Files
scylladb/test/lib/random_utils.hh
Avi Kivity aa1270a00c treewide: change assert() to SCYLLA_ASSERT()
assert() is traditionally disabled in release builds, but not in
scylladb. This hasn't caused problems so far, but the latest abseil
release includes a commit [1] that causes a 1000 insn/op regression when
NDEBUG is not defined.

Clearly, we must move towards a build system where NDEBUG is defined in
release builds. But we can't just define it blindly without vetting
all the assert() calls, as some were written with the expectation that
they are enabled in release mode.

To solve the conundrum, change all assert() calls to a new SCYLLA_ASSERT()
macro in utils/assert.hh. This macro is always defined and is not conditional
on NDEBUG, so we can later (after vetting Seastar) enable NDEBUG in release
mode.

[1] 66ef711d68

Closes scylladb/scylladb#20006
2024-08-05 08:23:35 +03:00

191 lines
4.8 KiB
C++

/*
* Copyright (C) 2018-present ScyllaDB
*/
/*
* SPDX-License-Identifier: AGPL-3.0-or-later
*/
#pragma once
#include <algorithm>
#include <random>
#include <boost/range/algorithm/generate.hpp>
#include <iostream>
#include <seastar/testing/test_runner.hh>
#include "bytes.hh"
#include "utils/assert.hh"
#include "utils/preempt.hh"
namespace tests::random {
inline std::default_random_engine& gen() {
return seastar::testing::local_random_engine;
}
/// Produces random integers from a set of steps.
///
/// Each step has a weight and a uniform distribution that determines the range
/// of values for that step. The probability of the generated number to be from
/// any given step is Ws/Wt, where Ws is the weight of the step and Wt is the
/// sum of the weight of all steps.
template <typename Integer>
class stepped_int_distribution {
public:
struct step {
double weight;
std::pair<Integer, Integer> range;
};
private:
std::discrete_distribution<Integer> _step_index_dist;
std::vector<std::uniform_int_distribution<Integer>> _step_ranges;
public:
explicit stepped_int_distribution(std::initializer_list<step> steps) {
std::vector<double> step_weights;
for (auto& s : steps) {
step_weights.push_back(s.weight);
_step_ranges.emplace_back(s.range.first, s.range.second);
}
_step_index_dist = std::discrete_distribution<Integer>{step_weights.begin(), step_weights.end()};
}
template <typename RandomEngine>
Integer operator()(RandomEngine& engine) {
return _step_ranges[_step_index_dist(engine)](engine);
}
};
template<typename T, typename RandomEngine>
T get_int(T min, T max, RandomEngine& engine) {
std::uniform_int_distribution<T> dist(min, max);
return dist(engine);
}
template<typename T, typename RandomEngine>
T get_int(T max, RandomEngine& engine) {
return get_int(T{0}, max, engine);
}
template<typename T, typename RandomEngine>
T get_int(RandomEngine& engine) {
return get_int(T{0}, std::numeric_limits<T>::max(), engine);
}
template<typename T>
T get_int() {
return get_int(T{0}, std::numeric_limits<T>::max(), gen());
}
template<typename T>
T get_int(T max) {
return get_int(T{0}, max, gen());
}
template<typename T>
T get_int(T min, T max) {
return get_int(min, max, gen());
}
template <typename Real, typename RandomEngine>
Real get_real(Real min, Real max, RandomEngine& engine) {
auto dist = std::uniform_real_distribution<Real>(min, max);
return dist(engine);
}
template <typename Real, typename RandomEngine>
Real get_real(Real max, RandomEngine& engine) {
return get_real<Real>(Real{0}, max, engine);
}
/// Returns true with probability p.
/// p = 1.0 means 100%.
inline
bool with_probability(double p) {
return get_real<double>(1, gen()) < p;
}
template <typename Real, typename RandomEngine>
Real get_real(RandomEngine& engine) {
return get_real<Real>(Real{0}, std::numeric_limits<Real>::max(), engine);
}
template <typename Real>
Real get_real(Real min, Real max) {
return get_real<Real>(min, max, gen());
}
template <typename Real>
Real get_real(Real max) {
return get_real<Real>(Real{0}, max, gen());
}
template <typename Real>
Real get_real() {
return get_real<Real>(Real{0}, std::numeric_limits<Real>::max(), gen());
}
template <typename RandomEngine>
inline bool get_bool(RandomEngine& engine) {
static std::bernoulli_distribution dist;
return dist(engine);
}
inline bool get_bool() {
return get_bool(gen());
}
inline bytes get_bytes(size_t n) {
bytes b(bytes::initialized_later(), n);
boost::generate(b, [] { return get_int<bytes::value_type>(); });
return b;
}
inline bytes get_bytes() {
return get_bytes(get_int<unsigned>(128 * 1024));
}
template <typename RandomEngine>
inline sstring get_sstring(size_t n, RandomEngine& engine) {
sstring str = uninitialized_string(n);
boost::generate(str, [&engine] { return get_int<sstring::value_type>('a', 'z', engine); });
return str;
}
inline sstring get_sstring(size_t n) {
return get_sstring(n, gen());
}
inline sstring get_sstring() {
return get_sstring(get_int<unsigned>(1024));
}
// Picks a random subset of size `m` from the given vector.
template <typename T>
std::vector<T> random_subset(std::vector<T> v, unsigned m, std::mt19937& engine) {
SCYLLA_ASSERT(m <= v.size());
std::shuffle(v.begin(), v.end(), engine);
return {v.begin(), v.begin() + m};
}
// Picks a random subset of size `m` from the set {0, ..., `n` - 1}.
template<typename T>
std::vector<T> random_subset(unsigned n, unsigned m, std::mt19937& engine) {
SCYLLA_ASSERT(m <= n);
std::vector<T> the_set(n);
std::iota(the_set.begin(), the_set.end(), T{});
return random_subset(std::move(the_set), m, engine);
}
inline
preemption_check random_preempt() {
return [] () noexcept {
return get_bool();
};
}
}