Package: aiodataloader
Asyncio DataLoaderDataLoader is a generic utility to be used as part of your application's data
fetching layer to provide a simplified and consistent API over various remote
data sources such as databases or web services via batching and caching.A port of the "Loader" API originally developed by@schrocknat Facebook in
2010 as a simplifying force to coalesce the sundry key-value store back-end
APIs which existed at the time. At Facebook, "Loader" became one of the
implementation details of the "Ent" framework, a privacy-aware data entity
loading and caching layer within web server product code. This ultimately became
the underpinning for Facebook's GraphQL server implementation and type
definitions.Asyncio DataLoader is a Python port of the original JavaScriptDataLoaderimplementation. DataLoader is often used when implementing aGraphQLservice,
though it is also broadly useful in other situations.Getting StartedFirst, install DataLoader using pip.pipinstallaiodataloaderTo get started, create aDataLoader. EachDataLoaderinstance represents a
unique cache. Typically instances are created per request when used within a
web-server likeSanicif different users can see different things.Note: DataLoader assumes a AsyncIO environment withasync/awaitavailable only in Python 3.5+.BatchingBatching is not an advanced feature, it's DataLoader's primary feature.
Create loaders by providing a batch loading function.fromaiodataloaderimportDataLoaderclassUserLoader(DataLoader):asyncdefbatch_load_fn(self,keys):returnawaitmy_batch_get_users(keys)user_loader=UserLoader()A batch loading function accepts an Iterable of keys, and returns a Promise which
resolves to a List of values*.Then load individual values from the loader. DataLoader will coalesce all
individual loads which occur within a single frame of execution (a single tick
of the event loop) and then call your batch function with all requested keys.user1_future=user_loader.load(1)user2_future=user_loader.load(2)user1=awaituser1_futureuser2=awaituser2_futureuser1_invitedby=user_loader.load(user1.invited_by_id)user2_invitedby=user_loader.load(user2.invited_by_id)print("User 1 was invited by",awaituser1_invitedby)print("User 2 was invited by",awaituser2_invitedby)A naive application may have issued four round-trips to a backend for the
required information, but with DataLoader this application will make at most
two.DataLoader allows you to decouple unrelated parts of your application without
sacrificing the performance of batch data-loading. While the loader presents an
API that loads individual values, all concurrent requests will be coalesced and
presented to your batch loading function. This allows your application to safely
distribute data fetching requirements throughout your application and maintain
minimal outgoing data requests.Batch FunctionA batch loading function accepts an List of keys, and returns a Future which
resolves to a List of values. There are a few constraints that must be upheld:The List of values must be the same length as the List of keys.Each index in the List of values must correspond to the same index in the List of keys.For example, if your batch function was provided the List of keys:[ 2, 9, 6, 1 ],
and loading from a back-end service returned the values:{'id':9,'name':'Chicago'}{'id':1,'name':'New York'}{'id':2,'name':'San Francisco'}Our back-end service returned results in a different order than we requested, likely
because it was more efficient for it to do so. Also, it omitted a result for key6,
which we can interpret as no value existing for that key.To uphold the constraints of the batch function, it must return an List of values
the same length as the List of keys, and re-order them to ensure each index aligns
with the original keys[ 2, 9, 6, 1 ]:[{'id':2,'name':'San Francisco'},{'id':9,'name':'Chicago'},None,{'id':1,'name':'New York'}]CachingDataLoader provides a memoization cache for all loads which occur in a single
request to your application. After.load()is called once with a given key,
the resulting value is cached to eliminate redundant loads.In addition to relieving pressure on your data storage, caching results per-request
also creates fewer objects which may relieve memory pressure on your application:user_future1=user_loader.load(1)user_future2=user_loader.load(1)assertuser_future1==user_future2Caching per-RequestDataLoader cachingdoes notreplace Redis, Memcache, or any other shared
