GitHub - mhmtszr/concurrent-swiss-map: A high-performance, thread-safe generic concurrent hash map implementation with Swiss Map. (original) (raw)

Concurrent Swiss Map is an open-source Go library that provides a high-performance, thread-safe generic concurrent hash map implementation designed to handle concurrent access efficiently. It's built with a focus on simplicity, speed, and reliability, making it a solid choice for scenarios where concurrent access to a hash map is crucial.

Uses dolthub/swiss map implementation under the hood.

Installation

Supports 1.18+ Go versions because of Go Generics

go get github.com/mhmtszr/concurrent-swiss-map

Usage

New functions will be added soon...

package main

import ( "hash/fnv"

csmap "github.com/mhmtszr/concurrent-swiss-map"

)

func main() { myMap := csmap.New[string, int]( // set the number of map shards. the default value is 32. csmap.WithShardCountstring, int,

    // if don't set custom hasher, use the built-in maphash.
    csmap.WithCustomHasher[string, int](func(key string) uint64 {
        hash := fnv.New64a()
        hash.Write([]byte(key))
        return hash.Sum64()
    }),

    // set the total capacity, every shard map has total capacity/shard count capacity. the default value is 0.
    csmap.WithSize[string, int](1000),
)

key := "swiss-map"
myMap.Store(key, 10)

val, ok := myMap.Load(key)
println("load val:", val, "exists:", ok)

deleted := myMap.Delete(key)
println("deleted:", deleted)

ok = myMap.Has(key)
println("has:", ok)

empty := myMap.IsEmpty()
println("empty:", empty)

myMap.SetIfAbsent(key, 11)

myMap.Range(func(key string, value int) (stop bool) {
    println("range:", key, value)
    return true
})

count := myMap.Count()
println("count:", count)

// Output:
// load val: 10 exists: true
// deleted: true
// has: false
// empty: true
// range: swiss-map 11
// count: 1

}

Basic Architecture

Benchmark Test

Benchmark was made on:

• Apple M1 Max
• 32 GB memory

Benchmark test results can be obtained by running this file on local computers.

Benchmark Results

• Memory usage of the concurrent swiss map is better than other map implementations in all checked test scenarios.
• In high concurrent systems, the concurrent swiss map is faster, but in systems containing few concurrent operations, it works similarly to RWMutexMap.