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// Copyright 2018 The Abseil 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
//
//
// 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.
#include <stdint.h>
#include <algorithm>
#include <functional>
#include <map>
#include <numeric>
#include <random>
#include <set>
#include <string>
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "benchmark/benchmark.h"
#include "absl/algorithm/container.h"
#include "absl/base/internal/raw_logging.h"
#include "absl/container/btree_map.h"
#include "absl/container/btree_set.h"
#include "absl/container/btree_test.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/container/internal/hashtable_debug.h"
#include "absl/hash/hash.h"
#include "absl/log/log.h"
#include "absl/memory/memory.h"
#include "absl/random/random.h"
#include "absl/strings/cord.h"
#include "absl/strings/str_format.h"
#include "absl/time/time.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace container_internal {
namespace {
constexpr size_t kBenchmarkValues = 1 << 20;
// How many times we add and remove sub-batches in one batch of *AddRem
// benchmarks.
constexpr size_t kAddRemBatchSize = 1 << 2;
// Generates n values in the range [0, 4 * n].
template <typename V>
std::vector<V> GenerateValues(int n) {
constexpr int kSeed = 23;
return GenerateValuesWithSeed<V>(n, 4 * n, kSeed);
}
// Benchmark insertion of values into a container.
template <typename T>
void BM_InsertImpl(benchmark::State& state, bool sorted) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
if (sorted) {
std::sort(values.begin(), values.end());
}
T container(values.begin(), values.end());
// Remove and re-insert 10% of the keys per batch.
const int batch_size = (kBenchmarkValues + 9) / 10;
while (state.KeepRunningBatch(batch_size)) {
state.PauseTiming();
const auto i = static_cast<int>(state.iterations());
for (int j = i; j < i + batch_size; j++) {
int x = j % kBenchmarkValues;
container.erase(key_of_value(values[x]));
}
state.ResumeTiming();
for (int j = i; j < i + batch_size; j++) {
int x = j % kBenchmarkValues;
container.insert(values[x]);
}
}
}
template <typename T>
void BM_Insert(benchmark::State& state) {
BM_InsertImpl<T>(state, false);
}
template <typename T>
void BM_InsertSorted(benchmark::State& state) {
BM_InsertImpl<T>(state, true);
}
// Benchmark inserting the first few elements in a container. In b-tree, this is
// when the root node grows.
template <typename T>
void BM_InsertSmall(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
const int kSize = 8;
std::vector<V> values = GenerateValues<V>(kSize);
T container;
while (state.KeepRunningBatch(kSize)) {
for (int i = 0; i < kSize; ++i) {
benchmark::DoNotOptimize(container.insert(values[i]));
}
state.PauseTiming();
// Do not measure the time it takes to clear the container.
container.clear();
state.ResumeTiming();
}
}
template <typename T>
void BM_LookupImpl(benchmark::State& state, bool sorted) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
if (sorted) {
std::sort(values.begin(), values.end());
}
T container(values.begin(), values.end());
while (state.KeepRunning()) {
int idx = state.iterations() % kBenchmarkValues;
benchmark::DoNotOptimize(container.find(key_of_value(values[idx])));
}
}
// Benchmark lookup of values in a container.
template <typename T>
void BM_Lookup(benchmark::State& state) {
BM_LookupImpl<T>(state, false);
}
// Benchmark lookup of values in a full container, meaning that values
// are inserted in-order to take advantage of biased insertion, which
// yields a full tree.
template <typename T>
void BM_FullLookup(benchmark::State& state) {
BM_LookupImpl<T>(state, true);
}
// Benchmark erasing values from a container.
template <typename T>
void BM_Erase(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
T container(values.begin(), values.end());
// Remove and re-insert 10% of the keys per batch.
const int batch_size = (kBenchmarkValues + 9) / 10;
while (state.KeepRunningBatch(batch_size)) {
const int i = state.iterations();
for (int j = i; j < i + batch_size; j++) {
int x = j % kBenchmarkValues;
container.erase(key_of_value(values[x]));
}
state.PauseTiming();
for (int j = i; j < i + batch_size; j++) {
int x = j % kBenchmarkValues;
container.insert(values[x]);
}
state.ResumeTiming();
}
}
// Benchmark erasing multiple values from a container.
template <typename T>
void BM_EraseRange(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
T container(values.begin(), values.end());
// Remove and re-insert 10% of the keys per batch.
