/*
    * 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;
     }
   }
 }