Benchmark: Generate random string

Script Preparation code:

function gen1() {
    return Math.random().toString(36).slice(2)
}

class EntropyPool {
    #entropy;
    #index
    constructor(poolSize = 1024) {
        this.#entropy = new Uint32Array(poolSize)
        this.#index = 0
        crypto.getRandomValues(this.#entropy)
    }
    next() {
        const value = this.#entropy[this.#index++]
        if (this.#index === this.#entropy.length) {
            crypto.getRandomValues(this.#entropy)
            this.#index = 0
        }
        return value
    }
}

const pool = new EntropyPool()

function random() {
    return pool.next() / 4294967295
}

const lowercase = 'abcdefghijklmnopqrstuvwxyz';
const uppercase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ';
const digits = '0123456789';
const charset = `${lowercase}${uppercase}${digits}`;

function gen2() {
    let result = '';
    for (let i = 0; i < length; i++) {
        const id = randomInt([0, charset.length - 1]);
        result += charset[id];
    }
    return result;
}

Tests:

Test gen1
gen1()
Test gen2
gen2()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Test gen1
Test gen2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36

Browser/OS: Chrome 128 on Windows

View result in a separate tab

Test name	Executions per second
Test gen1	2377419.0 Ops/sec
Test gen2	3494086.8 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the provided benchmark and its components.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark that measures the performance of two different functions: `gen1()` and `gen2()`. The benchmark definition is as follows:

* **Script Preparation Code**: This section contains the code that sets up the environment for the benchmark. In this case, it defines a class `EntropyPool` that generates random numbers using the `crypto.getRandomValues()` function.
* **Html Preparation Code**: This section is empty, indicating that no HTML-specific setup is required.

**Test Cases**

The benchmark consists of two test cases:

1. **Test gen1**: Measures the performance of the `gen1()` function, which generates a random string by converting a random number to a base-36 string using `Math.random().toString(36).slice(2)`.
2. **Test gen2**: Measures the performance of the `gen2()` function, which generates a random string by iterating over a charset and selecting a random character from it.

**Options Compared**

In this benchmark, two different approaches are compared:

1. **Approach 1: Using EntropyPool (gen1())**: This approach uses an `EntropyPool` class to generate random numbers. The pool is initialized with a fixed number of entropy values, and the `next()` function returns the next available value.
2. **Approach 2: Manual Character Selection (gen2())**: This approach manually selects characters from a charset using the `randomInt()` function, which generates a random index into the charset.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Using EntropyPool (gen1())**:
	* Pros: Efficient use of entropy values, reduces overhead of generating random numbers.
	* Cons: Limited control over character selection, may not be suitable for all use cases.
2. **Manual Character Selection (gen2())**:
	* Pros: Provides more control over character selection, can be tailored to specific requirements.
	* Cons: May lead to slower performance due to the overhead of generating random indices.

**Other Considerations**

When choosing between these approaches, consider the following factors:

* Performance requirements: If speed is crucial, using an EntropyPool class may provide better performance.
* Character distribution: If a specific character distribution is required, manual character selection may be more suitable.
* Code readability and maintainability: Manual character selection can lead to more complex code, while using an EntropyPool class may result in simpler code.

**Library Used**

The `EntropyPool` class uses the `crypto.getRandomValues()` function from Node.js's built-in crypto module. This library is used to generate cryptographically secure random numbers.

**Special JS Feature or Syntax**

None of the benchmark code uses any special JavaScript features or syntax, such as ES6 modules, async/await, or WebAssembly.

I hope this explanation helps software engineers understand the provided benchmark and its components!

Related benchmarks:

Generate random string (version: 0)

Comparing performance of: Test gen1 vs Test gen2

Created: one year ago by: Guest

Jump to the latest result

Test gen1

Test gen2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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