Benchmark: generateRandomString

HTML Preparation code:

<!--your preparation HTML code goes here-->

Script Preparation code:

function generateRandomString1(length) {
  const characters =
    'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
  let result = '';
  for (let i = 0; i < length; i++) {
    const randomIndex = Math.floor(Math.random() * characters.length);
    result += characters.charAt(randomIndex);
  }
  return result;
}

Tests:

generateRandomString1
generateRandomString1(8)
Math.floor(Math.random() * 9000000000) + 1000000000
Math.floor(Math.random() * 9000000000) + 1000000000
Math.floor(Math.random() * 1000000000)
Math.floor(Math.random() * 1000000000)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
generateRandomString1
Math.floor(Math.random() * 9000000000) + 1000000000
Math.floor(Math.random() * 1000000000)

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
generateRandomString1	7116735.0 Ops/sec
Math.floor(Math.random() * 9000000000) + 1000000000	87343368.0 Ops/sec
Math.floor(Math.random() * 1000000000)	86376784.0 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 3 months ago):

LLMs can make mistakes. Check important info.

The benchmark aims to evaluate the performance of different methods for generating random values in JavaScript. Below, we will compare the various test cases, highlight their pros and cons, and discuss related considerations.

### Benchmarked Methods

1. **Custom Random String Generator (`generateRandomString1`)**:
   - **Definition**: `generateRandomString1(length)` generates a random alphanumeric string of a specified length.
   - **Implementation**: It defines a character set composed of uppercase letters, lowercase letters, and numbers. It then iterates up to the specified length, randomly selecting characters from this set.
   - **Pros**:
     - Generates complex and diverse strings, which can be useful for scenarios like passwords, tokens, or identifiers.
   - **Cons**:
     - More computationally intensive than simple numeric operations, as it involves multiple operations (string concatenation, random number generation) in a loop.
   - **Considerations**: While customizable, this method introduces overhead due to string manipulation in JavaScript, which can be slower than direct numeric calculations.

2. **Random Integer Generation – Range 1 Billion to 10 Billion**:
   - **Definition**: `Math.floor(Math.random() * 9000000000) + 1000000000` generates a random integer in the range of 1 billion to 10 billion.
   - **Pros**:
     - Simplistic and efficient for generating a random number, focusing on performance due to the fewer number of operations.
   - **Cons**:
     - Limited to numerical output without the versatility of string generation; hence may not work for scenarios that need random strings.
   - **Considerations**: This method leverages the built-in `Math.random()` which is optimized for performance by the JavaScript engine.

3. **Random Integer Generation – Range 0 to 1 Billion**:
   - **Definition**: `Math.floor(Math.random() * 1000000000)` outputs a random integer from 0 to 1 billion.
   - **Pros**:
     - Similar advantages as the above numeric generation: its efficiency and direct approach.
   - **Cons**:
     - Here, it generates numbers with a smaller range compared to the previous method, limiting its potential use cases.
   - **Considerations**: Like the previous case, it’s highly optimized and showcases the same performance qualities related to simplicity.

### Performance Results

Based on the performance metrics from the latest benchmark results:

- The fastest approach was generating a random number in the range of 1 billion to 10 billion (`Math.floor(Math.random() * 9000000000) + 1000000000`), with 87,343,368 executions per second.
- The second was the method generating a random number in the range of 0 to 1 billion (`Math.floor(Math.random() * 1000000000)`), with 86,376,784 executions per second.
- The slowest was the custom string generator, which had only 7,116,735 executions per second.

### Summary of Pros and Cons

- **Custom Random String Generation**: Versatile, but slower due to overhead.
- **Numeric Random Generation**: Fast and efficient, suitable for scenarios requiring random numbers, but limited in output variety.

### Other Alternatives

Beyond the tested approaches, several other alternatives exist for randomness in JavaScript:

- **`crypto.getRandomValues()`**: This method provides a more secure way to generate random values, especially useful for cryptographic purposes. It's generally slower than non-secure random number generation.
- **Third-party libraries**: Libraries like `lodash` or `chance.js` provide robust utilities for random data generation, including more complex randomized datasets such as random names, dates, or objects.
- **Web Workers**: For generating random data in a web environment without blocking the UI, using Web Workers can enhance performance by performing heavy tasks in the background.

In conclusion, while there are trade-offs in performance versus capability when choosing a random generation technique, understanding the requirements of your application will help you select the right approach.

Related benchmarks:

generateRandomString (version: 2)

Comparing performance of: generateRandomString1 vs Math.floor(Math.random() * 9000000000) + 1000000000 vs Math.floor(Math.random() * 1000000000)

Created: 3 months ago by: Registered User

Jump to the latest result

generateRandomString1

Math.floor(Math.random() * 9000000000) + 1000000000

Math.floor(Math.random() * 1000000000)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 3 months ago):