REST and GraphQL API in Directus: Complex Queries and Optimization Cases

When building headless projects on Directus, standard API generation often falls short of covering business logic. A typical scenario: the frontend makes 30 requests for a catalog page due to N+1 relations, filters don't work for nested fields, and GraphQL throws errors on complex aggregations. The client loses users due to slow load times. We are a team with five years of experience implementing Directus — we turn your API into a high-performance solution. Our clients save up to 40% on server resources and reduce TTFB from 2 seconds to 200 ms.

Problems We Solve

N+1 Queries with Relations

A standard REST request /items/articles returns only flat fields. If the frontend needs the author, category, and comments—it makes 3 additional requests per post. With 20 posts per page, that's 60 requests. The solution is to use fields=*,author.*,category.*,comments.* with a single request. But if you need to sort comments or limit their count, the deep parameter is required. We configure deep populate with custom sorting and limits, reducing requests to 1–2.

Filtering by Related Collections

A common task: show articles only from a specific category, but the category is a related record. In Directus, this is done via filter[category][slug][_eq]=tech. However, OR conditions with nested relations can produce incorrect results. For example, filter[_or][0][title][_icontains]=react&filter[_or][1][content][_icontains]=react works, but combining it with a filter on the author complicates the syntax. We use custom endpoints on Flows for complex logic.

Aggregations with Grouping

Dashboards often need monthly sales sums. Directus supports aggregate[sum]=total&groupBy[]=status, but you cannot group by two different fields in one request. We use the SDK and write custom SQL queries through migrations.

How We Do It: Stack and Use Case

A client project — an online store on Next.js + Directus 10. Task: build an API for a catalog with filters (price, brand, attributes), search via Meilisearch, and real-time cart updates. Stack: Directus (REST + WebSocket), Meilisearch for full-text search, Redis for caching.

Case: aggregating average check per day. Standard REST cannot do this—only one aggregation type per request. We wrote a custom endpoint on Flows with a SQL query:

SELECT DATE(date_created) as day, AVG(total) as avg_check
FROM orders
WHERE status = 'paid'
GROUP BY day
ORDER BY day;

Result: 1 request instead of 30, dashboard speed increased 4x. The client saved $2000/month on caching and CDN.

Work Process

1. Analysis: audit of current requests, identifying bottlenecks (Core Web Vitals, request count).
2. Design: choose REST, GraphQL, or WebSocket for tasks. Define the relation and filter schema.
3. Implementation: configure endpoints, custom Flows, optimization via fields and deep. For search, integrate Meilisearch or Elasticsearch.
4. Testing: load testing (k6), check with 1000 concurrent requests.
5. Deploy: set up rate limiting, CORS, SSL, caching (Redis/Varnish).

How to Set Up GraphQL in Directus?

Enable GraphQL by specifying in .env:

GRAPHQL_SDLFILE=/tmp/schema.graphql

GraphQL is available at /graphql. In production, disable introspection via GRAPHQL_INTROSPECTION=false. Use mutations for creating records and subscriptions for realtime.

Why REST API Is Faster Than GraphQL?

In Directus, REST API uses database-level caching (query keys), while GraphQL does not. For simple selections, REST gives lower latency (20–30% faster). But GraphQL is more convenient for complex nested queries with different fields. The choice depends on the task: for public endpoints — REST, for admin panels — GraphQL.

How to Integrate Search via Meilisearch?

Meilisearch is connected as a service in Directus via a Hook or Flow. Configure indexing of fields, relevance, and filters. Example configuration:

{
  "index": "articles",
  "primaryKey": "id",
  "searchableAttributes": ["title", "content"],
  "filterableAttributes": ["status", "category_id"]
}

After synchronization, data is available through a separate endpoint. Result: search in 10–50 ms instead of 500+ ms with full-text search in PostgreSQL.

Comparison of REST and GraphQL

Typical Mistakes When Setting Up Directus API

What's Included

• Audit of current requests and optimization.
• Setup of REST, GraphQL, or WebSocket for tasks.
• Writing custom endpoints on Flows for complex logic.
• Search integration (Meilisearch/Elasticsearch).
• Rate limiting, caching, security configuration.
• API documentation (OpenAPI/Swagger).
• Training for the client's team.
• 1 month support after launch.

Timeline

Basic REST/GraphQL setup: 2 to 5 days. Custom endpoints and complex aggregations: 5 to 10 days. Exact timeline after audit.

Get a free consultation for your project. Order a Directus API audit — we'll propose an optimized solution.

API Development with REST, GraphQL, WebSocket, and tRPC

A client comes to us with a Postman collection of 200 endpoints and says: 'Everything works, but the frontend is slow.' We open the Network tab — 47 sequential requests to load one dashboard page. Each one waits for the previous. This is not a server speed issue — it's an API architecture problem. With 10 years on the market, we've redesigned dozens of such integrations, and we guarantee: the right protocol and contract solve the problem at its root.