const int batch_size = (kBenchmarkValues + 9) / 10;
while (state.KeepRunningBatch(batch_size)) {
const int i = state.iterations();
const int start_index = i % kBenchmarkValues;
state.PauseTiming();
{
std::vector<V> removed;
removed.reserve(batch_size);
auto itr = container.find(key_of_value(values[start_index]));
auto start = itr;
for (int j = 0; j < batch_size; j++) {
if (itr == container.end()) {
state.ResumeTiming();
container.erase(start, itr);
state.PauseTiming();
itr = container.begin();
start = itr;
}
removed.push_back(*itr++);
}
state.ResumeTiming();
container.erase(start, itr);
state.PauseTiming();
container.insert(removed.begin(), removed.end());
}
state.ResumeTiming();
}
}
// Predicate that erases every other element. We can't use a lambda because
// C++11 doesn't support generic lambdas.
// TODO(b/207389011): consider adding benchmarks that remove different fractions
// of keys (e.g. 10%, 90%).
struct EraseIfPred {
uint64_t i = 0;
template <typename T>
bool operator()(const T&) {
return ++i % 2;
}
};
// Benchmark erasing multiple values from a container with a predicate.
template <typename T>
void BM_EraseIf(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
// Removes half of the keys per batch.
const int batch_size = (kBenchmarkValues + 1) / 2;
EraseIfPred pred;
while (state.KeepRunningBatch(batch_size)) {
state.PauseTiming();
{
T container(values.begin(), values.end());
state.ResumeTiming();
erase_if(container, pred);
benchmark::DoNotOptimize(container);
state.PauseTiming();
}
state.ResumeTiming();
}
}
// Benchmark steady-state insert (into first half of range) and remove (from
// second half of range), treating the container approximately like a queue with
// log-time access for all elements. This benchmark does not test the case where
// insertion and removal happen in the same region of the tree. This benchmark
// counts two value constructors.
template <typename T>
void BM_QueueAddRem(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
ABSL_RAW_CHECK(kBenchmarkValues % 2 == 0, "for performance");
T container;
const size_t half = kBenchmarkValues / 2;
std::vector<int> remove_keys(half);
std::vector<int> add_keys(half);
// We want to do the exact same work repeatedly, and the benchmark can end
// after a different number of iterations depending on the speed of the
// individual run so we use a large batch size here and ensure that we do
// deterministic work every batch.
while (state.KeepRunningBatch(half * kAddRemBatchSize)) {
state.PauseTiming();
container.clear();
for (size_t i = 0; i < half; ++i) {
remove_keys[i] = i;
add_keys[i] = i;
}
constexpr int kSeed = 5;
std::mt19937_64 rand(kSeed);
std::shuffle(remove_keys.begin(), remove_keys.end(), rand);
std::shuffle(add_keys.begin(), add_keys.end(), rand);
// Note needs lazy generation of values.
Generator<V> g(kBenchmarkValues * kAddRemBatchSize);
for (size_t i = 0; i < half; ++i) {
container.insert(g(add_keys[i]));
container.insert(g(half + remove_keys[i]));
}
// There are three parts each of size "half":
// 1 is being deleted from [offset - half, offset)
// 2 is standing [offset, offset + half)
// 3 is being inserted into [offset + half, offset + 2 * half)
size_t offset = 0;
for (size_t i = 0; i < kAddRemBatchSize; ++i) {
std::shuffle(remove_keys.begin(), remove_keys.end(), rand);
std::shuffle(add_keys.begin(), add_keys.end(), rand);
offset += half;
state.ResumeTiming();
for (size_t idx = 0; idx < half; ++idx) {
container.erase(key_of_value(g(offset - half + remove_keys[idx])));
container.insert(g(offset + half + add_keys[idx]));
}
state.PauseTiming();
}
state.ResumeTiming();
}
}
// Mixed insertion and deletion in the same range using pre-constructed values.
template <typename T>
void BM_MixedAddRem(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
typename KeyOfValue<typename T::key_type, V>::type key_of_value;
ABSL_RAW_CHECK(kBenchmarkValues % 2 == 0, "for performance");
T container;
// Create two random shuffles
std::vector<int> remove_keys(kBenchmarkValues);
std::vector<int> add_keys(kBenchmarkValues);
// We want to do the exact same work repeatedly, and the benchmark can end
// after a different number of iterations depending on the speed of the
// individual run so we use a large batch size here and ensure that we do
// deterministic work every batch.
