Benchmark: uuid generation crypto.getRandomValues join

Tests:

uuidv4
bits = crypto.getRandomValues(new Uint8Array(20)) bits[5] = (bits[5] & 0x4F) | 0x40; bits[8] = (bits[8] & 0xBF) | 0x80; bits = [...bits].map(e => e.toString(16).padStart(2,'0') ).reduce((a,c,i)=>a+([4,7,10,13].includes(i)?'-':c));
random 1
Array(20).fill(0).map(()=>crypto.getRandomValues(new Uint8Array(1))[0].toString(16).padStart(2,'0')).reduce((a,c,i)=>a+([4,7,10,13].includes(i)?'-':c))
random 2
([1e3]+-10+-10+-1e5).replace(/\d/g, () => crypto.getRandomValues(new Uint8Array(1))[0].toString(16).padStart(2,'0'))
random 3
let s=[]; crypto.getRandomValues(new Uint8Array(20)).map( e => s.push(e.toString(16).padStart(2,'0')) ); [4,7,10,13].map( idx => s[idx]='-' ); s=s.join('');
uuidv4 with join
bits = crypto.getRandomValues(new Uint8Array(20)) bits[5] = (bits[5] & 0x4F) | 0x40; bits[8] = (bits[8] & 0xBF) | 0x80; bits = [...bits].map(e => e.toString(16).padStart(2,'0') ); [4,7,10,13].map( idx => bits[idx]='-' ); bits = bits.join('');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
uuidv4
random 1
random 2
random 3
uuidv4 with join

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0.1 Safari/605.1.15

Browser/OS: Safari 14 on Mac OS X 11.0

View result in a separate tab

Test name	Executions per second
uuidv4	246018.0 Ops/sec
random 1	66309.4 Ops/sec
random 2	83866.8 Ops/sec
random 3	345411.0 Ops/sec
uuidv4 with join	194023.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**Benchmark Overview**

The benchmark is designed to measure the performance of generating UUIDs (Universally Unique Identifiers) using JavaScript's `crypto` module. The tests aim to simulate real-world scenarios where UUIDs are generated and manipulated.

**Test Cases**

There are four test cases:

1. **uuidv4**: This test case generates a random UUID using `crypto.getRandomValues` and then joins the resulting hexadecimal values together.
2. **random 1**: This test case generates an array of random integers, converts them to hexadecimal strings, and then replaces each integer with its corresponding hexadecimal string.
3. **random 2**: Similar to `random 1`, but instead of replacing integers with their hexadecimal equivalents, it directly uses the resulting hexadecimal strings.
4. **uuidv4 with join**: This test case generates a random UUID using `crypto.getRandomValues` and then joins the resulting hexadecimal values together, just like in `uuidv4`.

**Library: crypto**

The `crypto` module is a built-in JavaScript library that provides functions for cryptographic operations, including generating random numbers. In this benchmark, it's used to generate random UUIDs.

**Special JS Features/Syntax**

None of the test cases use any special JavaScript features or syntax beyond what's standard in modern JavaScript.

**Approaches Compared**

The four test cases compare different approaches to generating and manipulating UUIDs:

* **Direct concatenation**: `uuidv4` and `uuidv4 with join` use direct concatenation of hexadecimal values to generate the final UUID.
* **Array manipulation**: `random 1` and `random 2` use array manipulation techniques (e.g., `map`, `replace`) to process the generated random numbers.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

* **Direct concatenation**:
	+ Pros: Simple, straightforward approach.
	+ Cons: May be slower due to string concatenation overhead.
* **Array manipulation**:
	+ Pros: Can be faster for large datasets, as it avoids string concatenation overhead.
	+ Cons: Requires additional memory and processing power.

**Other Alternatives**

If you were to rewrite these test cases using a different approach, some alternatives could include:

* Using a UUID library like `uuid` or `uuid-lite`, which provide more efficient and standardized UUID generation mechanisms.
* Implementing a custom UUID generator using bitwise operations or other algorithms.
* Using a different data structure, such as a binary tree or a hash table, to store and manipulate the generated UUIDs.

However, given the simplicity of the benchmark and its focus on comparing different approaches, it's unlikely that significant changes would be made to alter the underlying approach.

Related benchmarks:

uuid generation crypto.getRandomValues join (version: 0)

Comparing performance of: uuidv4 vs random 1 vs random 2 vs random 3 vs uuidv4 with join

Created: 4 years ago by: Guest

Jump to the latest result

uuidv4

random 1

random 2

random 3

uuidv4 with join

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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