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https://github.com/isaacs/node-lru-cache

A fast cache that automatically deletes the least recently used items
https://github.com/isaacs/node-lru-cache

cache caching lru lru-cache lrucache

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A fast cache that automatically deletes the least recently used items

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# lru-cache



A cache object that deletes the least-recently-used items.



Specify a max number of the most recently used items that you

want to keep, and this cache will keep that many of the most

recently accessed items.



This is not primarily a TTL cache, and does not make strong TTL

guarantees. There is no preemptive pruning of expired items by

default, but you _may_ set a TTL on the cache or on a single

`set`. If you do so, it will treat expired items as missing, and

delete them when fetched. If you are more interested in TTL

caching than LRU caching, check out

[@isaacs/ttlcache](http://npm.im/@isaacs/ttlcache).



As of version 7, this is one of the most performant LRU

implementations available in JavaScript, and supports a wide

diversity of use cases. However, note that using some of the

features will necessarily impact performance, by causing the

cache to have to do more work. See the "Performance" section

below.



## Installation



```bash

npm install lru-cache --save

```



## Usage



```js

// 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

```



If you put more stuff in the cache, then less recently used items

will fall out. That's what an LRU cache is.



For full description of the API and all options, please see [the

LRUCache typedocs](https://isaacs.github.io/node-lru-cache/)



## Storage Bounds Safety



This implementation aims to be as flexible as possible, within

the limits of safe memory consumption and optimal performance.



At initial object creation, storage is allocated for `max` items.

If `max` is set to zero, then some performance is lost, and item

count is unbounded. Either `maxSize` or `ttl` _must_ be set if

`max` is not specified.



If `maxSize` is set, then this creates a safe limit on the

maximum storage consumed, but without the performance benefits of

pre-allocation. When `maxSize` is set, every item _must_ provide

a size, either via the `sizeCalculation` method provided to the

constructor, or via a `size` or `sizeCalculation` option provided

to `cache.set()`. The size of every item _must_ be a positive

integer.



If neither `max` nor `maxSize` are set, then `ttl` tracking must

be enabled. Note that, even when tracking item `ttl`, items are

_not_ preemptively deleted when they become stale, unless

`ttlAutopurge` is enabled. Instead, they are only purged the

next time the key is requested. Thus, if `ttlAutopurge`, `max`,

and `maxSize` are all not set, then the cache will potentially

grow unbounded.



In this case, a warning is printed to standard error. Future

versions may require the use of `ttlAutopurge` if `max` and

`maxSize` are not specified.



If you truly wish to use a cache that is bound _only_ by TTL

expiration, consider using a `Map` object, and calling

`setTimeout` to delete entries when they expire. It will perform

much better than an LRU cache.



Here is an implementation you may use, under the same

[license](./LICENSE) as this package:



```js

// a storage-unbounded ttl cache that is not an lru-cache

const cache = {

  data: new Map(),

  timers: new Map(),

  set: (k, v, ttl) => {

    if (cache.timers.has(k)) {

      clearTimeout(cache.timers.get(k))

    }

    cache.timers.set(

      k,

      setTimeout(() => cache.delete(k), ttl)

    )

    cache.data.set(k, v)

  },

  get: k => cache.data.get(k),

  has: k => cache.data.has(k),

  delete: k => {

    if (cache.timers.has(k)) {

      clearTimeout(cache.timers.get(k))

    }

    cache.timers.delete(k)

    return cache.data.delete(k)

  },

  clear: () => {

    cache.data.clear()

    for (const v of cache.timers.values()) {

      clearTimeout(v)

    }

    cache.timers.clear()

  },

}

```



If that isn't to your liking, check out

[@isaacs/ttlcache](http://npm.im/@isaacs/ttlcache).



## Storing Undefined Values



This cache never stores undefined values, as `undefined` is used

internally in a few places to indicate that a key is not in the

cache.



You may call `cache.set(key, undefined)`, but this is just

an alias for `cache.delete(key)`. Note that this has the effect

that `cache.has(key)` will return _false_ after setting it to

undefined.



```js

cache.set(myKey, undefined)

cache.has(myKey) // false!

