This is a modern cache implementation, inspired by the following papers, provides high efficiency.
- SIEVE | SIEVE is Simpler than LRU: an Efficient Turn-Key Eviction Algorithm for Web Caches (NSDI'24)
- S3-FIFO | FIFO queues are all you need for cache eviction (SOSP'23)
This offers state-of-the-art efficiency and scalability compared to other LRU-based cache algorithms.
import "github.com/scalalang2/golang-fifo/sieve"
size := 1e5
ttl := 0 // 0 means no expiration
cache := sieve.New[string, string](size, ttl)
// set value under hello
cache.Set("hello", "world")
// get value under hello
val, ok := cache.Get("hello")
if ok {
fmt.Printf("value: %s", val) // => "world"
}
// set more keys
for i := 0; i < 10; i++ {
cache.Set(fmt.Sprintf("key%d", i), fmt.Sprintf("value%d", i))
}
// get number of cache entries
fmt.Printf("len: %d", cache.Len()) // => 11
// remove value under hello
removed := cache.Remove("hello")
if removed {
fmt.Println("hello was removed")
}import "github.com/scalalang2/golang-fifo/sieve"
size := 1e5
ttl := time.Second * 3600 // 1 hour
cache := sieve.New[string, string](size, ttl)
// this callback will be called when the element is expired
cahe.SetOnEvict(func(key string, value string) {
fmt.Printf("key: %s, value: %s was evicted", key, value)
})
// set value under hello
cache.Set("hello", "world")
// remove all cache entries and stop the eviction goroutine.
cache.Close()The benchmark result were obtained using go-cache-benchmark
itemSize=500000, workloads=7500000, cacheSize=0.10%, zipf's alpha=0.99, concurrency=16
CACHE | HITRATE | QPS | HITS | MISSES
-----------------+---------+---------+---------+----------
sieve | 47.66% | 2508361 | 3574212 | 3925788
tinylfu | 47.37% | 2269542 | 3552921 | 3947079
s3-fifo | 47.17% | 1651619 | 3538121 | 3961879
slru | 46.49% | 2201350 | 3486476 | 4013524
s4lru | 46.09% | 2484266 | 3456682 | 4043318
two-queue | 45.49% | 1713502 | 3411800 | 4088200
clock | 37.34% | 2370417 | 2800750 | 4699250
lru-groupcache | 36.59% | 2206841 | 2743894 | 4756106
lru-hashicorp | 36.57% | 2055358 | 2743000 | 4757000
SIEVE delivers both high hit rates and the highest QPS(queries per seconds) compared to other LRU-based caches. Additionally, It approximately improves 30% for efficiency than a simple LRU cache.
Increasing efficiency means not only reducing cache misses, but also reducing the demand for heavy operations such as backend database access, which lowers the mean latency.
While LRU promotes accessed objects to the head of the queue, requiring a potentially slow lock acquisition, SIEVE only needs to update a single bit upon a cache hit. This update can be done with a significantly faster reader lock, leading to increased performance.
The real-world traces are also evaluated at here
Performance : golang-fifo
goos: linux
goarch: amd64
pkg: github.com/scalalang2/golang-fifo
cpu: Intel(R) Core(TM) i5-10600KF CPU @ 4.10GHz
BenchmarkCache
BenchmarkCache/cache=sieve
BenchmarkCache/cache=sieve/t=int32
BenchmarkCache/cache=sieve/t=int32-12 2765682 393.8 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 3037669 388.1 ns/op 149 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 3075998 395.0 ns/op 149 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2924646 392.0 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2632326 409.3 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2746551 463.5 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 3004071 401.0 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2398981 456.0 ns/op 149 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2698939 422.9 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int32-12 2647030 392.1 ns/op 148 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64
BenchmarkCache/cache=sieve/t=int64-12 2532614 414.1 ns/op 158 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2825973 419.3 ns/op 158 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2693790 407.1 ns/op 158 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2882792 414.7 ns/op 157 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2903197 421.7 ns/op 157 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2876046 435.7 ns/op 157 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2846494 410.4 ns/op 157 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2455807 440.1 ns/op 158 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2774462 435.1 ns/op 158 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=int64-12 2833150 433.9 ns/op 157 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string
BenchmarkCache/cache=sieve/t=string-12 2117859 546.9 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2079752 527.1 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2210930 530.8 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2122942 514.4 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2222488 553.6 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2260266 558.6 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2239196 567.1 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 2064308 576.8 ns/op 186 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 1882754 569.9 ns/op 185 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=string-12 1917342 574.6 ns/op 185 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite
BenchmarkCache/cache=sieve/t=composite-12 1825063 707.0 ns/op 223 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1745775 660.1 ns/op 224 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1680552 678.1 ns/op 225 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1774438 690.1 ns/op 224 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1530580 731.1 ns/op 226 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1663950 761.7 ns/op 225 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1607760 678.4 ns/op 225 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1703283 784.4 ns/op 225 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1295089 864.6 ns/op 229 B/op 4 allocs/op
BenchmarkCache/cache=sieve/t=composite-12 1552182 769.9 ns/op 226 B/op 4 allocs/opWhy LRU Cache is not good enough?
- LRU is often implemented with a doubly linked list and a hash table, requiring two pointers per cache entry, which becomes large overhead when the object is small.
- It promotes objects to the head of the queue upon cache hit, which performs at least six random memory accesses protected by lock, which limits the scalability.
Things to consider before adoption
- Both S3-FIFO and SIEVE have a O(n) time complexity for cache eviction, which only occurs when all objects are hit the cache, which means that there is a perfect (100%) hit rate in the cache.
- SIEVE is not designed to be scan-resistant. Therefore, it's currently recommended for web cache workloads, which typically follow a power-law distribution.
- S3-FIFO filters out one-hit-wonders early, It bears some resemblance to designing scan-resistant cache eviction algorithms.
- SIEVE scales well for read-intensive applications such as blogs and online shops, because it doesn't require to hold a writer lock on cache hit.
- The
golang-fifolibrary aims to provide a straightforward and efficient cache implementation, similar to hashicorp-lru and groupcache. Its goal is not to outperform highly specialized in-memory cache libraries (e.g. bigcache, freecache and etc).
Brief overview of SIEVE & S3-FIFO
Various workloads typically follows Power law distribution (e.g. Zipf's law) as shown in the following figure.
The analysis reveals that most requests are "one-hit-wonders", which means it's accessed only once. Consequently, a cache eviction strategy should quickly remove most objects after insertion.
S3-FIFO and SIEVE achieves this goal with simplicity, efficiency, and scalability using simple FIFO queue only.
How to run unit test
$ go test -v ./...How to run benchmark test
$ ./bench.sh