application-level cache. DataLoader is first and foremost a data loading mechanism,
and its cache only serves the purpose of not repeatedly loading the same data in
the context of a single request to your Application. To do this, it maintains a
simple in-memory memoization cache (more accurately:.load()is a memoized function).Avoid multiple requests from different users using the DataLoader instance, which
could result in cached data incorrectly appearing in each request. Typically,
DataLoader instances are created when a Request begins, and are not used once the
Request ends.For example, when using withSanic:defcreate_loaders(auth_token){return{'users':user_loader,}}app=Sanic(__name__)@app.route("/")asyncdeftest(request):auth_token=authenticate_user(request)loaders=create_loaders(auth_token)returnrender_page(request,loaders)Clearing CacheIn certain uncommon cases, clearing the request cache may be necessary.The most common example when clearing the loader's cache is necessary is after
a mutation or update within the same request, when a cached value could be out of
date and future loads should not use any possibly cached value.Here's a simple example using SQL UPDATE to illustrate.# Request begins...user_loader=...# And a value happens to be loaded (and cached).user4=awaituser_loader.load(4)# A mutation occurs, invalidating what might be in cache.awaitsql_run('UPDATE users WHERE id=4 SET username="zuck"')user_loader.clear(4)# Later the value load is loaded again so the mutated data appears.user4=awaituser_loader.load(4)# Request completes.Caching ExceptionsIf a batch load fails (that is, a batch function throws or returns a rejected
Promise), then the requested values will not be cached. However if a batch
function returns anExceptioninstance for an individual value, thatExceptionwill
be cached to avoid frequently loading the sameException.In some circumstances you may wish to clear the cache for these individual Errors:try:user_loader.load(1)exceptExceptionase:user_loader.clear(1)raiseDisabling CacheIn certain uncommon cases, a DataLoader whichdoes notcache may be desirable.
CallingDataLoader(batch_fn, cache=false)will ensure that every
call to.load()will produce anewFuture, and requested keys will not be
saved in memory.However, when the memoization cache is disabled, your batch function will
receive an array of keys which may contain duplicates! Each key will be
associated with each call to.load(). Your batch loader should provide a value
for each instance of the requested key.For example:classMyLoader(DataLoader):cache=Falseasyncdefbatch_load_fn(self,keys):print(keys)returnkeysmy_loader=MyLoader()my_loader.load('A')my_loader.load('B')my_loader.load('A')# > [ 'A', 'B', 'A' ]More complex cache behavior can be achieved by calling.clear()or.clear_all()rather than disabling the cache completely. For example, this DataLoader will
provide unique keys to a batch function due to the memoization cache being
enabled, but will immediately clear its cache when the batch function is called
so later requests will load new values.classMyLoader(DataLoader):cache=Falseasyncdefbatch_load_fn(self,keys):self.clear_all()returnkeysAPIclass DataLoaderDataLoader creates a public API for loading data from a particular
data back-end with unique keys such as theidcolumn of a SQL table or
document name in a MongoDB database, given a batch loading function.EachDataLoaderinstance contains a unique memoized cache. Use caution when
used in long-lived applications or those which serve many users with different
access permissions and consider creating a new instance per web request.DataLoader(batch_load_fn, **options)Create a newDataLoadergiven a batch loading function and options.batch_load_fn: An async function (coroutine) which accepts an List of keys
and returns a Future which resolves to an List of values.options:batch: DefaultTrue. Set toFalseto disable batching, instead
immediately invokingbatch_load_fnwith a single load key.max_batch_size: DefaultInfinity. Limits the number of items that get
passed in to thebatch_load_fn.cache: DefaultTrue. Set toFalseto disable memoization caching,
instead creating a new Promise and new key in thebatch_load_fnfor every
load of the same key.cache_key_fn: A function to produce a cache key for a given load key.