while (state.KeepRunningBatch(kBenchmarkValues * kAddRemBatchSize)) {
state.PauseTiming();
container.clear();
constexpr int kSeed = 7;
std::mt19937_64 rand(kSeed);
std::vector<V> values = GenerateValues<V>(kBenchmarkValues * 2);
// Insert the first half of the values (already in random order)
container.insert(values.begin(), values.begin() + kBenchmarkValues);
// Insert the first half of the values (already in random order)
for (size_t i = 0; i < kBenchmarkValues; ++i) {
// remove_keys and add_keys will be swapped before each round,
// therefore fill add_keys here w/ the keys being inserted, so
// they'll be the first to be removed.
remove_keys[i] = i + kBenchmarkValues;
add_keys[i] = i;
}
for (size_t i = 0; i < kAddRemBatchSize; ++i) {
remove_keys.swap(add_keys);
std::shuffle(remove_keys.begin(), remove_keys.end(), rand);
std::shuffle(add_keys.begin(), add_keys.end(), rand);
state.ResumeTiming();
for (size_t idx = 0; idx < kBenchmarkValues; ++idx) {
container.erase(key_of_value(values[remove_keys[idx]]));
container.insert(values[add_keys[idx]]);
}
state.PauseTiming();
}
state.ResumeTiming();
}
}
// Insertion at end, removal from the beginning. This benchmark
// counts two value constructors.
// TODO(ezb): we could add a GenerateNext version of generator that could reduce
// noise for string-like types.
template <typename T>
void BM_Fifo(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
T container;
// Need lazy generation of values as state.max_iterations is large.
Generator<V> g(kBenchmarkValues + state.max_iterations);
for (int i = 0; i < kBenchmarkValues; i++) {
container.insert(g(i));
}
while (state.KeepRunning()) {
container.erase(container.begin());
container.insert(container.end(), g(state.iterations() + kBenchmarkValues));
}
}
// Iteration (forward) through the tree
template <typename T>
void BM_FwdIter(benchmark::State& state) {
using V = typename remove_pair_const<typename T::value_type>::type;
using R = typename T::value_type const*;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
T container(values.begin(), values.end());
auto iter = container.end();
R r = nullptr;
while (state.KeepRunning()) {
if (iter == container.end()) iter = container.begin();
r = &(*iter);
++iter;
}
benchmark::DoNotOptimize(r);
}
// Benchmark random range-construction of a container.
template <typename T>
void BM_RangeConstructionImpl(benchmark::State& state, bool sorted) {
using V = typename remove_pair_const<typename T::value_type>::type;
std::vector<V> values = GenerateValues<V>(kBenchmarkValues);
if (sorted) {
std::sort(values.begin(), values.end());
}
{
T container(values.begin(), values.end());
}
while (state.KeepRunning()) {
T container(values.begin(), values.end());
benchmark::DoNotOptimize(container);
}
}
template <typename T>
void BM_InsertRangeRandom(benchmark::State& state) {
BM_RangeConstructionImpl<T>(state, false);
}
template <typename T>
void BM_InsertRangeSorted(benchmark::State& state) {
BM_RangeConstructionImpl<T>(state, true);
}
#define STL_ORDERED_TYPES(value) \
using stl_set_##value = std::set<value>; \
using stl_map_##value = std::map<value, intptr_t>; \
using stl_multiset_##value = std::multiset<value>; \
using stl_multimap_##value = std::multimap<value, intptr_t>
using StdString = std::string;
STL_ORDERED_TYPES(int32_t);
STL_ORDERED_TYPES(int64_t);
STL_ORDERED_TYPES(StdString);
STL_ORDERED_TYPES(Cord);
