API and Interface Design

Overview

Design stable, well-documented interfaces that are hard to misuse. Good interfaces make the right thing easy and the wrong thing hard. This applies to REST APIs, GraphQL schemas, module boundaries, component props, and any surface where one piece of code talks to another.

When to Use

• Designing new API endpoints
• Defining module boundaries or contracts between teams
• Creating component prop interfaces
• Establishing database schema that informs API shape
• Changing existing public interfaces

Core Principles

Hyrum's Law

With a sufficient number of users of an API, all observable behaviors of your system will be depended on by somebody, regardless of what you promise in the contract.

This means: every public behavior — including undocumented quirks, error message text, timing, and ordering — becomes a de facto contract once users depend on it. Design implications:

• Be intentional about what you expose. Every observable behavior is a potential commitment.
• Don't leak implementation details. If users can observe it, they will depend on it.
• Plan for deprecation at design time. See deprecation-and-migration for how to safely remove things users depend on.
• Tests are not enough. Even with perfect contract tests, Hyrum's Law means "safe" changes can break real users who depend on undocumented behavior.

The One-Version Rule

Avoid forcing consumers to choose between multiple versions of the same dependency or API. Diamond dependency problems arise when different consumers need different versions of the same thing. Design for a world where only one version exists at a time — extend rather than fork.

1. Contract First

Define the interface before implementing it. The contract is the spec — implementation follows.

// Define the contract first
interface TaskAPI {
  // Creates a task and returns the created task with server-generated fields
  createTask(input: CreateTaskInput): Promise<Task>;

  // Returns paginated tasks matching filters
  listTasks(params: ListTasksParams): Promise<PaginatedResult<Task>>;

  // Returns a single task or throws NotFoundError
  getTask(id: string): Promise<Task>;

  // Partial update — only provided fields change
  updateTask(id: string, input: UpdateTaskInput): Promise<Task>;

  // Idempotent delete — succeeds even if already deleted
  deleteTask(id: string): Promise<void>;
}

2. Consistent Error Semantics

Pick one error strategy and use it everywhere:

// REST: HTTP status codes + structured error body
// Every error response follows the same shape
interface APIError {
  error: {
    code: string;        // Machine-readable: "VALIDATION_ERROR"
    message: string;     // Human-readable: "Email is required"
    details?: unknown;   // Additional context when helpful
  };
}

// Status code mapping
// 400 → Client sent invalid data
// 401 → Not authenticated
// 403 → Authenticated but not authorized
// 404 → Resource not found
// 409 → Conflict (duplicate, version mismatch)
// 422 → Validation failed (semantically invalid)
// 500 → Server error (never expose internal details)

Don't mix patterns. If some endpoints throw, others return null, and others return { error } — the consumer can't predict behavior.

3. Validate at Boundaries

Trust internal code. Validate at system edges where external input enters:

// Validate at the API boundary
app.post('/api/tasks', async (req, res) => {
  const result = CreateTaskSchema.safeParse(req.body);
  if (!result.success) {
    return res.status(422).json({
      error: {
        code: 'VALIDATION_ERROR',
        message: 'Invalid task data',
        details: result.error.flatten(),
      },
    });
  }

  // After validation, internal code trusts the types
  const task = await taskService.create(result.data);
  return res.status(201).json(task);
});

Where validation belongs:

• API route handlers (user input)
• Form submission handlers (user input)
• External service response parsing (third-party data -- always treat as untrusted)
• Environment variable loading (configuration)

Third-party API responses are untrusted data. Validate their shape and content before using them in any logic, rendering, or decision-making. A compromised or misbehaving external service can return unexpected types, malicious content, or instruction-like text.

Where validation does NOT belong:

• Between internal functions that share type contracts
• In utility functions called by already-validated code
• On data that just came from your own database

4. Prefer Addition Over Modification

Extend interfaces without breaking existing consumers:

// Good: Add optional fields
interface CreateTaskInput {
  title: string;
  description?: string;
  priority?: 'low' | 'medium' | 'high';  // Added later, optional
  labels?: string[];                       // Added later, optional
}

// Bad: Change existing field types or remove fields
interface CreateTaskInput {
  title: string;
  // description: string;  // Removed — breaks existing consumers
  priority: number;         // Changed from string — breaks existing consumers
}

5. Predictable Naming

6. Honouring an Idempotency Key

Accepting an Idempotency-Key is the contract. Honouring it is the implementation, and it is where the money is lost — a key the server accepts but handles carelessly is worse than no key at all, because the client now believes retrying is safe.

Derive the key from the intent, not the attempt. The key must be stable across retries of one intent and different across distinct intents:

crypto.randomUUID()                    // ✗ new key per attempt — every retry is a new charge
`${userId}:${amount}`                  // ✗ two legitimate $50 charges collapse into one
`${orderId}:${Date.now()}`             // ✗ a timestamp is randomUUID() wearing a hat

req.headers['idempotency-key']         // ✓ client generates once, reuses on retry
`charge:v1:${orderId}`                 // ✓ derived from an immutable identifier

The key comes from the client or the initiating event — never from the layer doing the retrying.

