Rotom

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📃Introduction

Welcome to rotom, an embedded high-performance key-value in-memory database written in Go, has many built-in data types, support for persistence and data recovery.

Currently features:

1. Built-in data types string, map, set, list, zset, and bitmap.
2. Second level ttl supported for each key-value pair.
3. Based on GigaCache, which is managing GB-level data, saving 50% memory compared to stdmap, with better performance and reduced GC overhead.
4. Internal encoding/decoding lib that more effective than protobuf.
5. Persistent log support, and can recover database from logs.

If you want to know more technical details, check out doc.

🚚Usage

Before using, please install rotom into your project first:

go get github.com/xgzlucario/rotom

And install the gofakeit library for generating some random data:

go get github.com/brianvoe/gofakeit/v6

Run the sample program:

package main

import (
	"fmt"
	"time"

	"github.com/brianvoe/gofakeit/v6"
	"github.com/xgzlucario/rotom"
)

func main() {
	db, err := rotom.Open(rotom.DefaultOptions)
	if err != nil {
		panic(err)
	}
	defer db.Close()

	// Set
	for i := 0; i < 10000; i++ {
		phone := gofakeit.Phone()
        user := []byte(gofakeit.Username())
		// Set bytes
		db.Set(phone, user)
		// Or set with ttl
		db.SetEx(phone, user, time.Minute)
		// Or set with deadline
		db.SetTx(phone, user, time.Now().Add(time.Minute).UnixNano())
	}
    
	// Get
	key := gofakeit.Phone()
	user, ttl, ok := db.Get(key)
	// ...
}

🚀Performance

Rotom has super multi-threading performance. The following is the bench test data.

Test Environment

goos: linux
goarch: amd64
pkg: github.com/xgzlucario/GigaCache
cpu: 13th Gen Intel(R) Core(TM) i5-13600KF

Benchmark

========== Set ==========
size: 100*10000 enties
desc: key 10 bytes, value 10 bytes
cost: 1.120466957s
qps: 892467.60
50th: 990 ns
90th: 1107 ns
99th: 1724 ns

========== BatchSet ==========
size: 100*10000 enties
desc: key 10 bytes, value 10 bytes, 100 key-values a batch
cost: 377.933308ms
qps: 2645850.27
50th: 20691 ns
90th: 32950 ns
99th: 95645 ns

========== Get ==========
size: 100*10000 enties
desc: key 10 bytes, value 10 bytes
cost: 255.023737ms
qps: 3920845.99
50th: 212 ns
90th: 267 ns
99th: 532 ns

========== Get 8 parallel ==========
size: 100*10000 enties
desc: key 10 bytes, value 10 bytes
cost: 138.089874ms
qps: 7240728.26
50th: 173 ns
90th: 261 ns
99th: 501 ns

========== RPush ==========
size: 100*10000 enties
desc: value 10 bytes
cost: 1.033844185s
qps: 967248.36
50th: 936 ns
90th: 1011 ns
99th: 1609 ns

========== HSet ==========
size: 100*10000 enties
desc: field 10 bytes, value 10 bytes
cost: 1.182939928s
qps: 845337.51
50th: 987 ns
90th: 1117 ns
99th: 1850 ns

========== BatchHSet ==========
size: 100*10000 enties
desc: field 10 bytes, value 10 bytes, 100 key-values a batch
cost: 365.036647ms
qps: 2739329.61
50th: 16900 ns
90th: 41312 ns
99th: 89803 ns

========== HGet ==========
size: 100*10000 enties
desc: field 10 bytes, value 10 bytes
cost: 292.57105ms
qps: 3417634.25
50th: 220 ns
90th: 306 ns
99th: 575 ns

========== BitSet ==========
size: 100*10000 enties
desc: offset uint32
cost: 916.172391ms
qps: 1091477.02
50th: 833 ns
90th: 880 ns
99th: 1219 ns

========== ZSet ==========
size: 100*10000 enties
desc: field 10 bytes, incr int64
cost: 1.209290877s
qps: 826918.57
50th: 1038 ns
90th: 1192 ns
99th: 2257 ns