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Dataloader

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Dataloader is a generic utility to be used as part of your application's data fetching layer to provide a simplified and consistent API to perform batching and caching within a request. It is heavily inspired by Facebook's dataloader.

Getting started

First, install Dataloader using bundler:

gem "dataloader"

To get started, instantiate Dataloader. Each Dataloader instance represents a unique cache. Typically instances are created per request when used within a web-server. To see how to use with GraphQL server, see section below.

Dataloader is dependent on promise.rb (Promise class) which you can use freely for batch-ready code (e.g. loader can return Promise that returns a Promise that returns a Promise). Dataloader will try to batch most of them.

Basic usage

# It will be called only once with ids = [0, 1, 2]
loader = Dataloader.new do |ids|
  User.find(*ids)
end

# Schedule data to load
promise_one = loader.load(0)
promise_two = loader.load_many([1, 2])

# Get promises results
user0 = promise_one.sync
user1, user2 = promise_two.sync

Using with GraphQL

You can pass loaders passed inside context.

UserType = GraphQL::ObjectType.define do
  field :name, types.String
end

QueryType = GraphQL::ObjectType.define do
  name "Query"
  description "The query root of this schema"

  field :user do
    type UserType
    argument :id, !types.ID
    resolve ->(obj, args, ctx) {
      ctx[:user_loader].load(args["id"])
    }
  end
end

Schema = GraphQL::Schema.define do
  lazy_resolve(Promise, :sync)

  query QueryType
end

context = {
  user_loader: Dataloader.new do |ids|
    User.find(*ids)
  end
}

Schema.execute("{ user(id: 12) { name } }", context: context)

Batching

You can create loaders by providing a batch loading function.

user_loader = Dataloader.new { |ids| User.find(*ids) }

A batch loading block accepts an Array of keys, and returns a Promise which resolves to an Array or Hash of values.

Dataloader will coalesce all individual loads which occur until first .sync is called on any promise returned by #load or #load_many, and then call your batch function with all requested keys.

user_loader.load(1)
  .then { |user| user_loader.load(user.invited_by_id)) }
  .then { |invited_by| "User 1 was invited by ${invited_by[:name]}" }

# Elsewhere in your backend
user_loader.load(2)
  .then { |user| user_loader.load(user.invited_by_id)) }
  .then { |invited_by| "User 2 was invited by ${invited_by[:name]}" }

A naive solution is to issue four SQL queries to get required information, but with Dataloader this application will make at most two queries (one to load users, and second one to load invites).

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 function

A batch loading function accepts an Array of keys, and returns Array of values or Hash that maps from keys to values (or a Promise that returns such Array or Hash). There are a few constraints that must be upheld:

For example, if your batch function was provided the Array of keys: [ 2, 9, 6 ], you could return one of following:

[
  { id: 2, name: "foo" },
  { id: 9, name: "bar" },
  { id: 6, name: "baz" }
]

{
  2 => { id: 2, name: "foo" },
  9 => { id: 9, name: "bar" },
  6 => { id: 6, name: "baz" }
}

Caching

Dataloader provides a memoization cache for all loads which occur withing single instance of it. After #load is called once with a given key, the resulting Promise 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:

promise1 = user_loader.load(1)
promise2 = user_loader.load(1)
promise1 == promise2 # => true

Caching per-request

Dataloader caching does not replace 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 same 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.

See Using with GraphQL section to see how you can pass dataloader instances using context.

Caching errors

If 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 an Error instance for an individual value, that Error will be cached to avoid frequently loading the same Error.

In some circumstances you may wish to clear the cache for these individual Errors:

user_loader.load(1).rescue do |error|
  user_loader.cache.delete(1)
  raise error
end

Disabling cache

In certain uncommon cases, a Dataloader which does not cache may be desirable. Calling Dataloader.new({ cache: nil }) { ... } will ensure that every call to #load will produce a new Promise, 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.

loader = Dataloader.new({ cache: nil }) do |keys|
  puts keys
  some_loading_function(keys)
end

loader.load('A')
loader.load('B')
loader.load('A')

// > [ 'A', 'B', 'A' ]

API

Dataloader

Dataloader is a class for fetching data given unique keys such as the id column (or any other key).

Each Dataloader instance contains a unique memoized cache. Because of it, it is recommended to use one Datalaoder instance per web request. You can use more long-lived instances, but then you need to take care of manually cleaning the cache.

You shouldn't share the same dataloader instance across different threads. This behavior is currently undefined.

Dataloader.new(options = {}, &batch_load)

Create a new Dataloader given a batch loading function and options.

#load(key)

key [Object] a key to load using batch_load

Returns a Promise of computed value.

You can resolve this promise when you actually need the value with promise.sync.

All calls to #load are batched until the first #sync is encountered. Then is starts batching again, et cetera.

#load_many(keys)

keys [Array<Object>] list of keys to load using batch_load

Returns a Promise<Array> of array of computed values.

To give an example, to multiple keys:

promise = loader.load_many(['a', 'b'])
object_a, object_b = promise.sync

This is equivalent to the more verbose:

promise = Promise.all([loader.load('a'), loader.load('b')])
object_a, object_b = promise.sync

#cache

Returns the internal cache that can be overridden with :cache option (see constructor)

This field is writable, so you can reset the cache with something like:

loader.cache = Concurrent::Map.new

#wait

Triggers all batched loaders until there are no keys to resolve.

This method is invoked automatically when the value of any promise is requested with #sync.

Here is the implementation that Dataloader sets as a default for Promise:

class Promise
  def wait
    Dataloader.wait
  end
end

License

MIT