive)
   * @param m total number of bits in Bloom filter (must be positive)
   */
  @VisibleForTesting
  static int optimalNumOfHashFunctions(long n, long m) {
    // (m / n) * log(2), but avoid truncation due to division!
    return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
  }

  /**
   * Computes m (total bits of Bloom filter) which is expected to achieve, for the specified
   * expected insertions, the required false positive probability.
   *
   * See http://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives for the formula.
   *
   * @param n expected insertions (must be positive)
   * @param p false positive rate (must be 0 < p < 1)
   */
  @VisibleForTesting
  static long optimalNumOfBits(long n, double p) {
    if (p == 0) {
      p = Double.MIN_VALUE;
    }
    return (long) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
  }

  private Object writeReplace() {
    return new SerialForm<T>(this);
  }

  private static class SerialForm<T> implements Serializable {
    final long[] data;
    final int numHashFunctions;
    final Funnel<? super T> funnel;
    final Strategy strategy;

    SerialForm(BloomFilter<T> bf) {
      this.data = bf.bits.data;
      this.numHashFunctions = bf.numHashFunctions;
      this.funnel = bf.funnel;
      this.strategy = bf.strategy;
    }
    Object readResolve() {
      return new BloomFilter<T>(new BitArray(data), numHashFunctions, funnel, strategy);
    }
    private static final long serialVersionUID = 1;
  }

  /**
   * Writes this {@code BloomFilter} to an output stream, with a custom format (not Java
   * serialization). This has been measured to save at least 400 bytes compared to regular
   * serialization.
   *
   * <p>Use {@linkplain #readFrom(InputStream, Funnel)} to reconstruct the written BloomFilter.
   */
  public void writeTo(OutputStream out) throws IOException {
    /*
     * Serial form:
     * 1 signed byte for the strategy
     * 1 unsigned byte for the number of hash functions
     * 1 big endian int, the number of longs in our bitset
     * N big endian longs of our bitset
     */
    DataOutputStream dout = new DataOutputStream(out);
    dout.writeByte(SignedBytes.checkedCast(strategy.ordinal()));
    dout.writeByte(UnsignedBytes.checkedCast(numHashFunctions)); // note: checked at the c'tor
    dout.writeInt(bits.data.length);
    for (long value : bits.data) {
      dout.writeLong(value);
    }
  }

  /**
   * Reads a byte stream, which was written by {@linkplain #writeTo(OutputStream)}, into
   * a {@code BloomFilter<T>}.
   *
   * The {@code Funnel} to be used is not encoded in the stream, so it must be provided here.
   * <b>Warning:</b> the funnel provided <b>must</b> behave identically to the one used to
   * populate the original Bloom filter!
   *
   * @throws IOException if the InputStream throws an {@code IOException}, or if its data does
   *     not appear to be a BloomFilter serialized using the
   *     {@linkplain #writeTo(OutputStream)} method.
   */
  public static <T> BloomFilter<T> readFrom(InputStream in, Funnel<T> funnel) throws IOException {
    checkNotNull(in, "InputStream");
    checkNotNull(funnel, "Funnel");
    int strategyOrdinal = -1;
    int numHashFunctions = -1;
    int dataLength = -1;
    try {
      DataInputStream din = new DataInputStream(in);
      // currently this assumes there is no negative ordinal; will have to be updated if we
      // add non-stateless strategies (for which we've reserved negative ordinals; see
      // Strategy.ordinal()).
      strategyOrdinal = din.readByte();
      numHashFunctions = UnsignedBytes.toInt(din.readByte());
      dataLength = din.readInt();

      Strategy strategy = BloomFilterStrategies.values()[strategyOrdinal];
      long[] data = new long[dataLength];
      for (int i = 0; i < data.length; i++) {
        data[i] = din.readLong();
      }
      return new BloomFilter<T>(new BitArray(data), numHashFunctions, funnel, strategy);
    } catch (RuntimeException e) {
      IOException ioException = new IOException(
          "Unable to deserialize BloomFilter from InputStream."
          + " strategyOrdinal: " + strategyOrdinal
          + " numHashFunctions: " + numHashFunctions
          + " dataLength: " + dataLength);
      ioException.initCause(e);
      throw ioException;
    }
  }
}