YugabyteDB (2.13.0.0-b42, bfc6a6643e7399ac8a0e81d06a3ee6d6571b33ab)

// Copyright (c) 2011 The LevelDB Authors. All rights reserved.

// Use of this source code is governed by a BSD-style license that can be

// found in the LICENSE file. See the AUTHORS file for names of contributors.

//

// The following only applies to changes made to this file as part of YugaByte development.

//

// Portions Copyright (c) YugaByte, Inc.

//

// 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.

//

#ifndef YB_UTIL_RANDOM_H_

#define YB_UTIL_RANDOM_H_

#include <stdint.h>

#include <cmath>

#include <mutex>

#include <vector>

#include "yb/gutil/map-util.h" // For ContainsKey

#include "yb/util/locks.h"

namespace yb {

namespace random_internal {

static const uint32_t M = 2147483647L;   // 2^31-1

const double kTwoPi = 6.283185307179586476925286;

} // namespace random_internal

// A very simple random number generator.  Not especially good at

// generating truly random bits, but good enough for our needs in this

// package. This implementation is not thread-safe.

class Random {

 private:

  uint32_t seed_;

 public:

  explicit Random(uint32_t s) {

    Reset(s);

  }

  // Reset the RNG to the given seed value.

  void Reset(uint32_t s) {

    seed_ = s & 0x7fffffffu;

    // Avoid bad seeds.

    if (seed_ == 0 || seed_ == random_internal::M) {

      seed_ = 1;

    }

  }

  // Next pseudo-random 32-bit unsigned integer.

  // FIXME: This currently only generates 31 bits of randomness.

  // The MSB will always be zero.

  uint32_t Next() {

    static const uint64_t A = 16807;  // bits 14, 8, 7, 5, 2, 1, 0

    // We are computing

    //       seed_ = (seed_ * A) % M,    where M = 2^31-1

    //

    // seed_ must not be zero or M, or else all subsequent computed values

    // will be zero or M respectively.  For all other values, seed_ will end

    // up cycling through every number in [1,M-1]

    uint64_t product = seed_ * A;

    // Compute (product % M) using the fact that ((x << 31) % M) == x.

    seed_ = static_cast<uint32_t>((product >> 31) + (product & random_internal::M));

    // The first reduction may overflow by 1 bit, so we may need to

    // repeat.  mod == M is not possible; using > allows the faster

    // sign-bit-based test.

    if (seed_ > random_internal::M) {

      seed_ -= random_internal::M;

    }

    return seed_;

  }

  // Alias for consistency with Next64

  uint32_t Next32() { return Next(); }

  // Next pseudo-random 64-bit unsigned integer.

  // FIXME: This currently only generates 62 bits of randomness due to Next()

  // only giving 31 bits of randomness. The 2 most significant bits will always

  // be zero.

  uint64_t Next64() {

    uint64_t large = Next();

    // Only shift by 31 bits so we end up with zeros in MSB and not scattered

    // throughout the 64-bit word. This is due to the weakness in Next() noted

    // above.

    large <<= 31;

    large |= Next();

    return large;

  }

  // Returns a uniformly distributed value in the range [0..n-1]

  // REQUIRES: n > 0

  uint32_t Uniform(uint32_t n) { return Next() % n; }

  // Alias for consistency with Uniform64

  uint32_t Uniform32(uint32_t n) { return Uniform(n); }

  // Returns a uniformly distributed 64-bit value in the range [0..n-1]

  // REQUIRES: n > 0

  uint64_t Uniform64(uint64_t n) { return Next64() % n; }

  // Randomly returns true ~"1/n" of the time, and false otherwise.

  // REQUIRES: n > 0

  bool OneIn(int n) { return (Next() % n) == 0; }

  // Skewed: pick "base" uniformly from range [0,max_log] and then

  // return "base" random bits.  The effect is to pick a number in the

  // range [0,2^max_log-1] with exponential bias towards smaller numbers.

  uint32_t Skewed(int max_log) {

    return Uniform(1 << Uniform(max_log + 1));

  }

  // Creates a normal distribution variable using the

  // Box-Muller transform. See:

  // http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform

  // Adapted from WebRTC source code at:

  // webrtc/trunk/modules/video_coding/main/test/test_util.cc

  double Normal(double mean, double std_dev) {

    double uniform1 = (Next() + 1.0) / (random_internal::M + 1.0);

    double uniform2 = (Next() + 1.0) / (random_internal::M + 1.0);

    return (mean + std_dev * sqrt(-2 * ::log(uniform1)) * cos(random_internal::kTwoPi * uniform2));

  }

  // Return a random number between 0.0 and 1.0 inclusive.

  double NextDoubleFraction() {

    return Next() / static_cast<double>(random_internal::M + 1.0);

  }

  // Sample 'k' random elements from the collection 'c' into 'result', taking care not to sample any

  // elements that are already present in 'avoid'.

  //

  // In the case that 'c' has fewer than 'k' elements then all elements in 'c' will be selected.

  //

  // 'c' should be an iterable STL collection such as a vector, set, or list.

  // 'avoid' should be an STL-compatible set.

  //

  // The results are not stored in a randomized order: the order of results will

  // match their order in the input collection.

  template<class Collection, class Set, class T>

  void ReservoirSample(const Collection& c, size_t k, const Set& avoid,

                       std::vector<T>* result) {

    result->clear();

    result->reserve(k);

    int i = 0;

    for (const T& elem : c) {

      if (ContainsKey(avoid, elem)) {

        continue;

      }

      i++;

      // Fill the reservoir if there is available space.

      if (result->size() < k) {

        result->push_back(elem);

        continue;

      }

      // Otherwise replace existing elements with decreasing probability.

      auto j = Uniform(i);

      if (j < k) {

        (*result)[j] = elem;

      }

    }

  }

};

// Thread-safe wrapper around Random.

class ThreadSafeRandom {

 public:

  explicit ThreadSafeRandom(uint32_t s)

      : random_(s) {

  }

  void Reset(uint32_t s) {

    std::lock_guard<simple_spinlock> l(lock_);

    random_.Reset(s);

  }

  uint32_t Next() {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Next();

  }

  uint32_t Next32() {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Next32();

  }

  uint64_t Next64() {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Next64();

  }

  uint32_t Uniform(uint32_t n) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Uniform(n);

  }

  uint32_t Uniform32(uint32_t n) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Uniform32(n);

  }

  uint64_t Uniform64(uint64_t n) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Uniform64(n);

  }

  bool OneIn(int n) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.OneIn(n);

  }

  uint32_t Skewed(int max_log) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Skewed(max_log);

  }

  double Normal(double mean, double std_dev) {

    std::lock_guard<simple_spinlock> l(lock_);

    return random_.Normal(mean, std_dev);

  }

  template<class Collection, class Set, class T>

  void ReservoirSample(const Collection& c, int k, const Set& avoid,

                       std::vector<T>* result) {

    std::lock_guard<simple_spinlock> l(lock_);

    random_.ReservoirSample(c, k, avoid, result);

  }

 private:

  simple_spinlock lock_;

  Random random_;

};

}  // namespace yb

#endif // YB_UTIL_RANDOM_H_