DataLoader is a generic utility to be used as part of your application's data fetching layer to provide a consistent API over various backends and reduce requests to those backends via batching and caching.
JavaScript Shell
Clone or download

README.md

DataLoader

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.

Build Status Coverage Status

A port of the "Loader" API originally developed by @schrockn at 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.

DataLoader is a simplified version of this original idea implemented in JavaScript for Node.js services. DataLoader is often used when implementing a graphql-js service, though it is also broadly useful in other situations.

This mechanism of batching and caching data requests is certainly not unique to Node.js or JavaScript, it is also the primary motivation for Haxl, Facebook's data loading library for Haskell. More about how Haxl works can be read in this blog post.

DataLoader is provided so that it may be useful not just to build GraphQL services for Node.js but also as a publicly available reference implementation of this concept in the hopes that it can be ported to other languages. If you port DataLoader to another language, please open an issue to include a link from this repository.

Getting Started

First, install DataLoader using npm.

npm install --save dataloader

To get started, create a DataLoader. Each DataLoader instance represents a unique cache. Typically instances are created per request when used within a web-server like express if different users can see different things.

Note: DataLoader assumes a JavaScript environment with global ES6 Promise and Map classes, available in all supported versions of Node.js.

Batching

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

var DataLoader = require('dataloader')

var userLoader = new DataLoader(keys => myBatchGetUsers(keys));

A batch loading function accepts an Array of keys, and returns a Promise which resolves to an Array 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.

userLoader.load(1)
  .then(user => userLoader.load(user.invitedByID))
  .then(invitedBy => console.log(`User 1 was invited by ${invitedBy}`));

// Elsewhere in your application
userLoader.load(2)
  .then(user => userLoader.load(user.lastInvitedID))
  .then(lastInvited => console.log(`User 2 last invited ${lastInvited}`));

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 Function

A batch loading function accepts an Array of keys, and returns a Promise which resolves to an Array of values. There are a few constraints that must be upheld:

  • The Array of values must be the same length as the Array of keys.
  • Each index in the Array of values must correspond to the same index in the Array of keys.

For example, if your batch function was provided the Array 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 key 6, which we can interpret as no value existing for that key.

To uphold the constraints of the batch function, it must return an Array of values the same length as the Array 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' },
  null,
  { id: 1, name: 'New York' }
]

Caching

DataLoader 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:

var userLoader = new DataLoader(...)
var promise1A = userLoader.load(1)
var promise1B = userLoader.load(1)
assert(promise1A === promise1B)

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 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 with express:

function createLoaders(authToken) {
  return {
    users: new DataLoader(ids => genUsers(authToken, ids)),
  }
}

var app = express()

app.get('/', function(req, res) {
  var authToken = authenticateUser(req)
  var loaders = createLoaders(authToken)
  res.send(renderPage(req, loaders))
})

app.listen()

Clearing Cache

In 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...
var userLoader = new DataLoader(...)

// And a value happens to be loaded (and cached).
userLoader.load(4).then(...)

// A mutation occurs, invalidating what might be in cache.
sqlRun('UPDATE users WHERE id=4 SET username="zuck"').then(
  () => userLoader.clear(4)
)

// Later the value load is loaded again so the mutated data appears.
userLoader.load(4).then(...)

// Request completes.

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:

userLoader.load(1).catch(error => {
  if (/* determine if should clear error */) {
    userLoader.clear(1);
  }
  throw error;
});

Disabling Cache

In certain uncommon cases, a DataLoader which does not cache may be desirable. Calling new DataLoader(myBatchFn, { cache: false }) 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.

For example:

var myLoader = new DataLoader(keys => {
  console.log(keys)
  return someBatchLoadFn(keys)
}, { cache: false })

myLoader.load('A')
myLoader.load('B')
myLoader.load('A')

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

More complex cache behavior can be achieved by calling .clear() or .clearAll() 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.

var myLoader = new DataLoader(keys => {
  identityLoader.clearAll()
  return someBatchLoadFn(keys)
})

API

class DataLoader

DataLoader creates a public API for loading data from a particular data back-end with unique keys such as the id column of a SQL table or document name in a MongoDB database, given a batch loading function.

