Best practice around GraphQL nesting depth - api

Question

Is there an optimum maximum depth to nesting?
We are often presented with the option to try to represent complex heirarchical data models with the nesting they demonstrate in real life. In my work this is genetics and modelling protein / transcript / homology relationships where it is possible to have very deep nesting up to maybe 7/8 levels. We use dataloader to make nested batching more efficient and resolver level caching with directives. Is it good practice to model a schema on a real life data model or should you focus on making your resolvers reasonable to query and keep nesting to a maximum ideal depth of say 4 levels?
When designing a schema is it better to create a different parent resolver for a type or use arguments that to direct a conditional response?
If I have two sets of for example ‘cars’ let’s say I have cars produced by Volvo and cars produced by tesla and the underlying data while having similarities is originally pulled from different apis with different characteristics. Is it best practice to have a tesla_cars and volvo_cars resolver or one cars resolver which uses for example a manufacturer argument to act differently on the data it returns and homogenise the response especially where there may then be a sub resolver that expects certain fields which may not be similar in the original data.
Or is it better to say that these two things are both cars but the shape of the data we have for them is significantly different so its better to create seperate resolvers with totally or notably different fields?
Should my resolvers and graphQL apis try to model the data they describe or should I allow duplication in order to create efficient application focused queries and responses?
We often find ourselves wondering do we have a seperate API for application x and y that maybe use underlying data and possibly even multiple sources (different databases or even API calls) inside resolvers very differently or should we try to make a resolver work with any application even if that means using type like arguments to allow custom filtering and conditional behaviour?

Answer 1

Is there an optimum maximum depth to nesting?
In general I'd say: don't restrict your schema. Your resolvers / data fetchers will only get called when the client requests the corresponding fields.
Look at it from this point of view: If your client needs the data from 8 levels of the hierarchy to work, then he will ask for it no matter what. With a restricted schema the client will execute multiple requests. With an unrestricted schema he can get all he needs in a single request. Though the amount processing on your server side and amount of data will still be the same, just split across multiple network requests.
The unrestricted schema has several benefits:
The client can decide if he wants all the data at once or use multiple requests
The server may be able to optimize the data fetching process (i.e. don't fetch duplicate data) when he knows everything the client wants to receive
The restricted schema on the other hand has only downsides.
When designing a schema is it better to create a different parent resolver for a type or use arguments that to direct a conditional response
That's a matter of taste and what you want to achieve. But if you expect your application to grow and incorporate more car manufacturers, your API may become messy, if there are lot's of abc_cars and xyz_cars queries.
Another thing to keep in mind: Even if the shape of data is different, all cars have something in common: They are some kind of type Car. And all of them have for example a construction year. If you now want to be able to query "all cars sorted by construction year" you will need a single query endpoint.
You can have a single cars query endpoint in your api an then use interfaces to query different kinds of cars. Just like GraphQL Relay's node endpoint works: Single endpoint that can query all types that implement the Node interface.
On the other hand, if you've got a very specialized application, where your type is not extensible (like for example white and black chess pieces), then I think it's totally valid to have a white_pieces and black_pieces endpoint in your API.
Another thing to keep in mind: With a single endpoint some queries become extremely hard (or even impossible), like "sort white_pieces by value ascending, and black_pieces by value descending". This is much easier if there are separate endpoints for each color.
But even this is solvable if you have a single endpoint for all pieces, and simply call it twice.
Should my resolvers and graphQL apis try to model the data they describe or should I allow duplication in order to create efficient application focused queries and responses?
That's question of use case and scalability. If you have exactly two types of clients that use the API in different ways, just build two seperate APIs. But if you expect your application to grow, get more different clients, then of course it will become an unmaintainable mess to have 20 APIs.
In this case have a look at schema directives. You can for example decorate your types and fields to make them behave differently for each client or even show/hide parts of your API depending on the client.
Summary:
Build your API with your clients in mind.
Keep things object oriented, make use of interfaces for similar types.
Don't provide endpoints you clients don't need, you can still extend your schema later if necessary.
Think of your data a huge graph ;) that's what GraphQL is all about.