shykoe / hashmap

An open addressing linear probing hash table, tuned for delete heavy workloads

HashMap.h

A hash table mostly compatible with the C++11 std::unordered_map interface, but with much higher performance for many workloads.

Implementation

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

Usage

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.

Example

  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 << hm.at("1") << "\n";

  // Erase entry
  hm.erase(1);

Benchmark

A benchmark src/HashMapBenchmark.cpp is included with the sources. The benchmark simulates a delete heavy workload where items are repeatedly inserted and deleted.

I ran this benchmark on the following configuration:

• AMD Ryzen 9 3900X
• Linux 5.8.4-200.fc32.x86_64
• gcc (GCC) 10.2.1 20200723 (Red Hat 10.2.1-1)
• Isolated a core complex (CCX) using isolcpus for running the benchmark

When working set fits in L3 cache (HashMapBenchmark -c 100000 -i 100000000):

Implementation	mean ns/iter	max ns/iter
HashMap	24	1082
absl::flat_hash_map	24	2074
google::dense_hash_map	49	689846
std::unordered_map	67	10299

When working set is larger than L3 cache (HashMapBenchmark -c 10000000 -i 1000000000):

Implementation	mean ns/iter	max ns/iter
HashMap	75	19026
absl::flat_hash_map	101	19848
google::dense_hash_map	111	226083255
std::unordered_map	408	22422

Cited by

HashMap has been cited by the following papers:

• Koppl, Dominik. “Separate Chaining Meets Compact Hashing.” (2019). https://arxiv.org/abs/1905.00163

About

This project was created by Erik Rigtorp <[email protected]>.