async-cache-dedupe
async-cache-dedupe
is a cache for asynchronous fetching of resources
with full deduplication, i.e. the same resource is only asked once at any given time.
Install
npm i async-cache-dedupe
Example
import { createCache } from 'async-cache-dedupe'
const cache = createCache({
ttl: 5,
storage: { type: 'memory' },
})
cache.define('fetchSomething', async (k) => {
console.log('query', k)
return { k }
})
const p1 = cache.fetchSomething(42)
const p2 = cache.fetchSomething(24)
const p3 = cache.fetchSomething(42)
const res = await Promise.all([p1, p2, p3])
console.log(res)
Commonjs/require
is also supported.
API
createCache(opts)
Creates a new cache.
Options:
ttl
: the maximum time a cache entry can live, default 0
; if 0
, an element is removed from the cache as soon as the promise resolves.onDedupe
: a function that is called every time it is defined is deduped.onError
: a function that is called every time there is a cache error.onHit
: a function that is called every time there is a hit in the cache.onMiss
: a function that is called every time the result is not in the cache.storage
: the storage options; default is { type: "memory" }
Storage options are:
type
: memory
(default) or redis
options
: by storage type
-
for memory
type
size
: maximum number of items to store in the cache per resolver. Default is 1024
.invalidation
: enable invalidation, see invalidation. Default is disabled.log
: logger instance pino
compatible, default is disabled.
Example
createCache({ storage: { type: 'memory', options: { size: 2048 } } })
-
for redis
type
client
: a redis client instance, mandatory. Should be an ioredis
client or compatible.invalidation
: enable invalidation, see invalidation. Default is disabled.invalidation.referencesTTL
: references TTL in seconds, it means how long the references are alive; it should be set at the maximum of all the caches ttl.log
: logger instance pino
compatible, default is disabled.
Example
createCache({ storage: { type: 'redis', options: { client: new Redis(), invalidation: { referencesTTL: 60 } } } })
cache.define(name[, opts], original(arg, cacheKey))
Define a new function to cache of the given name
.
The define
method adds a cache[name]
function that will call the original
function if the result is not present
in the cache. The cache key for arg
is computed using safe-stable-stringify
and it is passed as the cacheKey
argument to the original function.
Options:
-
ttl
: a number or a function that returns a number of the maximum time a cache entry can live, default as defined in the cache; default is zero, so cache is disabled, the function will be only the deduped. The first argument of the function is the result of the original function.
-
serialize
: a function to convert the given argument into a serializable object (or string).
-
onDedupe
: a function that is called every time there is defined is deduped.
-
onError
: a function that is called every time there is a cache error.
-
onHit
: a function that is called every time there is a hit in the cache.
-
onMiss
: a function that is called every time the result is not in the cache.
-
storage
: the storage to use, same as above. It's possible to specify different storages for each defined function for fine-tuning.
-
references
: sync or async function to generate references, it receives (args, key, result)
from the defined function call and must return an array of strings or falsy; see invalidation to know how to use them.
Example 1
const cache = createCache({ ttl: 60 })
cache.define('fetchUser', {
references: (args, key, result) => result ? [`user~${result.id}`] : null
},
(id) => database.find({ table: 'users', where: { id }}))
await cache.fetchUser(1)
Example 2 - dynamically set ttl
based on result.
const cache = createCache()
cache.define('fetchAccessToken', {
ttl: (result) => result.expiresInSeconds
}, async () => {
const response = await fetch("https://example.com/token");
const result = await response.json();
return result;
})
await cache.fetchAccessToken()
cache.clear([name], [arg])
Clear the cache. If name
is specified, all the cache entries from the function defined with that name are cleared.
If arg
is specified, only the elements cached with the given name
and arg
are cleared.
cache.invalidateAll(references, [storage])
cache.invalidateAll
perform invalidation over the whole storage; if storage
is not specified - using the same name
as the defined function, invalidation is made over the default storage.
references
can be:
- a single reference
- an array of references (without wildcard)
- a matching reference with wildcard, same logic for
memory
and redis
Example
const cache = createCache({ ttl: 60 })
cache.define('fetchUser', {
references: (args, key, result) => result ? [`user:${result.id}`] : null
}, (id) => database.find({ table: 'users', where: { id }}))
cache.define('fetchCountries', {
storage: { type: 'memory', size: 256 },
references: (args, key, result) => [`countries`]
}, (id) => database.find({ table: 'countries' }))
cache.invalidateAll('user:*')
cache.invalidateAll('user:1')
cache.invalidateAll(['user:1', 'user:2'])
cache.invalidateAll('countries', 'fetchCountries')
See below how invalidation and references work.
