Type errors caught at compile time cost minutes to fix. The same errors caught in production cost hours or days. We use TypeScript everywhere.
September 19, 2024 7 min read
# TypeScript Full-Stack: End-to-End Type Safety
Slug: typescript-fullstack-type-safety
Date: 2024-09-19
Tag: Tech Stack
Meta Description: How end-to-end TypeScript eliminates entire categories of bugs. Practical patterns for type-safe full-stack development with Next.js and Convex.
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Type errors caught at compile time cost minutes to fix. The same errors caught in production cost hours or days.
We use TypeScript everywhere—frontend, backend, database schemas. Not because we love static typing, but because end-to-end type safety eliminates entire categories of bugs that slow down development.
This post covers what full-stack type safety actually means, why it matters for startup velocity, and the practical patterns we use to achieve it.
What End-to-End Type Safety Means
Traditional web development has type boundaries everywhere:
Database schema defined in SQL or an ORM
Backend API returns JSON (untyped at the boundary)
Frontend code hopes the JSON matches expectations
UI components hope props match what's passed
Each boundary is a place where types can drift. The database schema changes, but the API response type isn't updated. The API returns a new field, but the frontend doesn't know it exists.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
End-to-end type safety eliminates these boundaries. One type definition flows from database to UI:
javascript
Database Schema (TypeScript)
↓
Backend Functions (TypeScript, types inferred from schema)
↓
API Layer (no boundary—types pass through)
↓
Frontend Components (TypeScript, same types as backend)
When you rename a field in the database schema, TypeScript errors appear everywhere that field is used—frontend, backend, tests. You fix them before the code runs.
Why This Matters for Startups
Startups change rapidly. Requirements shift weekly. Features get added, removed, and reshaped constantly.
In a loosely-typed codebase, every change is risky:
Will this database migration break the API?
Did I update all the frontend components that use this field?
Is there a conditional somewhere that depends on this value being a string?
You either move slowly (checking everything manually) or move fast (and discover bugs in production).
With end-to-end type safety:
Rename a field → TypeScript shows every location to update
Remove a property → Compile errors highlight all usages
Change a function signature → IDE shows all call sites
You can refactor aggressively without fear. The compiler catches what you miss.
The Velocity Argument
Type safety slows down the first hour of writing code. You're defining types, adding annotations, fixing compiler errors.
Type safety speeds up every subsequent hour:
No runtime type errors in production
Refactoring is safe
IDE autocompletion works everywhere
Documentation is automatic (types are the docs)
Code review focuses on logic, not "did you handle the null case"
For an MVP that ships once and never changes, the overhead isn't worth it. For a product that will evolve over months and years, the compound benefit is substantial.
How We Achieve It with Convex
Convex is the key piece that makes full-stack type safety practical. The schema definition generates types that flow through the entire application.
This schema is TypeScript code, not a separate DSL. Convex generates types from it automatically.
Backend Functions with Type Inference
Query and mutation functions get their types inferred:
typescript
// convex/tasks.ts
import { query, mutation } from "./_generated/server";
import { v } from "convex/values";
export const listByProject = query({
args: { projectId: v.id("projects") },
handler: async (ctx, args) => {
// ctx.db is fully typed
// args.projectId is typed as Id<"projects">
return await ctx.db
.query("tasks")
.withIndex("by_project", (q) => q.eq("projectId", args.projectId))
.collect();
// Return type is automatically Task[]
},
});
export const create = mutation({
args: {
projectId: v.id("projects"),
title: v.string(),
assigneeId: v.optional(v.id("users")),
},
handler: async (ctx, args) => {
// TypeScript ensures all required fields are included
return await ctx.db.insert("tasks", {
projectId: args.projectId,
title: args.title,
completed: false,
assigneeId: args.assigneeId,
// Omitting a required field → compile error
});
},
});
You don't write interface definitions. The types come from the schema and flow through your functions.