STL_ORDERED_TYPES(Time);
#define STL_UNORDERED_TYPES(value) \
using stl_unordered_set_##value = std::unordered_set<value>; \
using stl_unordered_map_##value = std::unordered_map<value, intptr_t>; \
using flat_hash_set_##value = flat_hash_set<value>; \
using flat_hash_map_##value = flat_hash_map<value, intptr_t>; \
using stl_unordered_multiset_##value = std::unordered_multiset<value>; \
using stl_unordered_multimap_##value = \
std::unordered_multimap<value, intptr_t>
#define STL_UNORDERED_TYPES_CUSTOM_HASH(value, hash) \
using stl_unordered_set_##value = std::unordered_set<value, hash>; \
using stl_unordered_map_##value = std::unordered_map<value, intptr_t, hash>; \
using flat_hash_set_##value = flat_hash_set<value, hash>; \
using flat_hash_map_##value = flat_hash_map<value, intptr_t, hash>; \
using stl_unordered_multiset_##value = std::unordered_multiset<value, hash>; \
using stl_unordered_multimap_##value = \
std::unordered_multimap<value, intptr_t, hash>
STL_UNORDERED_TYPES_CUSTOM_HASH(Cord, absl::Hash<absl::Cord>);
STL_UNORDERED_TYPES(int32_t);
STL_UNORDERED_TYPES(int64_t);
STL_UNORDERED_TYPES(StdString);
STL_UNORDERED_TYPES_CUSTOM_HASH(Time, absl::Hash<absl::Time>);
#define BTREE_TYPES(value) \
using btree_256_set_##value = \
btree_set<value, std::less<value>, std::allocator<value>>; \
using btree_256_map_##value = \
btree_map<value, intptr_t, std::less<value>, \
std::allocator<std::pair<const value, intptr_t>>>; \
using btree_256_multiset_##value = \
btree_multiset<value, std::less<value>, std::allocator<value>>; \
using btree_256_multimap_##value = \
btree_multimap<value, intptr_t, std::less<value>, \
std::allocator<std::pair<const value, intptr_t>>>
BTREE_TYPES(int32_t);
BTREE_TYPES(int64_t);
BTREE_TYPES(StdString);
BTREE_TYPES(Cord);
BTREE_TYPES(Time);
#define MY_BENCHMARK4(type, func) \
void BM_##type##_##func(benchmark::State& state) { BM_##func<type>(state); } \
BENCHMARK(BM_##type##_##func)
#define MY_BENCHMARK3_STL(type) \
MY_BENCHMARK4(type, Insert); \
MY_BENCHMARK4(type, InsertSorted); \
MY_BENCHMARK4(type, InsertSmall); \
MY_BENCHMARK4(type, Lookup); \
MY_BENCHMARK4(type, FullLookup); \
MY_BENCHMARK4(type, Erase); \
MY_BENCHMARK4(type, EraseRange); \
MY_BENCHMARK4(type, QueueAddRem); \
MY_BENCHMARK4(type, MixedAddRem); \
MY_BENCHMARK4(type, Fifo); \
MY_BENCHMARK4(type, FwdIter); \
MY_BENCHMARK4(type, InsertRangeRandom); \
MY_BENCHMARK4(type, InsertRangeSorted)
#define MY_BENCHMARK3(type) \
MY_BENCHMARK4(type, EraseIf); \
MY_BENCHMARK3_STL(type)
#define MY_BENCHMARK2_SUPPORTS_MULTI_ONLY(type) \
MY_BENCHMARK3_STL(stl_##type); \
MY_BENCHMARK3_STL(stl_unordered_##type); \
MY_BENCHMARK3(btree_256_##type)
#define MY_BENCHMARK2(type) \
MY_BENCHMARK2_SUPPORTS_MULTI_ONLY(type); \
MY_BENCHMARK3(flat_hash_##type)
// Define MULTI_TESTING to see benchmarks for multi-containers also.
//
// You can use --copt=-DMULTI_TESTING.
#ifdef MULTI_TESTING
#define MY_BENCHMARK(type) \
MY_BENCHMARK2(set_##type); \
MY_BENCHMARK2(map_##type); \
MY_BENCHMARK2_SUPPORTS_MULTI_ONLY(multiset_##type); \
MY_BENCHMARK2_SUPPORTS_MULTI_ONLY(multimap_##type)
#else
#define MY_BENCHMARK(type) \
MY_BENCHMARK2(set_##type); \
MY_BENCHMARK2(map_##type)
#endif
MY_BENCHMARK(int32_t);
MY_BENCHMARK(int64_t);
MY_BENCHMARK(StdString);
MY_BENCHMARK(Cord);
MY_BENCHMARK(Time);
// Define a type whose size and cost of moving are independently customizable.
