Is Sparsepp faster than std::unordered_map?

Yes, benchmarks in the README show Sparsepp typically outperforms std::unordered_map in both speed and memory usage, thanks to its open addressing approach and efficient resizing that avoids large memory spikes.

How do I handle custom hash functions with Sparsepp?

You can provide a hash function via the HashFcn template parameter or specialize std::hash for your class, using spp::hash_combine from sparsepp/spp_utils.h to combine fields, as illustrated in the Person struct example.

What happens to iterators when I erase elements in Sparsepp?

Iterators are often invalidated on erase, but the erase function returns an iterator to the next element, allowing safe traversal and deletion. This differs from std::unordered_map where iterators might remain valid depending on the implementation.

Sparsepp vs parallel hashmap: which one should I choose?

The author recommends parallel hashmap for most cases as it offers significantly better speed with slightly higher memory usage. Use Sparsepp only if you need the lowest memory overhead or are stuck with non-C++11 compilers.

How to serialize a hash map using Sparsepp?

Implement a serializer class with operator() for your data types, then call map.serialize(serializer, stream). The README provides a full FileSerializer example using stdio, demonstrating how to save and load hash map data efficiently.

Does Sparsepp work well on Windows?

Yes, it's tested on Windows with Visual Studio 2010-2015, but be cautious with the SPP_USE_SPP_ALLOC flag—it helps with large maps but can increase memory for small ones due to allocator fragmentation.

Open-Awesome

sparsepp

NOASSERTIONC++1.22

A fast, memory-efficient hash map implementation for C++ with minimal memory overhead during resizing.

GitHub

1.3k stars128 forks0 contributors

What is sparsepp?

Sparsepp is a C++ hash map library that provides a fast, memory-efficient alternative to std::unordered_map and std::unordered_set. It solves the problem of high memory overhead during resizing in open-addressing hash tables while maintaining excellent performance and low per-entry memory usage.

Target Audience

C++ developers who need high-performance associative containers with minimal memory footprint, particularly those working in memory-constrained environments or with large datasets.

Value Proposition

Developers choose Sparsepp for its unique combination of speed and memory efficiency, offering better performance than standard unordered_map implementations with significantly lower memory overhead, especially during resize operations.

Overview

A fast, memory efficient hash map for C++

Use Cases

Best For

Applications requiring hash maps with minimal memory overhead
High-performance C++ code where std::unordered_map is a bottleneck
Memory-constrained environments like embedded systems
Scenarios where hash tables need frequent resizing
Drop-in replacement for std::unordered_map with better performance
Situations needing serialization of hash map data structures

Not Ideal For

Projects where maximum speed is the top priority and higher memory usage is acceptable, as the author now recommends parallel hashmap for better performance.
Applications requiring stable iterators or references to elements after insert or erase operations, due to Sparsepp's invalidation behavior unlike std::unordered_map.
Environments with many small hash map instances on Windows, where the custom allocator can increase memory fragmentation and overhead.
Teams seeking the most current library recommendations, since the maintainer favors parallel hashmap for most new use cases.

Pros & Cons

Pros

Extremely Low Memory Usage

Typically adds only about one byte overhead per entry and minimizes memory spikes during resizing, making it ideal for memory-constrained applications like embedded systems.

High Performance

Benchmarks show it often outperforms std::unordered_map and Boost implementations, providing faster lookups and insertions due to its open addressing design.

Drop-in Compatibility

API is identical to std::unordered_map and unordered_set, allowing seamless replacement without code changes, as demonstrated in the example usage.

Built-in Serialization

Includes serialize() and unserialize() methods with customizable serializers, simplifying data persistence to files or networks, as shown in the FileSerializer example.

Cons

Iterator and Reference Invalidation

Erasing or inserting elements frequently invalidates iterators and references, complicating code that relies on element stability, a drawback not present in std::unordered_map.

Windows Allocator Overhead

On Windows, the custom allocator (SPP_USE_SPP_ALLOC) can waste memory for small hash maps by creating new 4K memory spaces per instance, increasing overhead in some scenarios.

Outdated Recommendation

The author now recommends parallel hashmap for a better trade-off, indicating Sparsepp may be suboptimal for new projects unless specific constraints like non-C++11 compilers apply.

Frequently Asked Questions

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