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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
+//
+// https://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.
+
+#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 "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/flags/flag.h"
+#include "absl/hash/hash.h"
+#include "absl/memory/memory.h"
+#include "absl/strings/str_format.h"
+#include "absl/time/time.h"
+#include "benchmark/benchmark.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);
+}
+
+// container::insert sometimes returns a pair<iterator, bool> and sometimes
+// returns an iterator (for multi- containers).
+template <typename Iter>
+Iter GetIterFromInsert(const std::pair<Iter, bool>& pair) {
+ return pair.first;
+}
+template <typename Iter>
+Iter GetIterFromInsert(const Iter iter) {
+ return iter;
+}
+
+// Benchmark insertion of values into a container at the end.
+template <typename T>
+void BM_InsertEnd(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;
+
+ T container;
+ const int kSize = 10000;
+ for (int i = 0; i < kSize; ++i) {
+ container.insert(Generator<V>(kSize)(i));
+ }
+ V v = Generator<V>(kSize)(kSize - 1);
+ typename T::key_type k = key_of_value(v);
+
+ auto it = container.find(k);
+ while (state.KeepRunning()) {
+ // Repeatedly removing then adding v.
+ container.erase(it);
+ it = GetIterFromInsert(container.insert(v));
+ }
+}
+
+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 deletion of values from a container.
+template <typename T>
+void BM_Delete(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 deletion of multiple values from a container.
+template <typename T>
+void BM_DeleteRange(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();
+ }
+}
+
+// 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(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(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(Time);
+
+#define MY_BENCHMARK4(type, func) \
+ void BM_##type##_##func(benchmark::State& state) { BM_##func<type>(state); } \
+ BENCHMARK(BM_##type##_##func)
+
+#define MY_BENCHMARK3(type) \
+ MY_BENCHMARK4(type, Insert); \
+ MY_BENCHMARK4(type, InsertSorted); \
+ MY_BENCHMARK4(type, InsertEnd); \
+ MY_BENCHMARK4(type, Lookup); \
+ MY_BENCHMARK4(type, FullLookup); \
+ MY_BENCHMARK4(type, Delete); \
+ MY_BENCHMARK4(type, DeleteRange); \
+ 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_BENCHMARK2_SUPPORTS_MULTI_ONLY(type) \
+ MY_BENCHMARK3(stl_##type); \
+ MY_BENCHMARK3(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(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& x) {
+ for (int i = 0; i < Size && i < Copies; ++i) values[i] = x.values[i];
+ // If Copies > Size, do extra copies.
+ for (int i = Size, idx = 0; i < Copies; ++i) {
+ int64_t tmp = x.values[idx];
+ benchmark::DoNotOptimize(tmp);
+ idx = idx + 1 == Size ? 0 : idx + 1;
+ }
+ }
+
+ BigType(const BigType& x) { Copy(x); }
+ BigType& operator=(const BigType& x) {
+ Copy(x);
+ 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& x) {
+ ptr = absl::make_unique<BigType<Size, Size>>(*x.ptr);
+ }
+ BigTypePtr(BigTypePtr&& x) noexcept = default;
+ BigTypePtr& operator=(const BigTypePtr& x) {
+ ptr = absl::make_unique<BigType<Size, Size>>(*x.ptr);
+ }
+ BigTypePtr& operator=(BigTypePtr&& x) 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_set_size##SIZE##copies##SIZE##ptr); \
+ MY_BENCHMARK3(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_map_size##SIZE##copies##SIZE##ptr); \
+ MY_BENCHMARK3(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);
+
+} // namespace
+} // namespace container_internal
+ABSL_NAMESPACE_END
+} // namespace absl
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-01 13:18:03 +0000