// When sizeof(value_type) increases, we expect btree to no longer have as much
// cache-locality advantage over STL. When cost of moving increases, we expect
// btree to actually do more work than STL because it has to move values around
// and STL doesn't have to.
template <int Size, int Copies>
struct BigType {
BigType() : BigType(0) {}
explicit BigType(int x) { std::iota(values.begin(), values.end(), x); }
void Copy(const BigType& other) {
for (int i = 0; i < Size && i < Copies; ++i) values[i] = other.values[i];
// If Copies > Size, do extra copies.
for (int i = Size, idx = 0; i < Copies; ++i) {
int64_t tmp = other.values[idx];
benchmark::DoNotOptimize(tmp);
idx = idx + 1 == Size ? 0 : idx + 1;
}
}
BigType(const BigType& other) { Copy(other); }
BigType& operator=(const BigType& other) {
Copy(other);
return *this;
}
// Compare only the first Copies elements if Copies is less than Size.
bool operator<(const BigType& other) const {
return std::lexicographical_compare(
values.begin(), values.begin() + std::min(Size, Copies),
other.values.begin(), other.values.begin() + std::min(Size, Copies));
}
bool operator==(const BigType& other) const {
return std::equal(values.begin(), values.begin() + std::min(Size, Copies),
other.values.begin());
}
// Support absl::Hash.
template <typename State>
friend State AbslHashValue(State h, const BigType& b) {
for (int i = 0; i < Size && i < Copies; ++i)
h = State::combine(std::move(h), b.values[i]);
return h;
}
std::array<int64_t, Size> values;
};
#define BIG_TYPE_BENCHMARKS(SIZE, COPIES) \
using stl_set_size##SIZE##copies##COPIES = std::set<BigType<SIZE, COPIES>>; \
using stl_map_size##SIZE##copies##COPIES = \
std::map<BigType<SIZE, COPIES>, intptr_t>; \
using stl_multiset_size##SIZE##copies##COPIES = \
std::multiset<BigType<SIZE, COPIES>>; \
using stl_multimap_size##SIZE##copies##COPIES = \
std::multimap<BigType<SIZE, COPIES>, intptr_t>; \
using stl_unordered_set_size##SIZE##copies##COPIES = \
std::unordered_set<BigType<SIZE, COPIES>, \
absl::Hash<BigType<SIZE, COPIES>>>; \
using stl_unordered_map_size##SIZE##copies##COPIES = \
std::unordered_map<BigType<SIZE, COPIES>, intptr_t, \
absl::Hash<BigType<SIZE, COPIES>>>; \
using flat_hash_set_size##SIZE##copies##COPIES = \
flat_hash_set<BigType<SIZE, COPIES>>; \
using flat_hash_map_size##SIZE##copies##COPIES = \
flat_hash_map<BigType<SIZE, COPIES>, intptr_t>; \
using stl_unordered_multiset_size##SIZE##copies##COPIES = \
std::unordered_multiset<BigType<SIZE, COPIES>, \
absl::Hash<BigType<SIZE, COPIES>>>; \
using stl_unordered_multimap_size##SIZE##copies##COPIES = \
std::unordered_multimap<BigType<SIZE, COPIES>, intptr_t, \
absl::Hash<BigType<SIZE, COPIES>>>; \
using btree_256_set_size##SIZE##copies##COPIES = \
btree_set<BigType<SIZE, COPIES>>; \
using btree_256_map_size##SIZE##copies##COPIES = \
btree_map<BigType<SIZE, COPIES>, intptr_t>; \
using btree_256_multiset_size##SIZE##copies##COPIES = \
btree_multiset<BigType<SIZE, COPIES>>; \
using btree_256_multimap_size##SIZE##copies##COPIES = \
btree_multimap<BigType<SIZE, COPIES>, intptr_t>; \
MY_BENCHMARK(size##SIZE##copies##COPIES)
// Define BIG_TYPE_TESTING to see benchmarks for more big types.
//
// You can use --copt=-DBIG_TYPE_TESTING.
#ifndef NODESIZE_TESTING
#ifdef BIG_TYPE_TESTING
BIG_TYPE_BENCHMARKS(1, 4);
BIG_TYPE_BENCHMARKS(4, 1);
BIG_TYPE_BENCHMARKS(4, 4);
BIG_TYPE_BENCHMARKS(1, 8);
BIG_TYPE_BENCHMARKS(8, 1);
BIG_TYPE_BENCHMARKS(8, 8);
BIG_TYPE_BENCHMARKS(1, 16);
BIG_TYPE_BENCHMARKS(16, 1);
BIG_TYPE_BENCHMARKS(16, 16);
BIG_TYPE_BENCHMARKS(1, 32);
BIG_TYPE_BENCHMARKS(32, 1);
BIG_TYPE_BENCHMARKS(32, 32);
#else
BIG_TYPE_BENCHMARKS(32, 32);
#endif
#endif
// Benchmark using unique_ptrs to large value types. In order to be able to use
// the same benchmark code as the other types, use a type that holds a
// unique_ptr and has a copy constructor.
