An open addressing linear probing hash table, tuned for delete heavy workloads
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A hash table mostly compatible with the C++11 std::unordered_map interface, but with much higher performance for many workloads.


This hash table uses open addressing with linear probing and backshift deletion. Open addressing and linear probing minimizes memory allocations and achives high cache effiency. Backshift deletion keeps performance high for delete heavy workloads by not clobbering the hash table with tombestones.


HashMap is mostly compatible with the C++11 container interface. The main differences are:

  • A key value to represent the empty key is required.
  • Key and T needs to be default constructible.
  • Iterators are invalidated on all modifying operations.
  • It's invalid to perform any operations with the empty key.
  • Destructors are not called on erase.
  • Extensions for lookups using related key types.

Member functions:

  • HashMap(size_type bucket_count, key_type empty_key);

    Construct a HashMap with bucket_count buckets and empty_key as the empty key.

The rest of the member functions are implemented as for std::unordered_map.


  using namespace rigtorp;

  // Hash for using std::string as lookup key
  struct Hash {
    size_t operator()(int v) { return v * 7; }
    size_t operator()(const std::string &v) { return std::stoi(v) * 7; }

  // Equal comparison for using std::string as lookup key
  struct Equal {
    bool operator()(int lhs, int rhs) { return lhs == rhs; }
    bool operator()(int lhs, const std::string &rhs) {
      return lhs == std::stoi(rhs);

  // Create a HashMap with 16 buckets and 0 as the empty key
  HashMap<int, int, Hash, Equal> hm(16, 0);
  hm.emplace(1, 1);
  hm[2] = 2;

  // Iterate and print key-value pairs
  for (const auto &e : hm) {
    std::cout << e.first << " = " << e.second << "\n";

  // Lookup using std::string
  std::cout <<"1") << "\n";

  // Erase entry


The benchmark first inserts 1M random entries in the table and then removes the last inserted item and inserts a new random entry 1 billion times. This is benchmark is designed to simulate a delete heavy workload.

Implementation ns/iter
HashMap 77
google::dense_hash_map 122
std::unordered_map 220


This project was created by Erik Rigtorp <>.