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 given. If you are more interested in TTL caching than LRU caching, check out @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

npm install lru-cache --save

Usage

const LRU = require('lru-cache')

// At least one of 'max' or 'ttl' 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.
const options = {
  // the number of most recently used items to keep.
  max: 500, // <-- Technically optional, but see "Storage Bounds Safety" below

  // max time to live for items before they are considered stale
  // note that stale items are NOT preemptively removed by default,
  // and MAY live in the cache, contributing to its LRU max, long after
  // they have expired.
  // Also, as this cache is optimized for LRU/MRU operations, some of
  // the staleness/TTL checks will reduce performance, as they will incur
  // overhead by deleting items.
  // Must be a positive integer in ms, defaults to 0, which means "no TTL"
  ttl: 1000 * 60 * 5,

  // return stale items from cache.get() before disposing of them
  // boolean, default false
  allowStale: false,

  // update the age of items on cache.get(), renewing their TTL
  // boolean, default false
  updateAgeOnGet: false,
}

const cache = new LRU(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 it, then items will fall out.

Options

max - The maximum number (or size) of items that remain in the cache (assuming no TTL pruning or explicit deletions). Note that fewer items may be stored if size calculation is used. This must be a positive finite intger.

At least one of max or ttl is required. This must be a positive integer if set.

It is strongly recommended to set a max to prevent unbounded growth of the cache. See "Storage Bounds Safety" below.
ttl - max time to live for items before they are considered stale. Note that stale items are NOT preemptively removed by default, and MAY live in the cache, contributing to its LRU max, long after they have expired.

Also, as this cache is optimized for LRU/MRU operations, some of the staleness/TTL checks will reduce performance, as they will incur overhead by deleting from Map objects rather than simply throwing old Map objects away.

This is not primarily a TTL cache, and does not make strong TTL guarantees. There is no pre-emptive pruning of expired items, but you may set a TTL on the cache, and it will treat expired items as missing when they are given, and delete them.

Optional, but must be a positive integer in ms if specified.

This may be overridden by passing an options object to cache.set().

At least one of max or ttl is required. This must be a positive integer if set.

Even if ttl tracking is enabled, it is strongly recommended to set a max to prevent unbounded growth of the cache. See "Storage Bounds Safety" below.

If ttl tracking is enabled, and max are not set, then a warning will be emitted cautioning about the potential for unbounded memory consumption.

Deprecated alias: maxAge
ttlResolution - Minimum amount of time in ms in which to check for staleness. Defaults to 1, which means that the current time is checked at most once per millisecond.

Set to 0 to check the current time every time staleness is tested.

Note that setting this to a higher value will improve performance somewhat while using ttl tracking, albeit at the expense of keeping stale items around a bit longer than intended.
allowStale - By default, if you set ttl, it'll only delete stale items from the cache when you get(key). That is, it's not preemptively pruning items.

If you set allowStale:true, it'll return the stale value as well as deleting it. If you don't set this, then it'll return undefined when you try to get a stale entry.

Note that when a stale entry is given, even if it is returned due to allowStale being set, it is removed from the cache immediately. You can immediately put it back in the cache if you wish, thus resetting the TTL.

This may be overridden by passing an options object to cache.get(). The cache.has() method will always return false for stale items.

Boolean, default false, only relevant if ttl is set.

Deprecated alias: stale
updateAgeOnGet - When using time-expiring entries with ttl, setting this to true will make each item's age reset to 0 whenever it is retrieved from cache with get(), causing it to not expire. (It can still fall out of cache based on recency of use, of course.)

This may be overridden by passing an options object to cache.get().

Boolean, default false, only relevant if ttl is set.

API

new LRUCache(options)

Create a new LRUCache. All options are documented above, and are on the cache as public members.
cache.max, cache.allowStale, cache.ttl, cache.updateAgeOnGet

All option names are exposed as public members on the cache object.

These are intended for read access only. Changing them during program operation can cause undefined behavior.
cache.size

The total number of items held in the cache at the current moment.
cache.calculatedSize

The total size of items in cache when using size tracking.
set(key, value, [{ size, ttl }])

Add a value to the cache.

Optional options object may contain ttl as described above, which default to the settings on the cache object.

Options object my also include size, which will just use the specified number if it is a positive integer.

Will update the recency of the entry.

Returns the cache object.
get(key, { updateAgeOnGet, allowStale } = {}) => value

Return a value from the cache.

Will update the recency of the cache entry found.

If the key is not found, get() will return undefined. This can be confusing when setting values specifically to undefined, as in cache.set(key, undefined). Use cache.has() to determine whether a key is present in the cache at all.
peek(key, { allowStale } = {}) => value

Like get() but doesn't update recency or delete stale items.

Returns undefined if the item is stale, unless allowStale is set either on the cache or in the options object.
has(key) => Boolean

Check if a key is in the cache, without updating the recency of use. Age is not updated.

Will return false if the item is stale, even though it is technically in the cache.
delete(key)

Deletes a key out of the cache.

Returns true if the key was deleted, false otherwise.
clear()

Clear the cache entirely, throwing away all values.
pop()

Evict the least recently used item, returning its value.

Returns undefined if cache is empty.

Internal Methods and Properties

In order to optimize performance as much as possible, "private" members and methods are exposed on the object as normal properties, rather than being accessed via Symbols, private members, or closure variables.

Do not use or rely on these. They will change or be removed without notice. They will cause undefined behavior if used inappropriately. There is no need or reason to ever call them directly.

This documentation is here so that it is especially clear that this not "undocumented" because someone forgot; it is documented, and the documentation is telling you not to do it.

Do not report bugs that stem from using these properties. They will be ignored.

initializeTTLTracking() Set up the cache for tracking TTLs
updateItemAge(index) Called when an item age is updated, by internal ID
setItemTTL(index) Called when an item ttl is updated, by internal ID
isStale(index) Called to check an item's staleness, by internal ID
newIndex() Create a new internal ID, either reusing a deleted ID, evicting the least recently used ID, or walking to the end of the allotted space.
evict() Evict the least recently used internal ID, returning its ID. Does not do any bounds checking.
connect(p, n) Connect the p and n internal IDs in the linked list.
moveToTail(index) Move the specified internal ID to the most recently used position.
keyMap Map of keys to internal IDs
keyList List of keys by internal ID
valList List of values by internal ID
ttls List of TTL values by internal ID
starts List of start time values by internal ID
next Array of "next" pointers by internal ID
prev Array of "previous" pointers by internal ID
head Internal ID of least recently used item
tail Internal ID of most recently used item
free Stack of deleted internal IDs

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. ttl must be set if max is not specified.

If max not set, then ttl tracking must be enabled. Note that, even when tracking item ttl, items are not preemptively deleted when they become stale. Instead, they are only purged the next time the key is requested. Thus, if max is not set, then the cache will potentially grow unbounded.

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

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:

// 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.del(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()
  }
}

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:

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:

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 which uses an Object as its data store.
Failing that, if at all possible, use short non-numeric strings (ie, less than 256 characters) as your keys, and use mnemonist's LRUCache.
If the types of your keys will be long strings, strings that look like floats, null, objects, or some mix of types, or if you aren't sure, then this library will work well for you.

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.

For more info, see the change log.

tramvaijs/lru-cache-nano