c++ - Is an unordered_map really faster than a map in practice?

Sure, the lookup performance of an unordered_map is constant on average, and the lookup performance of a map is O(logN).

But of course in order to find an object in an unordered_map, we have to:

hash the key we want to find.

equality_compare the key with every key in the same bucket.

Whereas in a map, we simply need to less_than compare the sought key with log2(N) keys, where N is the number of items in the map.

I wondered what the real performance difference would be, given that the hash function adds overhead and an equality_compare is no cheaper than a less_than compare.

Rather than bother the community with a question I could answer myself, I wrote a test.

I have shared the results below, in case anyone else finds this interesting or useful.

More answers are of course invited if someone is able and willing to add more information.

Answer

In response to questions about performance in relation to the number of missed searches, I have refactored the test to parameterise this.

Example results:

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

Key:

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.

TL;DR

Results: the unordered_map shows its superiority as soon as there is data in the map. The only time it exhibits worse performance than the ordered map is when the maps are empty.

Here's the new code:

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

#include 
#include 
#include 
#include 
#include 
#include 

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 make_sink(const std::string& name)
{
    if (name == "print")
    {
        return make_unique();

    }
    if (name == "noprint")
    {
        return make_unique();
    }
    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& l, const string& r) const {
        return l.first < r;
    }

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


template
auto time_test(F&& f, const vector 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 unordered;

    map ordered;
    vector> flat_map;

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

    // generate keys
    auto eng = std::default_random_engine(std::random_device()());

    auto alpha_dist = std::uniform_int_distribution('A', 'Z');
    auto prob_dist = std::uniform_real_distribution(0, 1.0 - std::numeric_limits::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;
}

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Saturday 13 April 2019

c++ - Is an unordered_map really faster than a map in practice?

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