Pros of jemalloc/jemalloc

• Scalability: jemalloc is designed to be highly scalable, with support for large-scale multi-threaded applications.
• Performance: jemalloc is known for its efficient memory management, leading to improved performance in many use cases.
• Fragmentation Reduction: jemalloc employs various techniques to minimize memory fragmentation, which can be a common issue with other memory allocators.

Cons of jemalloc/jemalloc

• Complexity: jemalloc is a relatively complex library, with a steep learning curve compared to simpler memory allocators.
• Overhead: The advanced features of jemalloc can introduce some overhead, which may not be desirable in all scenarios.
• Portability: While jemalloc is widely used, it may not be available or supported on all platforms, limiting its portability.

Code Comparison

Here's a brief code comparison between jemalloc/jemalloc and martinus/robin-hood-hashing:

jemalloc/jemalloc (memory allocation):

void* p = je_malloc(size);
if (p == NULL) {
    // Handle allocation failure
}
je_free(p);

martinus/robin-hood-hashing (hash table insertion):

robin_hood::unordered_map<std::string, int> map;
map.insert({"key", 42});

The jemalloc code demonstrates the basic usage of the je_malloc and je_free functions for dynamic memory allocation and deallocation. In contrast, the martinus/robin-hood-hashing code shows the insertion of a key-value pair into a Robin Hood hash table, which is a different data structure and use case compared to the memory allocator.

Pros of Abseil-CPP

• Comprehensive Utility Library: Abseil-CPP provides a wide range of utility functions and data structures, covering areas such as strings, containers, time, and more, making it a versatile tool for C++ development.
• Cross-Platform Compatibility: Abseil-CPP is designed to be cross-platform, ensuring compatibility across various operating systems and compilers.
• Active Development and Community: Abseil-CPP is actively maintained by the Abseil team and has a large and engaged community, ensuring ongoing support and improvements.

Cons of Abseil-CPP

• Larger Codebase: Abseil-CPP has a more extensive codebase compared to Robin Hood Hashing, which may result in a larger binary size and increased compilation times.
• Steeper Learning Curve: The breadth of features and functionality in Abseil-CPP may require more time and effort to learn and integrate into a project, especially for developers new to the library.
• Potential Dependency Overhead: Depending on the specific requirements of a project, integrating Abseil-CPP may introduce additional dependencies, which can increase the complexity of the build process.

Code Comparison

Here's a brief code comparison between Abseil-CPP and Robin Hood Hashing:

Abseil-CPP (absl::flat_hash_map):

#include <absl/container/flat_hash_map.h>

absl::flat_hash_map<std::string, int> map;
map["apple"] = 1;
map["banana"] = 2;
int value = map["apple"];  // value = 1

Robin Hood Hashing (tsl::robin_hood::unordered_map):

#include <tsl/robin_hood.h>

tsl::robin_hood::unordered_map<std::string, int> map;
map["apple"] = 1;
map["banana"] = 2;
int value = map["apple"];  // value = 1

Both examples demonstrate the usage of a hash map data structure, with Abseil-CPP providing the absl::flat_hash_map and Robin Hood Hashing providing the tsl::robin_hood::unordered_map. The code is largely similar, showcasing the insertion and retrieval of key-value pairs.

Pros of Folly

• Folly is a comprehensive library that provides a wide range of utilities and data structures, making it a powerful tool for building complex applications.
• Folly is actively maintained and has a large community of contributors, ensuring regular updates and bug fixes.
• Folly is used extensively by Facebook and other large tech companies, which means it has been thoroughly tested and optimized for performance.

Cons of Folly

• Folly has a larger codebase and a steeper learning curve compared to Robin Hood Hashing, which may be a drawback for smaller projects or teams.
• Folly may have a higher dependency footprint, which could be a concern for projects with strict resource constraints.

Code Comparison

Robin Hood Hashing:

template <typename Key, typename Value, typename Hash = std::hash<Key>, typename KeyEqual = std::equal_to<Key>>
class robin_hood_map : public robin_hood::unordered_map<Key, Value, Hash, KeyEqual> {
public:
    using base_type = robin_hood::unordered_map<Key, Value, Hash, KeyEqual>;
    using base_type::base_type;
};

Folly:

template <
    typename Key,
    typename Value,
    typename Hash = std::hash<Key>,
    typename KeyEqual = std::equal_to<Key>,
    typename Allocator = std::allocator<std::pair<const Key, Value>>>
class F14NodeMap : public folly::F14Map<Key, Value, Hash, KeyEqual, Allocator> {
public:
    using Base = folly::F14Map<Key, Value, Hash, KeyEqual, Allocator>;
    using Base::Base;
};

Pros of GSL

• GSL (Guidelines Support Library) is a collection of C++ core guidelines, providing a set of types, functions, and extensions to the C++ Standard Library.
• The library is actively maintained and supported by Microsoft, ensuring regular updates and bug fixes.
• GSL provides a comprehensive set of utilities and tools to help developers write safer and more robust C++ code.

Cons of GSL

• The library may have a steeper learning curve compared to Robin Hood Hashing, as it covers a broader range of functionality.
• The integration of GSL into existing codebases may require more effort than a more focused library like Robin Hood Hashing.
• The GSL library may have a larger footprint and dependencies compared to the more lightweight Robin Hood Hashing.

Code Comparison

Robin Hood Hashing:

template <typename Key, typename Value, typename Hash = std::hash<Key>, typename KeyEqual = std::equal_to<Key>>
class robin_hood_map : public robin_hood::unordered_map<Key, Value, Hash, KeyEqual> {
public:
    using base_type = robin_hood::unordered_map<Key, Value, Hash, KeyEqual>;
    using base_type::base_type;
};

GSL:

template <typename T, std::size_t Extent = dynamic_extent>
class span {
public:
    constexpr span() noexcept = default;
    constexpr span(pointer ptr, index_type count) noexcept;
    template <std::size_t N>
    constexpr span(element_type (&arr)[N]) noexcept;
};

Pros of Google Benchmark

• Provides a comprehensive set of tools for benchmarking C++ code, including support for different types of benchmarks (time, memory, etc.).
• Offers a user-friendly API for defining and running benchmarks, making it easy to integrate into existing projects.
• Generates detailed reports with statistics and visualizations, helping developers analyze the performance of their code.

Cons of Google Benchmark

• Requires more setup and configuration compared to Robin Hood Hashing, which is a single-header library.
• May have a higher learning curve for developers who are not familiar with the Google Benchmark framework.
• Adds more dependencies to a project, which can increase the overall project size and complexity.

Code Comparison

Google Benchmark:

#include <benchmark/benchmark.h>

static void BM_StringCreation(benchmark::State& state) {
  for (auto _ : state)
    std::string empty_string;
}
BENCHMARK(BM_StringCreation);

BENCHMARK_MAIN();

Robin Hood Hashing:

#include <robin_hood.h>

int main() {
  robin_hood::unordered_map<std::string, int> map;
  map["hello"] = 42;
  int value = map["hello"];
  return 0;
}