```



If you need to track `undefined` values, and still note that the

key is in the cache, an easy workaround is to use a sigil object

of your own.



```js

import { LRUCache } from 'lru-cache'

const undefinedValue = Symbol('undefined')

const cache = new LRUCache(...)

const mySet = (key, value) =>

  cache.set(key, value === undefined ? undefinedValue : value)

const myGet = (key, value) => {

  const v = cache.get(key)

  return v === undefinedValue ? undefined : v

}

```



## Performance



As of January 2022, version 7 of this library is one of the most

performant LRU cache implementations in JavaScript.



Benchmarks can be extremely difficult to get right. In

particular, the performance of set/get/delete operations on

objects will vary _wildly_ depending on the type of key used. V8

is highly optimized for objects with keys that are short strings,

especially integer numeric strings. Thus any benchmark which

tests _solely_ using numbers as keys will tend to find that an

object-based approach performs the best.



Note that coercing _anything_ to strings to use as object keys is

unsafe, unless you can be 100% certain that no other type of

value will be used. For example:



```js

const myCache = {}

const set = (k, v) => (myCache[k] = v)

const get = k => myCache[k]



set({}, 'please hang onto this for me')

set('[object Object]', 'oopsie')

```



Also beware of "Just So" stories regarding performance. Garbage

collection of large (especially: deep) object graphs can be

incredibly costly, with several "tipping points" where it

increases exponentially. As a result, putting that off until

later can make it much worse, and less predictable. If a library

performs well, but only in a scenario where the object graph is

kept shallow, then that won't help you if you are using large

objects as keys.



In general, when attempting to use a library to improve

performance (such as a cache like this one), it's best to choose

an option that will perform well in the sorts of scenarios where

you'll actually use it.



This library is optimized for repeated gets and minimizing

eviction time, since that is the expected need of a LRU. Set

operations are somewhat slower on average than a few other

options, in part because of that optimization. It is assumed

that you'll be caching some costly operation, ideally as rarely

as possible, so optimizing set over get would be unwise.



If performance matters to you:



1. If it's at all possible to use small integer values as keys,

   and you can guarantee that no other types of values will be

   used as keys, then do that, and use a cache such as

   [lru-fast](https://npmjs.com/package/lru-fast), or

   [mnemonist's

   LRUCache](https://yomguithereal.github.io/mnemonist/lru-cache)

   which uses an Object as its data store.



2. Failing that, if at all possible, use short non-numeric

   strings (ie, less than 256 characters) as your keys, and use

   [mnemonist's

   LRUCache](https://yomguithereal.github.io/mnemonist/lru-cache).



3. If the types of your keys will be anything else, especially

   long strings, strings that look like floats, objects, or some

   mix of types, or if you aren't sure, then this library will

   work well for you.



   If you do not need the features that this library provides

   (like asynchronous fetching, a variety of TTL staleness

   options, and so on), then [mnemonist's

   LRUMap](https://yomguithereal.github.io/mnemonist/lru-map) is

   a very good option, and just slightly faster than this module

   (since it does considerably less).



4. Do not use a `dispose` function, size tracking, or especially

   ttl behavior, unless absolutely needed. These features are

   convenient, and necessary in some use cases, and every attempt

   has been made to make the performance impact minimal, but it

   isn't nothing.



## Breaking Changes in Version 7



This library changed to a different algorithm and internal data

structure in version 7, yielding significantly better

performance, albeit with some subtle changes as a result.



If you were relying on the internals of LRUCache in version 6 or

before, it probably will not work in version 7 and above.



## Breaking Changes in Version 8



- The `fetchContext` option was renamed to `context`, and may no

  longer be set on the cache instance itself.

- Rewritten in TypeScript, so pretty much all the types moved

  around a lot.

- The AbortController/AbortSignal polyfill was removed. For this

  reason, **Node version 16.14.0 or higher is now required**.

- Internal properties were moved to actual private class

  properties.

- Keys and values must not be `null` or `undefined`.

- Minified export available at `'lru-cache/min'`, for both CJS

  and MJS builds.



## Breaking Changes in Version 9



- Named export only, no default export.

- AbortController polyfill returned, albeit with a warning when

  used.



## Breaking Changes in Version 10



- `cache.fetch()` return type is now `Promise`

  instead of `Promise`. This is an irrelevant change

  practically speaking, but can require changes for TypeScript

  users.



For more info, see the [change log](CHANGELOG.md).