当然，unordered_map 的查找性能平均是恒定的，而 map 的查找性能是 O(logN)。

当然，为了在 unordered_map 中找到一个对象，我们必须:

1. 散列我们想要找到的 key 。
2. equality_将 key 与同一存储桶中的每个 key 进行比较。

而在 map 中，我们只需要将查找到的键与 log2(N) 个键进行 less_than 比较，其中 N 是 map 中的项目数。

我想知道真正的性能差异是什么，因为散列函数会增加开销并且 equality_compare 并不比 less_than 比较便宜。

我没有用自己可以回答的问题来打扰社区，而是编写了一个测试。

我已经在下面分享了结果，以防其他人觉得这有趣或有用。

如果有人能够并愿意添加更多信息，当然会邀请更多答案。

最佳答案

为了回答与错过搜索次数相关的性能问题，我重构了测试以对其进行参数化。

示例结果:

searches=1000000 set_size=      0 miss=    100% ordered=   4384 unordered=  12901 flat_map=    681
searches=1000000 set_size=     99 miss=  99.99% ordered=  89127 unordered=  42615 flat_map=  86091
searches=1000000 set_size=    172 miss=  99.98% ordered= 101283 unordered=  53468 flat_map=  96008
searches=1000000 set_size=    303 miss=  99.97% ordered= 112747 unordered=  53211 flat_map= 107343
searches=1000000 set_size=    396 miss=  99.96% ordered= 124179 unordered=  59655 flat_map= 112687
searches=1000000 set_size=    523 miss=  99.95% ordered= 132180 unordered=  51133 flat_map= 121669
searches=1000000 set_size=    599 miss=  99.94% ordered= 135850 unordered=  55078 flat_map= 121072
searches=1000000 set_size=    695 miss=  99.93% ordered= 140204 unordered=  60087 flat_map= 124961
searches=1000000 set_size=    795 miss=  99.92% ordered= 146071 unordered=  64790 flat_map= 127873
searches=1000000 set_size=    916 miss=  99.91% ordered= 154461 unordered=  50944 flat_map= 133194
searches=1000000 set_size=    988 miss=   99.9% ordered= 156327 unordered=  54094 flat_map= 134288

键:

searches = number of searches performed against each map
set_size = how big each map is (and therefore how many of the searches will result in a hit)
miss = the probability of generating a missed search. Used for generating searches and set_size.
ordered = the time spent searching the ordered map
unordered = the time spent searching the unordered_map
flat_map = the time spent searching the flat map

note: time is measured in std::system_clock::duration ticks.

长话短说

结果:unordered_map一有数据就显示出优越性。它表现出比有序 map 更差的唯一时间是 map 为空时。

这是新代码:

#include <iostream>
#include <iomanip>
#include <random>
#include <algorithm>
#include <string>
#include <vector>
#include <map>
#include <unordered_map>
#include <unordered_set>
#include <chrono>
#include <tuple>
#include <future>
#include <stdexcept>
#include <sstream>

using namespace std;

// this sets the length of the string we will be using as a key.
// modify this to test whether key complexity changes the performance ratios
// of the various maps
static const size_t key_length = 20;

// the number of keys we will generate (the size of the test)
const size_t nkeys = 1000000;



// use a virtual method to prevent the optimiser from detecting that
// our sink function actually does nothing. otherwise it might skew the test
struct string_user
{
    virtual void sink(const std::string&) = 0;
    virtual ~string_user() = default;
};

struct real_string_user : string_user
{
    virtual void sink(const std::string&) override
    {

    }
};

struct real_string_user_print : string_user
{
    virtual void sink(const std::string& s) override
    {
        cout << s << endl;
    }
};

// generate a sink from a string - this is a runtime operation and therefore
// prevents the optimiser from realising that the sink does nothing
std::unique_ptr<string_user> make_sink(const std::string& name)
{
    if (name == "print")
    {
        return make_unique<real_string_user_print>();
    }
    if (name == "noprint")
    {
        return make_unique<real_string_user>();
    }
    throw logic_error(name);
}

// generate a random key, given a random engine and a distribution
auto gen_string = [](auto& engine, auto& dist)
{
    std::string result(key_length, ' ');
    generate(begin(result), end(result), [&] {
        return dist(engine);
    });
    return result;
};

