ad5325a736
Summary: * New public header unique_id.h and function GetUniqueIdFromTableProperties which computes a universally unique identifier based on table properties of table files from recent RocksDB versions. * Generation of DB session IDs is refactored so that they are guaranteed unique in the lifetime of a process running RocksDB. (SemiStructuredUniqueIdGen, new test included.) Along with file numbers, this enables SST unique IDs to be guaranteed unique among SSTs generated in a single process, and "better than random" between processes. See https://github.com/pdillinger/unique_id * In addition to public API producing 'external' unique IDs, there is a function for producing 'internal' unique IDs, with functions for converting between the two. In short, the external ID is "safe" for things people might do with it, and the internal ID enables more "power user" features for the future. Specifically, the external ID goes through a hashing layer so that any subset of bits in the external ID can be used as a hash of the full ID, while also preserving uniqueness guarantees in the first 128 bits (bijective both on first 128 bits and on full 192 bits). Intended follow-up: * Use the internal unique IDs in cache keys. (Avoid conflicts with https://github.com/facebook/rocksdb/issues/8912) (The file offset can be XORed into the third 64-bit value of the unique ID.) * Publish the external unique IDs in FileStorageInfo (https://github.com/facebook/rocksdb/issues/8968) Pull Request resolved: https://github.com/facebook/rocksdb/pull/8990 Test Plan: Unit tests added, and checking of unique ids in stress test. NOTE in stress test we do not generate nearly enough files to thoroughly stress uniqueness, but the test trims off pieces of the ID to check for uniqueness so that we can infer (with some assumptions) stronger properties in the aggregate. Reviewed By: zhichao-cao, mrambacher Differential Revision: D31582865 Pulled By: pdillinger fbshipit-source-id: 1f620c4c86af9abe2a8d177b9ccf2ad2b9f48243
193 lines
6.2 KiB
C++
193 lines
6.2 KiB
C++
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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//
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#pragma once
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#include <stdint.h>
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#include <algorithm>
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#include <random>
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#include "rocksdb/rocksdb_namespace.h"
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namespace ROCKSDB_NAMESPACE {
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// A very simple random number generator. Not especially good at
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// generating truly random bits, but good enough for our needs in this
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// package.
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class Random {
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private:
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enum : uint32_t {
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M = 2147483647L // 2^31-1
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};
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enum : uint64_t {
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A = 16807 // bits 14, 8, 7, 5, 2, 1, 0
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};
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uint32_t seed_;
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static uint32_t GoodSeed(uint32_t s) { return (s & M) != 0 ? (s & M) : 1; }
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public:
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// This is the largest value that can be returned from Next()
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enum : uint32_t { kMaxNext = M };
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explicit Random(uint32_t s) : seed_(GoodSeed(s)) {}
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void Reset(uint32_t s) { seed_ = GoodSeed(s); }
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uint32_t Next() {
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// We are computing
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// seed_ = (seed_ * A) % M, where M = 2^31-1
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//
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// seed_ must not be zero or M, or else all subsequent computed values
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// will be zero or M respectively. For all other values, seed_ will end
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// up cycling through every number in [1,M-1]
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uint64_t product = seed_ * A;
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// Compute (product % M) using the fact that ((x << 31) % M) == x.
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seed_ = static_cast<uint32_t>((product >> 31) + (product & M));
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// The first reduction may overflow by 1 bit, so we may need to
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// repeat. mod == M is not possible; using > allows the faster
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// sign-bit-based test.
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if (seed_ > M) {
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seed_ -= M;
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}
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return seed_;
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}
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uint64_t Next64() { return (uint64_t{Next()} << 32) | Next(); }
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint32_t Uniform(int n) { return Next() % n; }
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(int n) { return Uniform(n) == 0; }
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// "Optional" one-in-n, where 0 or negative always returns false
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// (may or may not consume a random value)
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bool OneInOpt(int n) { return n > 0 && OneIn(n); }
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// Returns random bool that is true for the given percentage of
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// calls on average. Zero or less is always false and 100 or more
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// is always true (may or may not consume a random value)
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bool PercentTrue(int percentage) {
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return static_cast<int>(Uniform(100)) < percentage;
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}
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint32_t Skewed(int max_log) {
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return Uniform(1 << Uniform(max_log + 1));
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}
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// Returns a random string of length "len"
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std::string RandomString(int len);
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// Generates a random string of len bytes using human-readable characters
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std::string HumanReadableString(int len);
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// Generates a random binary data
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std::string RandomBinaryString(int len);
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// Returns a Random instance for use by the current thread without
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// additional locking
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static Random* GetTLSInstance();
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};
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// A good 32-bit random number generator based on std::mt19937.
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// This exists in part to avoid compiler variance in warning about coercing
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// uint_fast32_t from mt19937 to uint32_t.
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class Random32 {
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private:
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std::mt19937 generator_;
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public:
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explicit Random32(uint32_t s) : generator_(s) {}
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// Generates the next random number
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uint32_t Next() { return static_cast<uint32_t>(generator_()); }
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint32_t Uniform(uint32_t n) {
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return static_cast<uint32_t>(
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std::uniform_int_distribution<std::mt19937::result_type>(
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0, n - 1)(generator_));
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}
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// Returns an *almost* uniformly distributed value in the range [0..n-1].
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// Much faster than Uniform().
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// REQUIRES: n > 0
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uint32_t Uniformish(uint32_t n) {
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// fastrange (without the header)
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return static_cast<uint32_t>((uint64_t(generator_()) * uint64_t(n)) >> 32);
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}
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(uint32_t n) { return Uniform(n) == 0; }
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint32_t Skewed(int max_log) {
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return Uniform(uint32_t{1} << Uniform(max_log + 1));
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}
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// Reset the seed of the generator to the given value
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void Seed(uint32_t new_seed) { generator_.seed(new_seed); }
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};
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// A good 64-bit random number generator based on std::mt19937_64
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class Random64 {
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private:
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std::mt19937_64 generator_;
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public:
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explicit Random64(uint64_t s) : generator_(s) { }
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// Generates the next random number
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uint64_t Next() { return generator_(); }
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint64_t Uniform(uint64_t n) {
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return std::uniform_int_distribution<uint64_t>(0, n - 1)(generator_);
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}
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(uint64_t n) { return Uniform(n) == 0; }
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint64_t Skewed(int max_log) {
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return Uniform(uint64_t(1) << Uniform(max_log + 1));
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}
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};
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// A seeded replacement for removed std::random_shuffle
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template <class RandomIt>
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void RandomShuffle(RandomIt first, RandomIt last, uint32_t seed) {
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std::mt19937 rng(seed);
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std::shuffle(first, last, rng);
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}
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// A replacement for removed std::random_shuffle
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template <class RandomIt>
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void RandomShuffle(RandomIt first, RandomIt last) {
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RandomShuffle(first, last, std::random_device{}());
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}
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} // namespace ROCKSDB_NAMESPACE
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