Each DataLoader instance 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.

new DataLoader(batchLoadFn [, options])

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

  • batchLoadFn: A function which accepts an Array of keys, and returns a Promise which resolves to an Array of values.

  • options: An optional object of options:

    Option Key Type Default Description
    batch Boolean true Set to false to disable batching, invoking batchLoadFn with a single load key.
    maxBatchSize Number Infinity Limits the number of items that get passed in to the batchLoadFn.
    cache Boolean true Set to false to disable memoization caching, creating a new Promise and new key in the batchLoadFn for every load of the same key.
    cacheKeyFn Function key => key Produces cache key for a given load key. Useful when objects are keys and two objects should be considered equivalent.
    cacheMap Object new Map() Instance of Map (or an object with a similar API) to be used as cache.
load(key)

Loads a key, returning a Promise for the value represented by that key.

  • key: A key value to load.
loadMany(keys)

Loads multiple keys, promising an array of values:

var [ a, b ] = await myLoader.loadMany([ 'a', 'b' ]);

This is equivalent to the more verbose:

var [ a, b ] = await Promise.all([
  myLoader.load('a'),
  myLoader.load('b')
]);
  • keys: An array of key values to load.
clear(key)

Clears the value at key from the cache, if it exists. Returns itself for method chaining.

  • key: A key value to clear.
clearAll()

Clears the entire cache. To be used when some event results in unknown invalidations across this particular DataLoader. 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 with loader.clear(key).prime(key, value).) Returns itself for method chaining.

Using with GraphQL

DataLoader pairs nicely well with 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 {
    name
    bestFriend {
      name
    }
    friends(first: 5) {
      name
      bestFriend {
        name
      }
    }
  }
}

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 the SQLite example with clearer code and at most 4 database requests, and possibly fewer if there are cache hits.

var UserType = new GraphQLObjectType({
  name: 'User',
  fields: () => ({
    name: { type: GraphQLString },
    bestFriend: {
      type: UserType,
      resolve: user => userLoader.load(user.bestFriendID)
    },
    friends: {
      args: {
        first: { type: GraphQLInt }
      },
      type: new GraphQLList(UserType),
      resolve: (user, { first }) => queryLoader.load([
        'SELECT toID FROM friends WHERE fromID=? LIMIT ?', user.id, first
      ]).then(rows => rows.map(row => userLoader.load(row.toID)))
    }
  })
})

Common Patterns

Creating 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:

function createLoaders(authToken) {
  return {
    users: new DataLoader(ids => genUsers(authToken, ids)),
    cdnUrls: new DataLoader(rawUrls => genCdnUrls(authToken, rawUrls)),
    stories: new DataLoader(keys => genStories(authToken, keys)),
  };
}

// When handling an incoming web request:
var loaders = createLoaders(request.query.authToken);

// Then, within application logic:
var user = await loaders.users.load(4);
var pic = await loaders.cdnUrls.load(user.rawPicUrl);

Creating an object where each key is a DataLoader is one common pattern which provides a single value to pass around to code which needs to perform data loading, such as part of the rootValue in a graphql-js request.

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:

let userByIDLoader = new DataLoader(ids => genUsersByID(ids).then(users => {
  for (let user of users) {
    usernameLoader.prime(user.username, user);
  }
  return users;
}));

let usernameLoader = new DataLoader(names => genUsernames(names).then(users => {
  for (let user of users) {
    userByIDLoader.prime(user.id, user);
  }
  return users;
}));

Custom Caches

DataLoader can optionaly be provided a custom Map instance to use as its memoization cache. More specifically, any object that implements the methods get(), set(), delete() and clear() can be provided. This allows for custom Maps which implement various cache algorithms to be provided. By default, DataLoader uses the standard Map which simply grows until the DataLoader is released. The default is appropriate when requests to your application are short-lived.

Common Back-ends

Looking to get started with a specific back-end? Try the loaders in the examples directory.

Other implementations

Video Source Code Walkthrough

DataLoader Source Code Walkthrough (YouTube):