You should already be familiar with GraphQL by now, but if you are still unsure about queries and mutations, don’t hesitate to click on the links to direct toward the article that talks about it.

In this one, we’ll be looking at how to optimize our query given a specific context. Not all solution may apply to your case, but hopefully it should give you some leads.

GraphQL Schema

For the purpose of an example, we’re going to use this schema:

type Movie @key(fields: "id") {
    id: ID!
    title: String!
    actors: [Actor]!

type Actor @key(fields: "id") {
    id: ID!
    name: String!
    movies: [Movie]!

We have the Movie type which have actors of type Actor. Since it’s a federated example we use the id as the key field to identify the resolved objects. In the backend the movies and actors are stored in different tables and will be resolved with different resolvers.

We also have a simple query to get the movie such as:

query {
    movie: Movie

With that setup, it will be easier to follow the rest of the examples in this article.

Resolver flow

Sequence Diagram

sequenceDiagram actor U as User participant API as GraphQL API participant DB as MySQL U ->> API: Query movie and actors opt Movie resolver API -->> DB: Query for movie DB -->> API: Return resolved movie end loop Actor resolver API -->> DB: Query actor DB -->> API: Return each resolved actors end API ->> U: Return resolved
movie and actors

The movie resolver will resolve the information and will contain the actors’ ids. The actor resolver is federated which means that for each Actor there’s one call to the actor’s resolver which will query the DB and retrieve the information from the database.

DB Calls

Let’s look at the DB calls in the previous flow, those are MySQL DB, so we’ll use the actual queries to demonstrate what is happening in the backend.

  1. First it resolves the movie:
SELECT * FROM movies WHERE id = 7
  1. Then for each actor we have an independent call to db each time the resolver is called with the actor’s id:
SELECT * FROM movies WHERE id = 1
SELECT * FROM movies WHERE id = 2
SELECT * FROM movies WHERE id = 3

With a movie with many actors, the performance may decrease with the amount of calls to the db to make.

Flow Diagram

When we see how each fields is getting resolved, it may become clearer. This flow diagram represents the fields being resolved, we have Movie which resolve the queried one and the actors field on the movie type is resolved by multiple single resolver:

graph TD M((Movie id=7)) --> id((id)) M --> A((actors)) A --> A1((actor id=1)) A --> A2((actor id=2)) A --> A3((actor id=3)) M --> title((title)) A1 --> aid((id)) A1 --> name((name))

To save some details, only the first actor’s resolved fields were represented (id and name), but all of them would resolve it.


How can we optimize the resolving process? It will depend on how often it’s queried by your users, don’t over optimize queries that are not critical as you may make it worse. Here are some leads that may or may not fit depending on your problem.

More resolvers?

One optimization which may not always be possible would be to have a actors resolver which would resolve all of them at once. This way, instead of resolving the actors one by one, we would make a call to the db for all of them at once.

Unfortunately in this use case the Actor type is federated, which means it might be resolved by an entirely other application within a different domain and team ownership.

For federated entities, the __resolveReference is always called.

In the case where we add the actors field is federated but within the app, we should also take into account that the __resolveReference on the Actor type will be called next, and might trigger another database call.

The resolver is called with its parent, so if it already contains the fetched entity there should be some logic within the resolve reference, so it does not call the database again. Because it would mean that it has been resolved by its parent resolver.

In those case, you will have to make a choice depending on how the users use your query:

  • which fields should always be returned?
  • which should be fetched on demand? So you gain maximum performance on your GraphQL query. (If you’ve turned to GraphQL that means fetching everything at once all the time is not necessary in most cases).

Check the info?

We talked about the info field in the advanced GraphQL article, which can be useful to know which fields are getting queried.

This way we can reduce the calls to make in the database, but let’s see how it would work in our example with this query:

query Movie {
    movie {
        actor {

If we know that only the id are going to be resolved from the actor, then since we by default receive those by federation on the resolver, we can skip the call to the database and return directly the output.

But when you actually need information from the database, this solution won’t reduce the amount of calls made.

Use a DataLoader?

The last solution we can leverage is the Dataloader which as its name says will solve the problem of calling the database for each individual actor. The dataloader will gather the calls to the actors database and use a bulk load function to get them all at once.

-- Transforming those queries
SELECT * FROM movies WHERE id = 1
SELECT * FROM movies WHERE id = 2
SELECT * FROM movies WHERE id = 3
-- Into this one
SELECT * FROM actors WHERE id IN (1, 2, 3)

This will reduce the amount of calls to the database for a resolved entity in this case. Why? 🥲 Because we are making \(n\) amount of calls depending on the amount of actors that starred in the movie. With the dataloader pattern, those calls will be caught and transformed into one call.

Here is how you would instantiate the Dataloader from graphql/dataloader, you can have it defined in your context:

import DataLoader from 'dataloader';

loader = new DataLoader(bacthFindFromIds)

function bacthFindFromIds(ids: readonly string[]): Promise<(Actor | undefined)[]> {
    return actorsDB.findAll(ids);

To simplify things a bit, we’re using actorsDB which would be our database, and it would have a batch function to find all ids. Note that:

  • The key for the batch function should be readonly (or you will have compilation errors with typescript).
  • The result’s order match should match the key order (or the dataloader won’t return the right information).
  • The number of results should match the number of keys (or it will crash).

Now instead of calling the database directly in your resolver, you can simply do:

export async function __resolveReference(
  parent: ActorReference, args: any, { loader }: AppContext
): Promise<Actor | undefined> {
  return loader.load(;

In this case, we’re using the loader from the context of your Application to load the actor.

For more use case on how to use the dataloader checkout the repository, there are a lot of explanation, examples and a video made by the creator himself that explains the inner mechanism of it.