Pseudata

Stop Fighting Inconsistent Test Data

Your frontend expects Alice. Your backend returns Bob.
Different faker libraries = Different realities.

Pseudata solves this by making mock data generation a standardized algorithm—not a language-specific implementation.

📖 Read the full story: Why faker.js and Faker Don’t Agree

Key Features

Cross-Language Consistency

Same seed = same data across all languages. No more integration test mismatches.

Infinite Scale

O(1) instant access to billions of records. Zero memory overhead—virtual arrays calculate on demand.

Multi-Locale Support

15+ locales with culturally appropriate names, addresses, and geographic data.

Zero Dependencies

Pure implementation in every language. No FFI, no bindings, just native code.

Same Seed, Same Data—At Any Scale

Pseudata is the only faker library that guarantees both cross-language consistency and O(1) infinite scale.

❌ Traditional Fakers

Different data across languages

# Python (Faker)
Faker.seed(42)
fake.name()  # → "Brett Davis"

// TypeScript (faker.js)
faker.seed(42);
faker.person.fullName(); // → "Miss Dora Kiehn"

Sequential access only

# To get item 1,000,000, must iterate - O(n)
faker.seed(42)
for i in range(1_000_000):
    name = faker.name()

✅ Pseudata

Identical data across all languages

# Python
users = UserArray(42)
user = users.at(0)
print(user.name)  # → "John Smith"

// TypeScript
const users = new UserArray(42);
const user = users.at(0);
console.log(user.name); // → "John Smith"

Direct access to any index - O(1)

# Jump directly to the billionth record
users = UserArray(42)
user = users.at(1_000_000_000)  # Instant

Why This Matters

Cross-Language Consistency enables reliable integration testing across polyglot systems. When your Go backend and TypeScript frontend both generate User[1000] with seed 42, you get the same test data—eliminating the integration test mismatches that plague microservices architectures.

Direct O(1) Access unlocks scenarios impossible with traditional fakers:

• Load testing - Each test worker accesses its own range (worker 1 uses indices 0-999, worker 2 uses 1000-1999) without coordination
• Parallel processing - Split a billion-record dataset across processes instantly, no sequential generation required
• Sparse testing - Test edge cases at indices 1, 1000, 1000000, 1000000000 without generating intermediate records
• Reproducible demos - Jump to the “interesting” user at index 42857 every time

See It In Action

User[1000] with seed 42 = Always the same across every language:

Go

package main
import "github.com/pseudata/pseudata"

users := pseudata.NewUserArray(42)
user := users.At(1000)
fmt.Println(user.Name)  // → "John Smith"
fmt.Println(user.Email) // → "[email protected]"

Java

import dev.pseudata.UserArray;

UserArray users = new UserArray(42);
User user = users.at(1000);
System.out.println(user.getName());  // → "John Smith"
System.out.println(user.getEmail()); // → "[email protected]"

Python

from pseudata import UserArray

users = UserArray(42)
user = users.at(1000)
print(user.name)  # → "John Smith"
print(user.email) # → "[email protected]"

TypeScript

import { UserArray } from "@pseudata/core";

const users = new UserArray(42);
const user = users.at(1000);
console.log(user.name);  // → "John Smith"
console.log(user.email); // → "[email protected]"