Invalidation
Along with time to live
invalidation of the cache entries, we can use invalidation by keys.
The concept behind invalidation by keys is that entries have an auxiliary key set that explicitly links requests along with their own result. These auxiliary keys are called here references
.
A scenario. Let's say we have an entry user {id: 1, name: "Alice"}
, it may change often or rarely, the ttl
system is not accurate:
- it can be updated before
ttl
expiration, in this case the old value is shown until expiration by ttl
. - it's not been updated during
ttl
expiration, so in this case, we don't need to reload the value, because it's not changed
To solve this common problem, we can use references
.
We can say that the result of defined function getUser(id: 1)
has reference user~1
, and the result of defined function findUsers
, containing {id: 1, name: "Alice"},{id: 2, name: "Bob"}
has references [user~1,user~2]
.
So we can find the results in the cache by their references
, independently of the request that generated them, and we can invalidate by references
.
So, when a writing event involving user {id: 1}
happens (usually an update), we can remove all the entries in the cache that have references to user~1
, so the result of getUser(id: 1)
and findUsers
, and they will be reloaded at the next request with the new data - but not the result of getUser(id: 2)
.
Explicit invalidation is disabled
by default, you have to enable it in storage
settings.
See mercurius-cache-example for a complete example.
Redis
Using a redis
storage is the best choice for a shared and/or large cache.
All the references
entries in redis have referencesTTL
, so they are all cleaned at some time.
referencesTTL
value should be set at the maximum of all the ttl
s, to let them be available for every cache entry, but at the same time, they expire, avoiding data leaking.
Anyway, we should keep references
up-to-date to be more efficient on writes and invalidation, using the garbage collector
function, that prunes the expired references: while expired references do not compromise the cache integrity, they slow down the I/O operations.
Storage memory
doesn't have gc
.
Redis garbage collector
As said, While the garbage collector is optional, is highly recommended to keep references up to date and improve performances on setting cache entries and invalidation of them.
storage.gc([mode], [options])
mode
: lazy
(default) or strict
.
In lazy
mode, only a chunk of the references
are randomly checked, and probably freed; running lazy
jobs tend to eventually clear all the expired references
.
In strict
mode, all the references
are checked and freed, and after that, references
and entries are perfectly clean.
lazy
mode is the light heuristic way to ensure cached entries and references
are cleared without stressing too much redis
, strict
mode at the opposite stress more redis
to get a perfect result.
The best strategy is to combine them both, running often lazy
jobs along with some strict
ones, depending on the size of the cache.
Options:
chunk
: the chunk size of references analyzed per loops, default 64
lazy~chunk
: the chunk size of references analyzed per loops in lazy
mode, default 64
; if both chunk
and lazy.chunk
is set, the maximum one is takenlazy~cursor
: the cursor offset, default zero; cursor should be set at report.cursor
to continue scanning from the previous operation
Return report
of the gc
job, as follows
"report":{
"references":{
"scanned":["r:user:8", "r:group:11", "r:group:16"],
"removed":["r:user:8", "r:group:16"]
},
"keys":{
"scanned":["users~1"],
"removed":["users~1"]
},
"loops":4,
"cursor":0,
"error":null
}
Example
import { createCache, createStorage } from 'async-cache-dedupe'
const cache = createCache({
ttl: 5,
storage: { type: 'redis', options: { client: redisClient, invalidation: true } },
})
const storage = createStorage('redis', { client: redisClient, invalidation: true })
let cursor
setInterval(() => {
const report = await storage.gc('lazy', { lazy: { cursor } })
if(report.error) {
console.error('error on redis gc', error)
return
}
console.log('gc report (lazy)', report)
cursor = report.cursor
}, 60e3).unref()
setInterval(() => {
const report = await storage.gc('strict', { chunk: 128 })
if(report.error) {
console.error('error on redis gc', error)
return
}
console.log('gc report (strict)', report)
}, 10 * 60e3).unref()
Maintainers
Breaking Changes
- version
0.5.0
-> 0.6.0
options.cacheSize
is dropped in favor of storage
License
MIT