LRU 缓存

LRU

LRU 是一种缓存算法，提供了一种简单而又高效的方式来存储数据。同时自动处理缓存的驱逐策略，即当缓存达到预设的大小限制时，它会自动移除最近最少使用的项。

1. 新数据插入到链表头部
2. 每当缓存命中（即缓存数据被访问），则将数据移到链表头部；
3. 当链表满的时候，将链表尾部的数据丢弃。

Lru-cache

lru-cache 是一个 Nodejs 的 LUR 库

// hybrid module, either works
import { LRUCache } from 'lru-cache'
// or:
const { LRUCache } = require('lru-cache')
// or in minified form for web browsers:
import { LRUCache } from 'http://unpkg.com/lru-cache@9/dist/mjs/index.min.mjs'
 
// At least one of 'max', 'ttl', or 'maxSize' is required, to prevent
// unsafe unbounded storage.
//
// In most cases, it's best to specify a max for performance, so all
// the required memory allocation is done up-front.
//
// All the other options are optional, see the sections below for
// documentation on what each one does.  Most of them can be
// overridden for specific items in get()/set()
const options = {
  max: 500,
 
  // for use with tracking overall storage size
  maxSize: 5000,
  sizeCalculation: (value, key) => {
    return 1
  },
 
  // for use when you need to clean up something when objects
  // are evicted from the cache
  dispose: (value, key) => {
    freeFromMemoryOrWhatever(value)
  },
 
  // how long to live in ms
  ttl: 1000 * 60 * 5,
 
  // return stale items before removing from cache?
  allowStale: false,
 
  updateAgeOnGet: false,
  updateAgeOnHas: false,
 
  // async method to use for cache.fetch(), for
  // stale-while-revalidate type of behavior
  fetchMethod: async (
    key,
    staleValue,
    { options, signal, context }
  ) => {},
}
 
const cache = new LRUCache(options)
 
cache.set('key', 'value')
cache.get('key') // "value"
 
// non-string keys ARE fully supported
// but note that it must be THE SAME object, not
// just a JSON-equivalent object.
var someObject = { a: 1 }
cache.set(someObject, 'a value')
// Object keys are not toString()-ed
cache.set('[object Object]', 'a different value')
assert.equal(cache.get(someObject), 'a value')
// A similar object with same keys/values won't work,
// because it's a different object identity
assert.equal(cache.get({ a: 1 }), undefined)
 
cache.clear() // empty the cache

缺点

LRU 缓存是基于内存的缓存方案，所以在分布式项目中没办法使用，无法做不同机器下的同步

参考

• LruCache在美团DSP系统中的应用演进 - 知乎