Benchmark: qwe1234123 - MeasureThat.net

Tests:

qw
function createArray(n) { const arr = []; for (let i = 1; i <= n; i++) { arr.push(i); } return arr; } const result = createArray(1000);
qwe
function createArray(n) { return Array.from({ length: n }, (_, i) => i + 1); } const result = createArray(1000);
qwert
function createArray(n) { return [...Array(n).keys()].map((i) => i + 1); } const result = createArray(1000);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
qw
qwe
qwert

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmarking JSON and explain what's being tested, compared, and the pros/cons of each approach.

**Benchmark Definition**

The provided `Benchmark Definition` is a script that creates an array of numbers using different methods:

1. **Traditional Loop**: The first script uses a traditional loop to push numbers into an array: `arr.push(i);`. This method can be slow for large arrays due to the overhead of function calls and array resizing.
2. **Array.from() Method**: The second script uses the `Array.from()` method to create an array from an object with a length property. This method is more efficient than the traditional loop, as it avoids the overhead of array resizing.
3. **Array.prototype.keys().map() Method**: The third script uses the `keys()` method to get an iterator over the indices of an empty array, and then maps this iterator to create a new array with numbers: `[...Array(n).keys()].map((i) => i + 1)`. This method is similar to `Array.from()` but uses a different approach.

**Comparison**

The benchmark measures the execution time for each of these methods in creating an array of 1000 numbers. The results are compared across different browsers, devices, and platforms to determine which method is fastest.

**Pros and Cons**

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

1. **Traditional Loop**
	* Pros: Simple, easy to understand.
	* Cons: Slow due to array resizing and function call overhead.
2. **Array.from() Method**
	* Pros: Efficient, fast, and modern.
	* Cons: May not work in older browsers that don't support `Array.from()`.
3. **Array.prototype.keys().map() Method**
	* Pros: Similar to `Array.from()` but with a different approach.
	* Cons: May be slower due to the overhead of iterator creation.

**Library and Special JS Features**

The benchmark uses the following libraries:

1. **None**: There are no external libraries used in this benchmark.

The benchmark does not use any special JavaScript features, such as async/await or Promises.

**Other Considerations**

When creating benchmarks like this one, it's essential to consider the following factors:

* **Test scope**: The benchmark should test a specific piece of code or functionality.
* **Test coverage**: Ensure that the benchmark covers different scenarios and edge cases.
* **Browser support**: Test the benchmark in multiple browsers and platforms to ensure compatibility.

**Alternatives**

Other alternatives for measuring JavaScript performance include:

1. **JavaScript engines like V8, SpiderMonkey, or SquirrelFish**, which can be used to optimize and compare engine implementations.
2. **Benchmarking frameworks like Benchmark.js or js-benchmark**, which provide a more structured approach to benchmarking.
3. **WebAssembly benchmarks**, which measure the performance of JavaScript code compiled to WebAssembly.

In summary, the provided benchmark measures the execution time of three different methods for creating an array in JavaScript: traditional loop, `Array.from()`, and `Array.prototype.keys().map()`. The results are compared across browsers and platforms to determine which method is fastest.

Related benchmarks:

qwe1234123 (version: 0)

Comparing performance of: qw vs qwe vs qwert

Created: 2 years ago by: Guest

Jump to the latest result

qw

qwe

qwert

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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