When REST stops being enough

REST works well for simple CRUD operations. But as soon as a mobile app appears alongside the web interface, over-fetching begins: the mobile app requests /api/users/123 and gets a 4KB object, but only needs name and avatar. Multiply that by a list of 50 users — 200KB traffic instead of 8KB.

GraphQL solves this with selection sets. The client describes exactly the fields it needs, and the server returns only those. On a project with React Native + Next.js, we migrated from REST to Apollo Server: payload size on the main screen dropped from 340KB to 28KB — a 92% traffic savings. Our certified engineers confirm: the typical pain when adopting GraphQL is N+1 query. A resolver for the author field on a post calls SELECT * FROM users WHERE id = ? for each post in the list. On a page with 20 posts — 21 database queries. Solved with DataLoader — it batches queries and turns them into one SELECT * FROM users WHERE id IN (...).

What is tRPC and how is it better than REST/GraphQL?

If the entire stack is TypeScript (Next.js + Node/Bun), tRPC removes a whole layer of problems. You define a procedure on the server — the client gets full type-safety automatically, without code generation and without Swagger. Renamed a field in the Zod schema — TypeScript highlights all places on the frontend where it's used. tRPC reduces code by 2 times compared to REST + Swagger + openapi-typescript: no need to maintain a separate specification and generate types — everything is inferred from runtime validators. However, tRPC is not suitable if the API is consumed by third-party clients or mobile apps in other languages — in such cases we use GraphQL or REST with OpenAPI specification.

WebSocket and real-time: when SSE, when WS?

HTTP polling every 5 seconds is an illusion of real-time with up to 5 seconds delay and useless server load. For chats, live notifications, collaborative editing — WebSocket or Server-Sent Events. SSE is a one-way stream from server to client, works over ordinary HTTP, automatically reconnects. Suitable for notifications, data streaming, progress bars. WebSocket is bidirectional, needed for chats and collaborative features. Experience shows: 80% of 'real-time' tasks are solved with SSE, not WebSocket — fewer infrastructure complexities.

A typical mistake: opening a WebSocket connection for each page component. On one project, the dashboard opened 12 parallel WS connections. The correct approach is one connection manager at the application level, subscriptions through it. In our work results, we always transfer the connection scheme and a ready solution.

Swagger / OpenAPI as a contract

Documentation written after the fact becomes outdated the day after release. We write the OpenAPI 3.1 specification before development starts; it becomes the contract between frontend and backend. The frontend generates types via openapi-typescript, the backend validates incoming data using generated schemas. Contract deviation from implementation is caught on CI, not during review. For Laravel — l5-swagger or dedoc/scramble. For Node.js — @fastify/swagger or Zod + zod-to-openapi.

How to properly authenticate an API?

JWT with long-lived access tokens without rotation is a source of problems when compromised. The correct scheme: access token for 15 minutes, refresh token for 30 days with rotation on each use. Refresh token stored in an httpOnly cookie, access token in memory (not in localStorage). For inter-service communication — API Keys with scope limitations or mTLS. OAuth 2.0 with PKCE for public clients (SPA, mobile).

How to handle versioning and backward compatibility?

Breaking changes in an API without versioning break clients. Three approaches we use in projects:

We guarantee backward compatibility through automated checks (oasdiff) on CI.

How we develop APIs: step-by-step plan

1. Analysis — audit of current integrations, data schema compilation, protocol selection (REST/GraphQL/tRPC/WebSocket).
2. Contract design — OpenAPI or SDL (GraphQL) before the first line of code.
3. Development — implementation per contract, unit tests for each endpoint.
4. Load testing — k6: 500 virtual users, 10 minutes, p95 latency ≤ 200ms.
5. Deployment — CI/CD with backward compatibility check, automatic documentation publication.
6. Team training — handover of Postman collection or Playground, connection instructions.

What is included (deliverables)

• OpenAPI 3.1 specification (or SDL for GraphQL).
• Generated client types for TypeScript / Dart / Kotlin.
• Set of automated tests covering all endpoints (unit + integration).
• Load tests (k6) and report (p50/p95/p99 latency, RPS).
• Documentation in Swagger UI / Redoc / GraphiQL.
• Team training (2–4 hour workshop).
• Support for 30 days after delivery (per contract).

Our experience

• 10+ years in the API development market.
• 200+ completed projects (REST, GraphQL, WebSocket, tRPC).
• 50+ certified engineers (AWS, Kubernetes, API Design).
• Traffic savings averaging 85% when migrating from REST to GraphQL for mobile apps.
• 100% backward compatibility — not a single broken client in the last 3 years.

Timeline

API development for a typical SaaS project with 30–50 endpoints: from 3 to 8 weeks depending on business logic complexity and number of external integrations. Migration of an existing REST API to GraphQL: from 2 to 6 weeks. Adding a WebSocket layer to an existing backend: from 1 to 3 weeks. Cost is calculated individually after an audit. Get a consultation — contact us to discuss your project.