// comparison predicate for our flat map.
struct pair_less
{
    bool operator()(const pair<string, string>& l, const string& r) const {
        return l.first < r;
    }

    bool operator()(const string& l, const pair<string, string>& r) const {
        return l < r.first;
    }
};

template<class F>
auto time_test(F&& f, const vector<string> keys)
{
    auto start_time = chrono::system_clock::now();

    for (auto const& key : keys)
    {
        f(key);
    }

    auto stop_time = chrono::system_clock::now();
    auto diff =  stop_time - start_time;
    return diff;
}

struct report_key
{
    size_t nkeys;
    int miss_chance;
};

std::ostream& operator<<(std::ostream& os, const report_key& key)
{
    return os << "miss=" << setw(2) << key.miss_chance << "%";
}

void run_test(string_user& sink, size_t nkeys, double miss_prob)
{
    // the types of map we will test
    unordered_map<string, string> unordered;
    map<string, string> ordered;
    vector<pair<string, string>> flat_map;

    // a vector of all keys, which we can shuffle in order to randomise
    // access order of all our maps consistently
    vector<string> keys;
    unordered_set<string> keys_record;

    // generate keys
    auto eng = std::default_random_engine(std::random_device()());
    auto alpha_dist = std::uniform_int_distribution<char>('A', 'Z');
    auto prob_dist = std::uniform_real_distribution<double>(0, 1.0 - std::numeric_limits<double>::epsilon());

    auto generate_new_key = [&] {
        while(true) {
            // generate a key
            auto key = gen_string(eng, alpha_dist);
            // try to store it in the unordered map
            // if it already exists, force a regeneration
            // otherwise also store it in the ordered map and the flat map
            if(keys_record.insert(key).second) {
                return key;
            }
        }
    };

    for (size_t i = 0 ; i < nkeys ; ++i)
    {
        bool inserted = false;
        auto value = to_string(i);

        auto key = generate_new_key();
        if (prob_dist(eng) >= miss_prob) {
            unordered.emplace(key, value);
            flat_map.emplace_back(key, value);
            ordered.emplace(key, std::move(value));
        }
        // record the key for later use
        keys.emplace_back(std::move(key));
    }
    // turn our vector 'flat map' into an actual flat map by sorting it by pair.first. This is the key.
    sort(begin(flat_map), end(flat_map),
         [](const auto& l, const auto& r) { return l.first < r.first; });

    // shuffle the keys to randomise access order
    shuffle(begin(keys), end(keys), eng);

    auto unordered_lookup = [&](auto& key) {
        auto i = unordered.find(key);
        if (i != end(unordered)) {
            sink.sink(i->second);
        }
    };

    auto ordered_lookup = [&](auto& key) {
        auto i = ordered.find(key);
        if (i != end(ordered)) {
            sink.sink(i->second);
        }
    };

    auto flat_map_lookup = [&](auto& key) {
        auto i = lower_bound(begin(flat_map),
                             end(flat_map),
                             key,
                             pair_less());
        if (i != end(flat_map) && i->first == key) {
            sink.sink(i->second);
        }
    };

    // spawn a thread to time access to the unordered map
    auto unordered_future = async(launch::async,
                                  [&]()
                                  {
                                      return time_test(unordered_lookup, keys);
                                  });

    // spawn a thread to time access to the ordered map
    auto ordered_future = async(launch::async, [&]
                                {
                                    return time_test(ordered_lookup, keys);
                                });

    // spawn a thread to time access to the flat map
    auto flat_future = async(launch::async, [&]
                             {
                                 return time_test(flat_map_lookup, keys);
                             });

    // synchronise all the threads and get the timings
    auto ordered_time = ordered_future.get();
    auto unordered_time = unordered_future.get();
    auto flat_time = flat_future.get();

    cout << "searches=" << setw(7) << nkeys;
    cout << " set_size=" << setw(7) << unordered.size();
    cout << " miss=" << setw(7) << setprecision(6) << miss_prob * 100.0 << "%";
    cout << " ordered=" << setw(7) << ordered_time.count();
    cout << " unordered=" << setw(7) << unordered_time.count();
    cout << " flat_map=" << setw(7) << flat_time.count() << endl;

}

int main()
{
    // generate the sink, preventing the optimiser from realising what it
    // does.
    stringstream ss;
    ss << "noprint";
    string arg;
    ss >> arg;
    auto puser = make_sink(arg);

    for (double chance = 1.0 ; chance >= 0.0 ; chance -= 0.0001)
    {
        run_test(*puser, 1000000, chance);
    }


    return 0;
}

关于c++ - unordered_map 在实践中真的比 map 快吗？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/36392583/