Frontend with Type-Safe Queries
On the frontend, the same types apply:
typescript
// components/TaskList.tsx
import { useQuery, useMutation } from "convex/react";
import { api } from "@/convex/_generated/api";
function TaskList({ projectId }: { projectId: Id<"projects"> }) {
// tasks is typed as Task[] | undefined
const tasks = useQuery(api.tasks.listByProject, { projectId });
// createTask has typed arguments
const createTask = useMutation(api.tasks.create);
const handleCreate = async (title: string) => {
await createTask({
projectId,
title,
// Missing required field → TypeScript error
// Wrong field type → TypeScript error
});
};
return (
<ul>
{tasks?.map((task) => (
// task.title is typed as string
// task.unknownField → TypeScript error
<li key={task._id}>{task.title}</li>
))}
</ul>
);
}
The api object is generated by Convex and contains type information for all your functions. Your IDE knows exactly what arguments each function accepts and what it returns.
For objects used across many components, export explicit types:
typescript
// convex/types.ts
import { Doc, Id } from "./_generated/dataModel";
// Document types from schema
export type User = Doc<"users">;
export type Project = Doc<"projects">;
export type Task = Doc<"tasks">;
// Custom types for specific use cases
export type TaskWithAssignee = Task & {
assignee: User | null;
};
export type ProjectSummary = {
project: Project;
taskCount: number;
completedCount: number;
};
Components import these types:
typescript
import { Task, TaskWithAssignee } from "@/convex/types";
function TaskCard({ task }: { task: Task }) {
// ...
}
function TaskDetailCard({ task }: { task: TaskWithAssignee }) {
// task.assignee is available and typed
}
Pattern 2: Validated API Inputs
For external inputs (forms, URL parameters), validate at the boundary:
typescript
// lib/validation.ts
import { z } from "zod";
export const createProjectSchema = z.object({
name: z.string().min(1).max(100),
description: z.string().max(1000).optional(),
});
export type CreateProjectInput = z.infer<typeof createProjectSchema>;
Use the validated type in your components:
typescript
function CreateProjectForm() {
const createProject = useMutation(api.projects.create);
const handleSubmit = async (data: CreateProjectInput) => {
// data is validated and typed
await createProject(data);
};
return (
<form onSubmit={/* validate with Zod, then call handleSubmit */}>
{/* ... */}
</form>
);
}
The validation schema and TypeScript type derive from the same source. They can't drift.
Pattern 3: Discriminated Unions for State
Use TypeScript's union types for states that have different shapes:
typescript
type LoadingState<T> =
| { status: "loading" }
| { status: "error"; error: Error }
| { status: "success"; data: T };
function useAsyncData<T>(query: () => Promise<T>): LoadingState<T> {
// Implementation
}
// Usage
function TasksView({ projectId }) {
const state = useAsyncData(() => fetchTasks(projectId));
switch (state.status) {
case "loading":
return <Spinner />;
case "error":
// TypeScript knows state.error exists here
return <Error message={state.error.message} />;
case "success":
// TypeScript knows state.data exists here
return <TaskList tasks={state.data} />;
}
}
TypeScript narrows the type based on the status check. You can't accidentally access data when in loading state.
Pattern 4: Branded Types for IDs
Prevent mixing up IDs of different types:
typescript
// Convex does this automatically with Id<"tableName">
const userId: Id<"users"> = "..." as Id<"users">;
const projectId: Id<"projects"> = "..." as Id<"projects">;
// This is a type error:
// const wrongId: Id<"users"> = projectId;
Without branded types, all IDs are strings. It's easy to pass a project ID where a user ID is expected. With Convex's typed IDs, the compiler catches this.
Pattern 5: Exhaustive Switch Statements
Ensure you handle all cases:
typescript
type TaskStatus = "pending" | "in_progress" | "completed" | "cancelled";
function getStatusColor(status: TaskStatus): string {
switch (status) {
case "pending":
return "gray";
case "in_progress":
return "blue";
case "completed":
return "green";
case "cancelled":
return "red";
// No default needed—TypeScript knows all cases are covered
}
}
If you add a new status later, TypeScript errors appear at every switch statement that doesn't handle it.
