How to handle slow resolver performance in Apollo Server with large datasets?

Server
1

Hello

I am running Apollo Server for a project that queries fairly large datasets & I have noticed that some of my resolvers become very slow when handling complex queries with multiple nested fields. :slightly_smiling_face:

For smaller requests; the response time is fine but when the query includes joins / deeply nested relations, the latency increases significantly. This makes the API feel sluggish for end users.:innocent:

I tried using DataLoader to batch database calls, which helped a little but I still see performance bottlenecks. :upside_down_face: I am wondering if the issue is more about the way my schema is structured or if I need to consider caching strategies. :innocent:

I am also not sure whether Apollo Server has built-in tools to log / profile slow resolvers so I can pinpoint the exact cause. Checked Server-Side Caching - Apollo GraphQL Docs guide related to this and found it quite informative. When I first had to look up what is Microsoft SQL Server while dealing with database performance, since understanding the backend is just as important as optimizing resolvers.:slightly_smiling_face:

Has anyone faced a similar issue and found a good workflow for improving resolver performance? Should I focus on schema design, caching / database optimization first?

Any guidance on best practices would be really helpful for developers who are scaling up their Apollo Server apps.:thinking:

Thank you !!:slightly_smiling_face:

2

Hello! :waving_hand:

In my opinion, caching should usually be the last optimization to consider. Not all requests should be cached, and not all of them will have the same cache key. Before jumping into caching, there are several things you can do to improve resolver performance.

Let’s start with a simple principle:
In GraphQL, the parent resolver always resolves before its children.

For example, take this query:

query ListFeaturedProducts {
  featuredProducts {
    id
    name
    reviews {
      average
    }
  }
}

If featuredProducts takes 1 second to resolve, and reviews takes 0.8 seconds, then the reviews resolver can only start after the 1 second from the parent has finished and the response will be returned in 1.8 second

What can you do?

  1. Inspect the info object
    You can check whether a child field (like reviews) was requested, and optimize accordingly, for example, by running a SQL JOIN only when it’s needed.
    A library like graphql-parse-resolve-info helps:
const parsedInfo = parseResolveInfo(info);
const isReviewsRequested = !!parsedInfo.fieldsByTypeName.Product?.reviews;

Then, you can use isReviewsRequested to decide whether to fetch reviews in the same query.

  1. Use @defer for partial responses
    Apollo supports @defer, which lets you return partial data as soon as it’s available instead of blocking everything on slower fields. This is especially useful for deeply nested or expensive fields.
    Knowing this, you can return two response in a single request, first returning the featuredProducts (without reviews) and after that returning the reviews field
    The easiest way is to add a specific version, for example graphql-js 17 or you can check
    GitHub - graphql/graphql-js: A reference implementation of GraphQL for JavaScript
query ListFeaturedProducts {
  featuredProducts {
    id
    name
    ...@defer {
      reviews {
        average
      }
    }
  }
}

The easiest way is to add a specific version, for example graphql-js 17 or you can check the experimental-features section: GitHub - graphql/graphql-js: A reference implementation of GraphQL for JavaScript

  1. Watch out for “global middlewares”
    Unlike REST, GraphQL executes resolvers (and any middleware logic you attach) per field. In the query above, there are 5 fields, so any global logic could run 5 times unnecessarily. Make sure cross-cutting logic like auth or logging is efficient and scoped properly.
  2. If using Prisma
    Take advantage of Prisma’s new JOIN strategies. This avoids multiple SELECTs and can drastically reduce query time for nested relations.
  3. Reduce GraphQL execution overhead
  • Enable GraphQL JIT to skip repeated parsing and validation of queries.
  • Use Persisted Queries to avoid sending large queries over the wire and reduce parsing cost.
  1. Add observability
    GraphOS / Apollo Studio gives you resolver-level metrics, which are super useful to identify bottlenecks. Alternatively, you can build a custom Apollo plugin or even drop simple console.log timings inside your resolvers to see what’s slow.