Defaults tokey => key. Useful to provide when Python objects are keys
and two similarly shaped objects should be considered equivalent.cache_map: An instance ofdict(or an object with a similar API) to be
used as the underlying cache for this loader. Default{}.load(key)Loads a key, returning aFuturefor the value represented by that key.key: An key value to load.load_many(keys)Loads multiple keys, promising an array of values:a,b=awaitmy_loader.load_many(['a','b']);This is equivalent to the more verbose:fromasyncioimportgathera,b=awaitgather(my_loader.load('a'),my_loader.load('b'))keys: A list of key values to load.clear(key)Clears the value atkeyfrom the cache, if it exists. Returns itself for
method chaining.key: An key value to clear.clear_all()Clears the entire cache. To be used when some event results in unknown
invalidations across this particularDataLoader. Returns itself for
method chaining.prime(key, value)Primes the cache with the provided key and value. If the key already exists, no
change is made. (To forcefully prime the cache, clear the key first withloader.clear(key).prime(key, value).) Returns itself for method chaining.Using with GraphQLDataLoader pairs nicely well withGraphQL. GraphQL fields are
designed to be stand-alone functions. Without a caching or batching mechanism,
it's easy for a naive GraphQL server to issue new database requests each time a
field is resolved.Consider the following GraphQL request:{
  me {
    name
    bestFriend {
      name
    }
    friends(first: 5) {
      name
      bestFriend {
        name
      }
    }
  }
}Naively, ifme,bestFriendandfriendseach need to request the backend,
there could be at most 13 database requests!When using DataLoader withgraphene, we could define theUsertype with clearer code and
at most 4 database requests, and possibly fewer if there are cache hits.classUser(graphene.ObjectType):name=graphene.String()best_friend=graphene.Field(lambda:User)friends=graphene.List(lambda:User)defresolve_best_friend(self,args,context,info):returnuser_loader.load(self.best_friend_id)defresolve_friends(self,args,context,info):returnuser_loader.load_many(self.friend_ids)Common PatternsCreating a new DataLoader per request.In many applications, a web server using DataLoader serves requests to many
different users with different access permissions. It may be dangerous to use
one cache across many users, and is encouraged to create a new DataLoader
per request:defcreate_loaders(auth_token):return{'users':DataLoader(lambdaids:gen_users(auth_token,ids)),'cdn_urls':DataLoader(lambdaraw_urls:gen_cdn_urls(auth_token,raw_urls)),'stories':DataLoader(lambdakeys:gen_stories(auth_token,keys)),}}# When handling an incoming web request:loaders=create_loaders(request.query.auth_token)# Then, within application logic:user=awaitloaders.users.load(4)pic=awaitloaders.cdn_urls.load(user.raw_pic_url)Creating an object where each key is aDataLoaderis one common pattern which
provides a single value to pass around to code which needs to perform
data loading, such as part of theroot_valuein aGraphQLrequest.Loading by alternative keys.Occasionally, some kind of value can be accessed in multiple ways. For example,
perhaps a "User" type can be loaded not only by an "id" but also by a "username"
value. If the same user is loaded by both keys, then it may be useful to fill
both caches when a user is loaded from either source:asyncdefuser_by_id_batch_fn(ids):users=awaitgen_users_by_id(ids)foruserinusers:username_loader.prime(user.username,user)returnusersuser_by_id_loader=DataLoader(user_by_id_batch_fn)asyncdefusername_batch_fn(names):users=awaitgen_usernames(names)foruserinusers:user_by_id_loader.prime(user.id,user)returnusersusername_loader=DataLoader(username_batch_fn)Custom CachesDataLoader can optionaly be provided a custom dict instance to use as its
memoization cache. More specifically, any object that implements the methodsget(),set(),delete()andclear()can be provided. This allows for custom dicts
which implement variouscache algorithmsto be provided. By default,
DataLoader uses the standarddictwhich simply grows until the DataLoader
is released. The default is appropriate when requests to your application are
short-lived.Video Source Code WalkthroughDataLoader Source Code Walkthrough (YouTube):