cuid2 3.1.0

A dart implementaion of the secure, collision-resistant ids optimized for horizontal scaling and performance (v2).

Cuid2

Secure, collision-resistant ids optimized for horizontal scaling and performance. Next generation UUIDs.

Need unique ids in your app? Forget UUIDs and GUIDs which often collide in large apps. Use Cuid2, instead.

Cuid2 is:

• Secure: It's not feasible to guess the next id, existing valid ids, or learn anything about the referenced data from the id. Cuid2 uses multiple, independent entropy sources and hashes them with a security-audited, NIST-standard cryptographically secure hashing algorithm (Sha3).
• Collision resistant: It's extremely unlikely to generate the same id twice (by default, you'd need to generate roughly 4,000,000,000,000,000,000 ids (sqrt(36^(24-1) * 26) = 4.0268498e+18) to reach 50% chance of collision.
• Horizontally scalable: Generate ids on multiple machines without coordination.
• Offline-compatible: Generate ids without a network connection.
• URL and name-friendly: No special characters.
• Fast and convenient: No async operations. Won't introduce user-noticeable delays. Less than 5k, gzipped.
• But not too fast: If you can hash too quickly you can launch parallel attacks to find duplicates or break entropy-hiding. For unique ids, the fastest runner loses the security race.

Cuid2 is not good for:

• Sequential ids (see the note on K-sortable ids, below)
• High performance tight loops, such as render loops (if you don't need cross-host unique ids or security, consider a simple counter for this use-case, or try Ulid or NanoId).

Learn more

Install

dart pub add cuid2

Usage

import 'package:cuid2/cuid2.dart';

void main() {
  final id = cuid();  // default options
  final id30 = cuidSecure(30);  // set length to 30, use Random.secure()
  final cc = cuidConfig(length: 30);  // custom config - see example

  print(cc.gen())
  print(id); // eh82waoo5fi41lgncwv5oxxb
  print(id30); // oxjkyfqo3aqk3jigelnuyp3ef299qx
}

Testing

a histogram analysis is done on every batch of tests to ensure a fair and random distribution across the whole entropy range. Any bias found during the analysis would increase the chances of ID collision and cause the tests to fail automatically.