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Ristretto

Ristretto is a fast, concurrent cache library built with a focus on performance and correctness.

The motivation to build Ristretto comes from the need for a contention-free cache in Dgraph.

Features

• High Hit Ratios - with our unique admission/eviction policy pairing, Ristretto's performance is best in class.
  • Eviction: SampledLFU - on par with exact LRU and better performance on Search and Database traces.
  • Admission: TinyLFU - extra performance with little memory overhead (12 bits per counter).
• Fast Throughput - we use a variety of techniques for managing contention and the result is excellent throughput.
• Cost-Based Eviction - any large new item deemed valuable can evict multiple smaller items (cost could be anything).
• Fully Concurrent - you can use as many goroutines as you want with little throughput degradation.
• Metrics - optional performance metrics for throughput, hit ratios, and other stats.
• Simple API - just figure out your ideal Config values and you're off and running.

Status

Ristretto is production-ready. See Projects using Ristretto.

Usage

package main

import (
	"fmt"

	"github.com/dgraph-io/ristretto/v2"
)

func main() {
	cache, err := ristretto.NewCache(&ristretto.Config[string, string]{
		NumCounters: 1e7,     // number of keys to track frequency of (10M).
		MaxCost:     1 << 30, // maximum cost of cache (1GB).
		BufferItems: 64,      // number of keys per Get buffer.
	})
	if err != nil {
		panic(err)
	}
	defer cache.Close()

	// set a value with a cost of 1
	cache.Set("key", "value", 1)

	// wait for value to pass through buffers
	cache.Wait()

	// get value from cache
	value, found := cache.Get("key")
	if !found {
		panic("missing value")
	}
	fmt.Println(value)

	// del value from cache
	cache.Del("key")
}

Benchmarks

The benchmarks can be found in https://github.com/dgraph-io/benchmarks/tree/master/cachebench/ristretto.

Hit Ratios for Search

This trace is described as "disk read accesses initiated by a large commercial search engine in response to various web search requests."

Hit Ratio for Database

This trace is described as "a database server running at a commercial site running an ERP application on top of a commercial database."

Hit Ratio for Looping

This trace demonstrates a looping access pattern.

Hit Ratio for CODASYL

This trace is described as "references to a CODASYL database for a one hour period."

Throughput for Mixed Workload

Throughput ffor Read Workload

Through for Write Workload

Projects Using Ristretto

Below is a list of known projects that use Ristretto:

• Badger - Embeddable key-value DB in Go
• Dgraph - Horizontally scalable and distributed GraphQL database with a graph backend

FAQ

How are you achieving this performance? What shortcuts are you taking?

We go into detail in the Ristretto blog post, but in short: our throughput performance can be attributed to a mix of batching and eventual consistency. Our hit ratio performance is mostly due to an excellent admission policy and SampledLFU eviction policy.

As for "shortcuts," the only thing Ristretto does that could be construed as one is dropping some Set calls. That means a Set call for a new item (updates are guaranteed) isn't guaranteed to make it into the cache. The new item could be dropped at two points: when passing through the Set buffer or when passing through the admission policy. However, this doesn't affect hit ratios much at all as we expect the most popular items to be Set multiple times and eventually make it in the cache.

Is Ristretto distributed?

No, it's just like any other Go library that you can import into your project and use in a single process.