# Optimizing TypeScript performance with large Prisma schemas

When working with large database schemas in Prisma applications, a simple change in the type definition strategy can deliver massive performance improvements:

| Approach | Types | Instantiations | Memory | Compile Time |
|----------|-------|----------------|--------|--------------|
| **Direct Reference** | 269,597 | 2,772,929 | 395MB | 1.86s |
| **typeof technique** | 222 (**99.9% reduction**) | 152 (**99.9% reduction**) | 147MB (**62% reduction**) | 0.41s (**78% reduction**) |

(Performance was verified using `tsc --noEmit --extendedDiagnostics`.)

This guide shows you how to achieve these dramatic performance gains using TypeScript's `typeof` operator instead of direct type references.

## Test schema overview

The performance measurements were conducted using a deliberately complex Prisma schema with 30 interconnected models creating deep relationship chains:

```prisma
// Example of the test schema structure
model Tree1 {
  id        Int      @id @default(autoincrement())
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
  published Boolean  @default(false)
  title     String
  childId   Int
  Tree2     Tree2[]
}

model Tree2 {
  id        Int      @id @default(autoincrement())
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
  published Boolean  @default(false)
  title     String
  childTree Tree1    @relation(fields: [childId], references: [id])
  childId   Int
  Tree3     Tree3?   @relation(fields: [tree3Id], references: [id])
  tree3Id   Int?
}

// ... continues through Tree30 with similar interconnected patterns
```

This schema creates complex type dependencies that stress-test TypeScript compilation, simulating real-world enterprise applications with extensive table relationships.

## Problem

In enterprise applications with extensive database schemas—think e-commerce platforms with hundreds of product variants, financial systems with complex transaction hierarchies, or content management systems with intricate relationship webs—Prisma's generated types can become enormous.

A schema with 50+ tables and deep relationships can lead to:

- Compilation times exceeding several minutes
- High memory usage during type checking
- IDE responsiveness degrading significantly
- CI/CD pipelines timing out on type checks

## Solution

The solution involves using TypeScript's `typeof` operator instead of direct type references when defining function parameters that accept PrismaClient instances.
(Of course, if you're familiar with the TypeScript type system, you can use other methods.)

### Problematic approach for large schemas

```typescript
import { PrismaClient } from '../prisma/generated/client'

const saveFn = async (prismaClient: PrismaClient) => {
  // Function implementation
}

const client = new PrismaClient()
await saveFn(client)
```

**Performance impact:**
- Types: 269,597
- Instantiations: 2,772,929
- Memory usage: 394,718K
- Compilation time: 1.86s

### Optimized approach with `typeof`

```typescript
import { PrismaClient } from '../prisma/generated/client'

const saveFn = async (prismaClient: typeof client) => {
  // Function implementation
}

const client = new PrismaClient()
await saveFn(client)
```

**Performance impact:**
- Types: 222
- Instantiations: 152
- Memory usage: 146,854K
- Compilation time: 0.41s


## Why `typeof` is more efficient

The `typeof` operator creates a more efficient type resolution path by changing how TypeScript resolves types:

1. **Type Query Reference**: `typeof client` performs a type query that obtains the widened type of the identifier expression, avoiding the need to re-expand the complex `PrismaClient` type definition
2. **Reduced Type Instantiation**: The compiler avoids expanding the entire Prisma type hierarchy for each type check (resulting in a 99.9% reduction in instantiations)
3. **Memory Efficiency**: Referencing an existing instance's inferred type requires significantly less memory than expanding complex conditional types and generics

## Conclusion

When working with large Prisma schemas, the choice between direct type references and type queries becomes crucial for maintaining development velocity. The `typeof` approach isn't just an optimization—it's an essential technique for scaling TypeScript compilation performance as your database schema grows in complexity.

The 78% compilation time reduction demonstrated here scales exponentially with schema complexity, making this optimization foundational for maintaining an efficient development workflow in enterprise-scale applications.

## Benchmark

The complete benchmark code and test cases used to verify this analysis are available in the ts-bench repository:  
https://github.com/ToyB0x/ts-bench/pull/211

---

# Help articles