Solving the N+1 Problem in GraphQL: A Performance Breakthrough
When implementing a GraphQL server using popular frameworks like Apollo Server, graphql-yoga, or graphql-php, you may have encountered frustrating roadblocks. Despite their many features, these frameworks often fall short in addressing critical concerns like server-side caching, schema-first vs. code-first approaches, subscription support, and ease of implementation and extension. One of the most significant hurdles is the N+1 problem, which can render GraphQL nearly useless if left unchecked.
The N+1 Problem: A Performance Killer
The N+1 problem arises when GraphQL resolvers only handle one object at a time, leading to an exponential increase in database queries as the graph grows deeper. For example, retrieving a list of directors and their films would require 1 + 10 = 11 queries, and adding each film’s list of actors would result in 1 + 10 + 100 = 111 queries. This problem can quickly spiral out of control, making GraphQL inefficient and slow.
Dealing with the N+1 Problem
Facebook’s DataLoader utility, implemented in Node.js, introduced the batching strategy to solve the N+1 problem. This approach defers resolving segments of the query until a later stage, allowing all objects of the same kind to be resolved together in a single query. While effective, this solution is often an afterthought, adding complexity to the development process.
A Better Approach: Baking in Deferred Resolution
Instead of offering deferred resolution as an add-on, GraphQL servers should incorporate it as the default strategy. By transferring the responsibility of resolving object types from resolvers to the server’s data loading engine, we can eliminate the N+1 problem. Here’s how:
- Resolvers return IDs, not objects: When resolving relationships, resolvers should return IDs, not objects.
- DataLoader retrieves objects: A DataLoader entity retrieves objects for the IDs, efficiently including all IDs in a single query.
- GraphQL server’s data loading engine: The engine glues the resolvers and DataLoader together, obtaining object IDs from resolvers and retrieving objects through the DataLoader.
Implementing the Adapted Approach
In PHP, the GraphQL by PoP implementation demonstrates this adapted strategy. We split resolvers into two entities: FieldResolvers and TypeDataLoaders. FieldResolvers resolve fields, while TypeDataLoaders load objects of a specific type. The code becomes more concise, with a clear separation of concerns.
Testing the New Approach
To demonstrate the effectiveness of this solution, we can execute a complex query involving a graph 10 levels deep. The results show that the N+1 problem is avoided, and the query is resolved efficiently.
Conclusion
The N+1 problem is GraphQL’s biggest performance hurdle. By incorporating deferred resolution into the core of the GraphQL server, we can avoid this problem and make GraphQL more efficient and scalable. With a simple reorganization of the server’s architecture, we can create a more robust and performant GraphQL implementation.