template <int Size>
struct BigTypePtr {
BigTypePtr() : BigTypePtr(0) {}
explicit BigTypePtr(int x) {
ptr = absl::make_unique<BigType<Size, Size>>(x);
}
BigTypePtr(const BigTypePtr& other) {
ptr = absl::make_unique<BigType<Size, Size>>(*other.ptr);
}
BigTypePtr(BigTypePtr&& other) noexcept = default;
BigTypePtr& operator=(const BigTypePtr& other) {
ptr = absl::make_unique<BigType<Size, Size>>(*other.ptr);
}
BigTypePtr& operator=(BigTypePtr&& other) noexcept = default;
bool operator<(const BigTypePtr& other) const { return *ptr < *other.ptr; }
bool operator==(const BigTypePtr& other) const { return *ptr == *other.ptr; }
std::unique_ptr<BigType<Size, Size>> ptr;
};
template <int Size>
double ContainerInfo(const btree_set<BigTypePtr<Size>>& b) {
const double bytes_used =
b.bytes_used() + b.size() * sizeof(BigType<Size, Size>);
const double bytes_per_value = bytes_used / b.size();
BtreeContainerInfoLog(b, bytes_used, bytes_per_value);
return bytes_per_value;
}
template <int Size>
double ContainerInfo(const btree_map<int, BigTypePtr<Size>>& b) {
const double bytes_used =
b.bytes_used() + b.size() * sizeof(BigType<Size, Size>);
const double bytes_per_value = bytes_used / b.size();
BtreeContainerInfoLog(b, bytes_used, bytes_per_value);
return bytes_per_value;
}
#define BIG_TYPE_PTR_BENCHMARKS(SIZE) \
using stl_set_size##SIZE##copies##SIZE##ptr = std::set<BigType<SIZE, SIZE>>; \
using stl_map_size##SIZE##copies##SIZE##ptr = \
std::map<int, BigType<SIZE, SIZE>>; \
using stl_unordered_set_size##SIZE##copies##SIZE##ptr = \
std::unordered_set<BigType<SIZE, SIZE>, \
absl::Hash<BigType<SIZE, SIZE>>>; \
using stl_unordered_map_size##SIZE##copies##SIZE##ptr = \
std::unordered_map<int, BigType<SIZE, SIZE>>; \
using flat_hash_set_size##SIZE##copies##SIZE##ptr = \
flat_hash_set<BigType<SIZE, SIZE>>; \
using flat_hash_map_size##SIZE##copies##SIZE##ptr = \
flat_hash_map<int, BigTypePtr<SIZE>>; \
using btree_256_set_size##SIZE##copies##SIZE##ptr = \
btree_set<BigTypePtr<SIZE>>; \
using btree_256_map_size##SIZE##copies##SIZE##ptr = \
btree_map<int, BigTypePtr<SIZE>>; \
MY_BENCHMARK3_STL(stl_set_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3_STL(stl_unordered_set_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3(flat_hash_set_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3(btree_256_set_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3_STL(stl_map_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3_STL(stl_unordered_map_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3(flat_hash_map_size##SIZE##copies##SIZE##ptr); \
MY_BENCHMARK3(btree_256_map_size##SIZE##copies##SIZE##ptr)
BIG_TYPE_PTR_BENCHMARKS(32);
void BM_BtreeSet_IteratorSubtraction(benchmark::State& state) {
absl::InsecureBitGen bitgen;
std::vector<int> vec;
// Randomize the set's insertion order so the nodes aren't all full.
vec.reserve(state.range(0));
for (int i = 0; i < state.range(0); ++i) vec.push_back(i);
absl::c_shuffle(vec, bitgen);
absl::btree_set<int> set;
for (int i : vec) set.insert(i);
size_t distance = absl::Uniform(bitgen, 0u, set.size());
while (state.KeepRunningBatch(distance)) {
size_t end = absl::Uniform(bitgen, distance, set.size());
size_t begin = end - distance;
benchmark::DoNotOptimize(set.find(static_cast<int>(end)) -
set.find(static_cast<int>(begin)));
distance = absl::Uniform(bitgen, 0u, set.size());
}
}
BENCHMARK(BM_BtreeSet_IteratorSubtraction)->Range(1 << 10, 1 << 20);
} // namespace
} // namespace container_internal
ABSL_NAMESPACE_END
} // namespace absl