Benchmark: Array Apply vs Array Spread

Tests:

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)
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)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array Spread
Array Apply

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years 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 Spread	14567.2 Ops/sec
Array Apply	14711.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

The provided JSON represents a benchmark comparison between two approaches: `Array.apply()` and `Array spread` (also known as the spread operator `...`). The benchmark is designed to test which approach is faster for creating an array of dates within a specific month.

**What is being tested?**

In this benchmark, we have two test cases:

1. `Array Apply`: This approach uses the `Array.apply()` method to create an array with a specified length. In this case, the length is determined by the number of days in the specified month.
2. `Array Spread`: This approach uses the spread operator (`...`) to create an array from an existing array. In this case, we use `new Array(new Date(year, month, 0).getDate())` as the source array.

**Options compared**

The two approaches are being compared in terms of execution speed (measured in executions per second).

**Pros and Cons of each approach:**

* **Array.apply()**:
	+ Pros: More readable code, as it explicitly states the intention of creating an array with a specific length.
	+ Cons: Can be slower due to the overhead of function calls and method lookups. Additionally, `apply()` can only be called on objects that have the `apply()` method, which can lead to errors if not handled properly.
* **Array spread**:
	+ Pros: More concise code, as it leverages the spread operator to create a new array from an existing one. This approach is also more modern and widely adopted.
	+ Cons: Can be slower due to the overhead of creating a new array using the spread operator.

**Library usage**

In this benchmark, there are no libraries used beyond the built-in JavaScript features (e.g., `Date`).

**Special JS feature or syntax**

The test cases use ES6 syntax, specifically:

* The spread operator (`...`) in the `Array spread` approach.
* Template literals (`\r\n...\r\n`) for formatting the code.

**Other considerations**

When choosing between these two approaches, consider the trade-offs between readability and performance. If you prioritize conciseness and modernity, the `Array spread` approach might be a better choice. However, if you prefer to explicitly state the intention of creating an array with a specific length, `Array.apply()` might be a more readable option.

As for alternatives, other approaches could include:

* Using `Array.from()`, which creates a new array from an iterable.
* Utilizing `reduce()` or another array method to create the desired array structure.
* Leveraging libraries like Lodash or Underscore.js, which provide utility functions for creating arrays.

Keep in mind that the performance differences between these approaches can be significant, especially when working with large datasets.

Related benchmarks:

Array Apply vs Array Spread (version: 0)

Comparing performance of: Array Spread vs Array Apply

Created: 2 years ago by: Guest

Jump to the latest result

Array Spread

Array Apply

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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