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
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, reason) => {
freeFromMemoryOrWhatever(value)
},
// for use when you need to know that an item is being inserted
// note that this does NOT allow you to prevent the insertion,
// it just allows you to know about it.
onInsert: (value, key, reason) => {
logInsertionOrWhatever(key, 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 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
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, 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.
Testing
When writing tests that involve TTL-related functionality, note
that this module creates an internal reference to the global
performance or Date objects at import time. If you import it
statically at the top level, those references cannot be mocked or
overridden in your test environment.
To avoid this, dynamically import the package within your tests
so that the references are captured after your mocks are applied.
For example:
```ts
// ❌ Not recommended
import { LRUCache } from 'lru-cache'
// mocking timers, e.g. jest.useFakeTimers()
// ✅ Recommended for TTL tests
// mocking timers, e.g. jest.useFakeTimers()
const { LRUCache } = await import('lru-cache')
```
This ensures that your mocked timers or time sources are
respected when testing TTL behavior.
Additionally, you can pass in a perf option when creating your
LRUCache instance. This option accepts any object with a now
method that returns a number.
For example, this would be a very bare-bones time-mocking system
you could use in your tests, without any particular test
framework:
```ts
import { LRUCache } from 'lru-cache'
let myClockTime = 0
const cache = new LRUCache<string>({
max: 10,
ttl: 1000,
perf: {
now: () => myClockTime,
},
})
// run tests, updating myClockTime as needed
```
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
fetchContextoption was renamed tocontext, 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
nullorundefined. - 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 nowPromise<V | undefined>
instead of Promise<V | void>. This is an irrelevant change
practically speaking, but can require changes for TypeScript
users.
For more info, see the change log.