Benchmark: Array Apply vs Array Spread vs Array Fill

Tests:

Array Fill
function createDateArrayInAMonth(year, month) { return new Array(new Date(year, month, 0).getDate()).fill().map((_, i) => new Date(year, month - 1, i + 1).toLocaleString()) } createDateArrayInAMonth(2023, 10)
Array Apply
function createDateArrayInAMonth(year, month) { return Array.apply(null, new Array(new Date(year, month, 0).getDate())).map((_, i) => new Date(year, month - 1, i + 1).toLocaleString()) } createDateArrayInAMonth(2023, 10)
Array Spread
function createDateArrayInAMonth(year, month) { return [...new Array(new Date(year, month, 0).getDate())].map((_, i) => new Date(year, month - 1, i + 1).toLocaleString()) } createDateArrayInAMonth(2023, 10)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array Fill
Array Apply
Array Spread

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 2 days ago):

LLMs can make mistakes. Check important info.

**Overview**

The provided JSON represents a JavaScript benchmark test case on the MeasureThat.net website. The test compares three different approaches for creating an array of dates in a given month: `Array Fill`, `Array Apply`, and `Array Spread`. Each approach is measured and compared to determine which one performs best.

**Approaches Compared**

1. **Array Fill**: This method uses the `fill()` method to initialize an empty array with a specified length, followed by mapping over the array to create dates.
2. **Array Apply**: This method uses the `apply()` method to call a function on an array object, passing the desired number of elements as the first argument.
3. **Array Spread**: This method uses the spread operator (`...`) to create an array with a specified length, followed by mapping over the array to create dates.

**Pros and Cons of Each Approach**

1. **Array Fill**:
	* Pros: Simple and concise syntax.
	* Cons: May be slower due to the overhead of `fill()` method.
2. **Array Apply**:
	* Pros: Can be more efficient for large arrays, as it avoids creating an intermediate array.
	* Cons: Syntax can be less readable and more verbose than other approaches.
3. **Array Spread**:
	* Pros: Modern syntax is concise and easy to read.
	* Cons: May require a newer JavaScript engine to support the spread operator.

**Library Usage**

None of the test cases use any external libraries or dependencies.

**Special JS Feature/Syntax**

The `map()` function is used in all three approaches, which is a modern JavaScript feature that allows transforming arrays into new arrays. The `toLocaleString()` method is also used to format dates as strings.

**Benchmark Results**

The latest benchmark results show the following:

| Test Name | Executions Per Second |
| --- | --- |
| Array Fill | 14924.818359375 |
| Array Apply | 14761.6669921875 |
| Array Spread | 14646.08984375 |

The results indicate that `Array Fill` performs best, followed by `Array Apply`, and then `Array Spread`.

**Alternative Approaches**

Other approaches to creating an array of dates in a given month might include:

* Using `Array.from()` method (a more modern and concise alternative to `apply()`)
* Utilizing a library like Lodash or Moment.js for date manipulation
* Implementing a custom loop-based approach
* Leveraging a Just-In-Time (JIT) compiler or other optimization techniques to improve performance.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Windows

View result in a separate tab

Test name	Executions per second
Array Fill	14924.8 Ops/sec
Array Apply	14761.7 Ops/sec
Array Spread	14646.1 Ops/sec

Related benchmarks:

Array Apply vs Array Spread vs Array Fill (version: 0)

Comparing performance of: Array Fill vs Array Apply vs Array Spread

Created: 10 months ago by: Guest

Jump to the latest result

Array Fill

Array Apply

Array Spread

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 2 days ago):