Type Safety Across the Network Boundary
The trickiest part of full-stack type safety is the network. HTTP requests send JSON, which is inherently untyped.
The Traditional Problem
With REST APIs:
typescript
// Frontend assumes this shape
interface User {
id: string;
name: string;
email: string;
}
const user: User = await fetch("/api/user/123").then((r) => r.json());
// TypeScript trusts you, but the API could return anything
TypeScript can't verify that the API actually returns what you declared. The types are aspirational, not guaranteed.
How Convex Solves This
Convex functions are called directly, not through HTTP endpoints you define:
typescript
// This is a function call, not a fetch
const user = useQuery(api.users.get, { id: userId });
// The return type comes from the function definition itself
The type information comes from the actual function code, not a separate interface you hope matches. If the function changes its return type, the frontend immediately shows type errors.
tRPC for Traditional Backends
If you're using a traditional backend (not Convex), tRPC provides similar benefits:
typescript
// Server
const appRouter = router({
user: router({
get: publicProcedure.input(z.object({ id: z.string() })).query(async ({ input }) => {
return await db.users.findUnique({ where: { id: input.id } });
// Return type flows to client
}),
}),
});
// Client
const user = trpc.user.get.useQuery({ id: "123" });
// Fully typed based on server definition
tRPC generates a type-safe client from your router definition. Types flow from server to client automatically.
Practical Benefits We See
Refactoring Confidence
Rename user.username to user.displayName across the codebase:
Change the schema field
Run tsc (TypeScript compiler)
Fix every error that appears
Done—no hidden usages remain
This takes 30 minutes instead of 3 hours of searching and hoping.
Better IDE Experience
With full type coverage:
Autocompletion shows available fields
Hover shows types and documentation
Go to definition works across frontend/backend
Find all references works across the codebase
The IDE becomes genuinely helpful instead of occasionally useful.
Fewer Production Bugs
Categories of bugs that essentially disappear:
"Cannot read property X of undefined" (fields that don't exist)
Typos in property names
Wrong argument types to functions
Missing required fields
Incorrect enum values
These are trivial bugs, but they consume debugging time. Eliminating them entirely is valuable.
Onboarding Speed
New developers understand the codebase faster:
Types document what data structures exist
IDE guidance shows how to use functions
Compile errors prevent common mistakes
Junior developers can contribute safely because TypeScript catches their mistakes before code review.
When Type Safety Isn't Worth It
Full type safety has costs:
Learning curve for developers new to TypeScript
Overhead for truly disposable prototypes
Occasional fights with the type system
Some libraries have poor type definitions
When to skip or reduce type strictness:
24-hour hackathon: Speed matters more than correctness
Pure UI prototype: No real data, just layout exploration
Script you'll run once: Throwaway code doesn't need guarantees
For anything you'll maintain beyond a week, the investment pays off. To understand how type safety works with modern React patterns, see React 19 Server Components Explained.
Migration Path for Existing Projects
If you have a JavaScript codebase:
Phase 1: Add TypeScript Compiler
Rename .js to .ts. Enable TypeScript with loose settings:
Fix errors as they appear. The codebase becomes progressively safer.
Phase 4: Achieve End-to-End
Connect the pieces:
Use Convex or tRPC for type-safe API boundaries
Generate types from database schema
Remove all any types
The timeline is typically 2-4 weeks for an MVP-sized codebase, done incrementally alongside feature development.
Key Takeaways
End-to-end type safety isn't about loving static types. It's about practical benefits:
Refactoring is safe: Change anything, find all affected code
Fewer production bugs: Catch type errors at compile time
Better IDE support: Autocompletion and navigation actually work
Faster onboarding: Types document the codebase
The tools that make this practical:
TypeScript: The language foundation
Convex: Type-safe database to frontend flow
Zod: Validated external inputs
tRPC: Type-safe APIs if not using Convex
The overhead is real but front-loaded. After the initial setup, type safety accelerates development rather than slowing it.
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At NextBuild, we use TypeScript, Convex, and Next.js for full-stack type safety on every project. If you want an MVP with a codebase that's safe to refactor from day one, let's discuss your project.
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