Projectious Work Aibox GRAPHQL Patterns

GraphQL Patterns

Intro

GraphQL schemas are designed from the client's perspective, not the database. Resolvers stay thin, DataLoader prevents N+1 fan-out, lists use Relay-style cursor pagination, and schemas evolve only by adding fields and deprecating the old ones.

Overview

Schema design

Model the domain as the client wants to consume it. Types are nouns (User, Order); queries are reads (user(id: ID!), orders(filter: OrderFilter)); mutations are verb-object writes (createOrder, cancelOrder); subscriptions are real-time streams (orderStatusChanged(orderId: ID!)).

Default fields to non-nullable (String!) and only mark them nullable when null is a meaningful state — the inverse of most REST APIs. Define custom scalars for domain values like DateTime and Email. Always use input types for mutation arguments so they can evolve independently of output types.

Resolver patterns

• Root resolvers handle top-level Query and Mutation fields.
• Field resolvers handle nested fields that come from a different source than the parent.
• Default resolvers return parent[fieldName] automatically — do not write trivial passthrough resolvers.

Resolvers stay thin: they call into a service or domain layer. Never embed business logic, validation, or SQL inside a resolver.

DataLoader and N+1

A list query that triggers one extra query per item is the N+1 problem. Solve it with DataLoader, which batches and caches reads within a single request:

const orderLoader = new DataLoader(async (userIds) => {
  const orders = await db.orders.findByUserIds(userIds);
  return userIds.map(id => orders.filter(o => o.userId === id));
});
// In User.orders resolver: (user) => orderLoader.load(user.id)

Two non-negotiable rules:

1. Create a new DataLoader instance per request. Sharing across requests leaks data between users.
2. The batch function must return results in the same order as the input keys, with null for missing entries.

Pagination (Relay Connection spec)

Use cursor-based connections for every list field:

type OrderConnection {
  edges: [OrderEdge!]!
  pageInfo: PageInfo!
}
type OrderEdge   { cursor: String!  node: Order! }
type PageInfo    { hasNextPage: Boolean!  hasPreviousPage: Boolean!  endCursor: String }

Cursors are opaque base64 tokens. Default first to 20, enforce a hard maximum of 100 to prevent unbounded queries.

Errors

Prefer typed union results for expected, domain-level errors so clients pattern-match in a type-safe way:

union CreateOrderResult = Order | ValidationError | InsufficientStock

Reserve the top-level errors array for unexpected failures (authentication, server errors, transport). Always include extensions.code (e.g. UNAUTHENTICATED, INTERNAL_SERVER_ERROR) so clients can branch on a stable string.

Schema evolution

• Adding types, fields, and enum values is always safe.
• Deprecating: mark with @deprecated(reason: "Use newField"), monitor field-usage telemetry, then remove once usage is near zero.
• Never change a field's type, and never make a nullable field non-nullable — both are silently breaking.

Gotchas

Agent-specific failure modes — provider-neutral pause-and-self-check items:

• DataLoader shared across requests. A DataLoader caches results for the lifetime of the instance. Sharing one instance across requests means user A's query result can be returned to user B. Always create a new DataLoader instance per request.
• N+1 resolvers without DataLoader. A list resolver that triggers one database query per item will fan out to N+1 queries for a list of N items. This is the most common GraphQL performance failure mode. Every nested field resolver that fetches from a database or external service needs to be batched with DataLoader.
• Making nullable fields non-nullable in a schema update. A nullable field made non-nullable is a breaking change for any client that previously received null and handled it. Schema evolution only goes in one direction: add fields, deprecate old ones, never tighten nullability.
• Returning Boolean from mutations. A mutation that returns true/false forces the client to re-fetch the entity to update its cache. Return the affected entity or a result union so the client has the data it needs without a round-trip.
• No depth or complexity limits in production. A deeply nested recursive query can cause exponential database fan-out. Without depth and complexity limits, a single malicious or buggy query can exhaust the server. Enable depth limiting and cost-based rate limiting.
• CRUD resolvers that mirror the database schema. GraphQL APIs should be designed from the client's consumption needs, not from the storage model. A mutation called updateOrder(id, status, tracking_number, shipped_at, ...) with 10 optional fields is a database operation disguised as an intent; model it as shipOrder(id, tracking_number) instead.
• One mutation that does everything with optional fields. Large mutations with many optional fields make it impossible for the client to know which fields are required for which operations and make server-side validation complex. Split into intent-specific, focused mutations.

Full reference

Federation

For microservice architectures using Apollo Federation:

• Each service owns a subgraph and marks shared entities with @key(fields: "id").
• The gateway composes subgraphs into a supergraph at build time, not at request time, so composition errors fail fast in CI.
• A subgraph only extends fields it can resolve from its own data — never reach across subgraphs from a resolver.

This keeps each subgraph independently deployable and avoids the distributed-monolith trap.

Persisted queries

In production, prefer automatic persisted queries (APQ): the client first sends only the SHA-256 hash of the query; the server looks it up and executes the cached query. Benefits:

• Reduced bandwidth (hash, not full query string).
• Allowlisting becomes possible — reject any query whose hash is not in the registry, blocking arbitrary or hostile queries.
• Easier query-level caching at the edge.

Performance and depth limits

Reject queries that exceed a sensible depth (e.g. 10) and complexity score. Without limits, a malicious client can request a deeply recursive graph and exhaust the server. Most GraphQL servers ship a depth-limit middleware — turn it on.

Rate-limit by query cost, not by request count. A single GraphQL request can be cheap or catastrophically expensive depending on the selection set.

Subscriptions

Subscriptions push events over WebSockets. Use them sparingly: each subscription holds a connection, so the server pays for every connected client. Prefer polling for low-frequency updates and reserve subscriptions for genuinely real-time UX (live order status, chat, collaborative editing).

Anti-patterns to avoid

• CRUD resolvers that mirror database tables. Design from the client's needs, not from the schema migration file.
• One mutation that does everything. Split into named intent-specific mutations (shipOrder, cancelOrder) instead of updateOrder(input) with twenty optional fields.
• Returning Boolean from mutations. Return the affected entity or a result union so the client can update its cache.
• DataLoader shared across requests. Per-request, always.
• Throwing strings or generic Error from resolvers. Use the result-union pattern for expected errors.
• Letting clients send arbitrary queries in production. Use APQ
  • allowlisting.

When to break the rules

Internal-only APIs with a single trusted client can skip persisted queries, depth limits, and federation. Tiny schemas (under ~20 types) can skip DataLoader if every resolver hits an in-memory cache. The result-union error pattern adds verbosity that may not be worth it for a small admin UI — top-level errors is fine there. As always, understand the rule first; only then break it deliberately.

Source: projectious-work/aibox — distributed by TomeVault.