DataLoader 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.


Batching is not an advanced feature, it’s DataLoader’s primary feature. Create loaders by providing a batch loading function.

from promise import Promise
from promise.dataloader import DataLoader

class UserLoader(DataLoader):
    def batch_load_fn(self, keys):
        # Here we return a promise that will result on the
        # corresponding user for each key in keys
        return Promise.resolve([get_user(id=key) for key in keys])

A batch loading function accepts a list 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 (executed once the wrapping promise is resolved) and then call your batch function with all requested keys.

user_loader = UserLoader()

user_loader.load(1).then(lambda user: user_loader.load(user.best_friend_id))

user_loader.load(2).then(lambda user: user_loader.load(user.best_friend_id))

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.

Note that loaded values are one-to-one with the keys and must have the same order. This means that if you load all values from a single query, you must make sure that you then order the query result for the results to match the keys:

class UserLoader(DataLoader):
    def batch_load_fn(self, keys):
        users = { user for user in User.objects.filter(id__in=keys)}
        return Promise.resolve([users.get(user_id) for user_id in keys])

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.

Using with Graphene

DataLoader pairs nicely well with Graphene/GraphQL. 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 {
    bestFriend {
    friends(first: 5) {
      bestFriend {

Naively, if me, bestFriend and friends each need to request the backend, there could be at most 13 database requests!

When using DataLoader, we could define the User type using our previous example with leaner code and at most 4 database requests, and possibly fewer if there are cache hits.

class User(graphene.ObjectType):
    name = graphene.String()
    best_friend = graphene.Field(lambda: User)
    friends = graphene.List(lambda: User)

    def resolve_best_friend(root, info):
        return user_loader.load(root.best_friend_id)

    def resolve_friends(root, info):
        return user_loader.load_many(root.friend_ids)