Benchmark: ID Generation Performance 2023

HTML Preparation code:

<script type="module">
  import { v4 as uuidv4, v1 as uuidv1 } from 'https://jspm.dev/uuid';
  import {ulid} from "https://jspm.dev/ulid";
  import {createId, init} from "https://jspm.dev/@paralleldrive/cuid2";
  import {nanoid} from "https://jspm.dev/nanoid";
  import {uid} from "https://jspm.dev/uid/secure";
  
  window.uuidv4 = uuidv4;
  window.uuidv1 = uuidv1;
  window.ulid = ulid;
  window.cuid2 = createId;
  window.cuid2_32 = init({length: 32});
  window.nanoid = nanoid;
  window.uid = uid;
  
  console.log("uuidv4", uuidv4());
  console.log("uuidv1", uuidv1());
  console.log("ulid", ulid());
  console.log("cuid2", window.cuid2());
  console.log("cuid2_32", window.cuid2_32());
  console.log("nanoid", nanoid());
  console.log("crypto.randomUUID", crypto.randomUUID());
  console.log("uid", uid(32));
    
</script>

Tests:

uuidv4
uuidv4()
uuidv1
uuidv1()
ulid
ulid()
cuid2
cuid2()
cuid2_32
cuid2_32()
nanoid
nanoid()
crypto.randomUUID()
crypto.randomUUID()
uid
uid(32)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
uuidv4
uuidv1
ulid
cuid2
cuid2_32
nanoid
crypto.randomUUID()
uid

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/142.0.0.0 YaBrowser/25.12.0.0 Safari/537.36

Browser/OS: Yandex Browser 25 on Windows

View result in a separate tab

Test name	Executions per second
uuidv4	2196087.8 Ops/sec
uuidv1	7680164.0 Ops/sec
ulid	104535.8 Ops/sec
cuid2	94244.8 Ops/sec
cuid2_32	62121.1 Ops/sec
nanoid	799548.0 Ops/sec
crypto.randomUUID()	1936652.5 Ops/sec
uid	11166427.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark test suite, specifically designed to compare the performance of various ID generation libraries.

**ID Generation Libraries and Their Purposes:**

1. **uuidv4**: A widely used random UUID (Universally Unique Identifier) generator from the `https://jspm.dev/uuid` package. It generates 128-bit UUIDs with high entropy.
2. **uuidv1**: Another type of UUID, which uses a timestamp-based algorithm to generate IDs. This library is deprecated in favor of v4 for its predictability and reduced uniqueness.
3. **ulid**: A time-segmented ID generator that produces unique identifiers based on timestamp and node ID information. It is designed for high-performance and low-latency applications.
4. **cuid2**: An implementation of the `@paralleldrive/cuid2` library, which generates cryptographically secure IDs using a combination of UUID-like values and a hash function.
5. **nanoid**: A lightweight, non-cryptographic ID generator that produces unique identifiers with a limited length (typically 4-10 characters).
6. **crypto.randomUUID**: A cryptographically secure random value generated by the `crypto` module in Node.js.

**Options Compared**

The benchmark compares the execution performance of each library under different scenarios:

1. **Library-specific functions**: The benchmark measures the time taken to execute each library's respective function (e.g., `uuidv4()`, `ulid()`).
2. **Library variants**: Some libraries have variant functions with modified parameters, such as `cuid2_32` (which generates IDs of a fixed length).

**Pros and Cons of Each Approach:**

1. **High-entropy UUIDs (uuidv4)**:
	* Pros: Suitable for most applications requiring unique identifiers.
	* Cons: May produce larger IDs than necessary, affecting storage or transmission efficiency.
2. **Time-segmented ID generators (ulid)**:
	* Pros: Designed for high-performance and low-latency applications; suitable for real-time systems.
	* Cons: May be less suitable for applications requiring more traditional UUIDs or cryptographically secure IDs.
3. **Cryptographic ID generators (cuid2, crypto.randomUUID)**:
	* Pros: Offers high security and uniqueness guarantees, making them suitable for sensitive applications.
	* Cons: Typically slower than other libraries due to the added cryptographic overhead.

**Benchmark Results**

The provided benchmark results show the execution performance of each library under various scenarios. The `ExecutionsPerSecond` values indicate the number of iterations performed within a second. These results can help developers choose the most suitable ID generation library for their specific use case, considering factors such as performance, security, and storage requirements.

By analyzing these results, you can make informed decisions about which ID generation library to use in your project, taking into account the trade-offs between performance, security, and uniqueness.

Related benchmarks:

ID Generation Performance 2023 (version: 0)

Compare ID generation libraries

Comparing performance of: uuidv4 vs uuidv1 vs ulid vs cuid2 vs cuid2_32 vs nanoid vs crypto.randomUUID() vs uid

Created: 2 years ago by: Guest

Jump to the latest result

uuidv4

uuidv1

ulid

cuid2

cuid2_32

nanoid

crypto.randomUUID()

uid

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):