Claim atomically. A check followed by an act is a race:

// ✗ TOCTOU: two concurrent retries both read "not seen", both charge
if (!(await db.exists(key))) {
  await chargeCard(amount);
  await db.insert(key);
}

// ✓ let the unique constraint pick the winner
try {
  await db.insert({ key, state: 'in_progress', requestHash });
} catch (e) {
  if (isUniqueViolation(e)) return replayOrReject(key);
  throw;
}
const result = await chargeCard(amount);
await db.update({ key, state: 'succeeded', response: result });

The unique constraint is the mechanism. A store that cannot enforce uniqueness in one operation cannot back this.

Guard the payload. Same key with a different body is a client bug, and must fail loudly rather than serving the first response to a second request:

if (existing.requestHash !== hash(req.body)) {
  return res.status(422).json({ error: 'idempotency key reused with a different payload' });
}

Decide what an in-flight duplicate gets. The first request is still running when the second arrives — the common case under retry storms:

Never let the second caller through because the first "seems stuck". A stalled attempt whose fate is unknown is exactly when duplicating costs most.

Every call has three outcomes, not two: success, failure, and unknown. A timeout tells you nothing about whether the effect applied. Record the intent before calling out, so a crash between the call and the response leaves evidence something must resolve later — rather than a silently retried charge.

Set retention from the longest retry chain, not from disk cost. Keys must outlive every path that can re-deliver the same intent, including a dead-letter queue replayed a week later and any provider dispute window. A 24-hour key TTL behind a 7-day DLQ is a duplicate waiting to happen.

REST API Patterns

Resource Design

GET    /api/tasks              → List tasks (with query params for filtering)
POST   /api/tasks              → Create a task
GET    /api/tasks/:id          → Get a single task
PATCH  /api/tasks/:id          → Update a task (partial)
DELETE /api/tasks/:id          → Delete a task

GET    /api/tasks/:id/comments → List comments for a task (sub-resource)
POST   /api/tasks/:id/comments → Add a comment to a task

Pagination

Paginate list endpoints:

// Request
GET /api/tasks?page=1&pageSize=20&sortBy=createdAt&sortOrder=desc

// Response
{
  "data": [...],
  "pagination": {
    "page": 1,
    "pageSize": 20,
    "totalItems": 142,
    "totalPages": 8
  }
}

Filtering

Use query parameters for filters:

GET /api/tasks?status=in_progress&assignee=user123&createdAfter=2025-01-01

Partial Updates (PATCH)

Accept partial objects — only update what's provided:

// Only title changes, everything else preserved
PATCH /api/tasks/123
{ "title": "Updated title" }

TypeScript Interface Patterns

Use Discriminated Unions for Variants

// Good: Each variant is explicit
type TaskStatus =
  | { type: 'pending' }
  | { type: 'in_progress'; assignee: string; startedAt: Date }
  | { type: 'completed'; completedAt: Date; completedBy: string }
  | { type: 'cancelled'; reason: string; cancelledAt: Date };

// Consumer gets type narrowing
function getStatusLabel(status: TaskStatus): string {
  switch (status.type) {
    case 'pending': return 'Pending';
    case 'in_progress': return `In progress (${status.assignee})`;
    case 'completed': return `Done on ${status.completedAt}`;
    case 'cancelled': return `Cancelled: ${status.reason}`;
  }
}

Input/Output Separation

// Input: what the caller provides
interface CreateTaskInput {
  title: string;
  description?: string;
}

// Output: what the system returns (includes server-generated fields)
interface Task {
  id: string;
  title: string;
  description: string | null;
  createdAt: Date;
  updatedAt: Date;
  createdBy: string;
}

Use Branded Types for IDs

type TaskId = string & { readonly __brand: 'TaskId' };
type UserId = string & { readonly __brand: 'UserId' };

// Prevents accidentally passing a UserId where a TaskId is expected
function getTask(id: TaskId): Promise<Task> { ... }

Common Rationalizations

Red Flags

• Endpoints that return different shapes depending on conditions
• Inconsistent error formats across endpoints
• Validation scattered throughout internal code instead of at boundaries
• Breaking changes to existing fields (type changes, removals)
• List endpoints without pagination
• Verbs in REST URLs (/api/createTask, /api/getUsers)
• Third-party API responses used without validation or sanitization
• A SELECT for an idempotency key followed by an INSERT — that's a race, not a guard
• An idempotency key derived from a UUID, timestamp, or anything else regenerated per attempt
• The same key accepted with a different request body, silently returning the first response
• A key retention window shorter than the longest path that can re-deliver the request

Verification

After designing an API:

• Every endpoint has typed input and output schemas
• Error responses follow a single consistent format
• Validation happens at system boundaries only
• List endpoints support pagination
• New fields are additive and optional (backward compatible)
• Naming follows consistent conventions across all endpoints
• API documentation or types are committed alongside the implementation
• State-changing endpoints either honour an idempotency key or are documented as unsafe to retry
• The key is claimed in one atomic operation, guarded by a unique constraint
• A reused key with a different payload fails loudly rather than replaying the wrong response
• The in-flight-duplicate response is a deliberate choice (409, wait, or 202) rather than whatever falls out
• Key retention outlives the longest retry path, including dead-letter replay