Benchmark: compare the ways to generate a random array for iteration

compare the ways to generate a random array for iteration (version: 0)

- Array.apply: Array.apply(null, Array(100)) - Destructuring operator: [...Array(100)] - Array.prototype.fill Array(100).fill(undefined) - Array.from Array.from({ length: 100 })

Comparing performance of: Array.apply(null, Array(n)) vs [...Array(n)] vs Array(n).fill(null) vs Array.from({ length: n }) vs Array.from({ length:n }, (item, i) => i)

Created: 10 months ago by: Guest

Jump to the latest result

Tests:

Array.apply(null, Array(n))
Array.apply(null, Array(1000)).map(() => Math.random());
[...Array(n)]
[...Array(1000)].map(() => Math.random());
Array(n).fill(null)
Array(1000).fill(null).map(() => Math.random());
Array.from({ length: n })
Array.from({ length: 1000 }).map(() => Math.random());
Array.from({ length:n }, (item, i) => i)
Array.from({ length:1000 }, () => Math.random());

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.apply(null, Array(n))
[...Array(n)]
Array(n).fill(null)
Array.from({ length: n })
Array.from({ length:n }, (item, i) => i)

Fastest: N/A

Slowest: N/A

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/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Windows

View result in a separate tab

Test name	Executions per second
Array.apply(null, Array(n))	10262.1 Ops/sec
[...Array(n)]	10559.2 Ops/sec
Array(n).fill(null)	10088.3 Ops/sec
Array.from({ length: n })	7899.3 Ops/sec
Array.from({ length:n }, (item, i) => i)	7284.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Definition:**

The provided JSON represents a benchmarking test case created on MeasureThat.net. The purpose of this benchmark is to compare different ways of generating a random array for iteration. The test includes four approaches:

1.  **Array.apply**: This method uses `Array.apply(null, Array(100))` to create an array and then maps over it using the callback function.
2.  **Destructuring operator**: This approach uses the spread operator (`...`) to create an array from `Array(100)`.
3.  **Array.prototype.fill**: This method creates an array of a specified length (in this case, 1000) and fills it with `null`, then maps over the resulting array.
4.  **Array.from**: This method is used to create an array from an object with a specified length.

**Options Compared:**

The benchmark compares these four approaches:

*   Array.apply
*   Destructuring operator
*   Array.prototype.fill
*   Array.from

Each approach has its own strengths and weaknesses, which we'll discuss below.

**Pros and Cons of Each Approach:**

1.  **Array.apply**: This method is generally considered old-fashioned and not recommended for new code. However, it can be useful in certain situations where you need to support older browsers or environments that don't support modern JavaScript features.
    *   Pros:
        *   Works across a wide range of browsers and environments
    *   Cons:
        *   Not recommended for new code due to its outdated nature
2.  **Destructuring operator**: This approach is concise and easy to read, making it a popular choice among modern JavaScript developers.
    *   Pros:
        *   Concise and readable syntax
        *   Works well with most modern browsers and environments
    *   Cons:
        *   May not work in older browsers or environments that don't support the spread operator
3.  **Array.prototype.fill**: This method is useful when you need to fill an array with a specific value, but it's not ideal for iterating over the array.
    *   Pros:
        *   Fast and efficient for filling arrays with values
    *   Cons:
        *   Not suitable for iteration due to its implementation
4.  **Array.from**: This method is a modern addition to the JavaScript language and provides an elegant way to create arrays from objects.
    *   Pros:
        *   Concise and readable syntax
        *   Works well with most modern browsers and environments
    *   Cons:
        *   May not work in older browsers or environments that don't support the spread operator

**Library Usage:**

There is no explicit library usage mentioned in the benchmark definition. However, some methods like `Array.prototype.fill` might use internal browser libraries to implement their behavior.

**Special JS Features/Syntax:**

The benchmark uses several modern JavaScript features:

*   Destructuring operator (`...`)
*   Spread syntax (`Array.from({ length: n })`)

These features are widely supported across most modern browsers and environments, but it's essential to ensure compatibility when using them in production code.

**Alternatives:**

If you're looking for alternative ways to generate random arrays for iteration, you might consider the following approaches:

*   **Random number generator**: You can use a high-quality random number generator library like `crypto` or `randomseed` to generate random numbers.
*   **Array.from with a custom mapping function**: Instead of using an array comprehension, you can use `Array.from` with a custom mapping function to create the array.

However, keep in mind that these alternatives might not be as efficient or readable as the methods presented in the benchmark.

Related benchmarks:

compare the ways to generate a random array for iteration (version: 0)

- Array.apply: Array.apply(null, Array(100)) - Destructuring operator: [...Array(100)] - Array.prototype.fill Array(100).fill(undefined) - Array.from Array.from({ length: 100 })

Comparing performance of: Array.apply(null, Array(n)) vs [...Array(n)] vs Array(n).fill(null) vs Array.from({ length: n }) vs Array.from({ length:n }, (item, i) => i)

Created: 10 months ago by: Guest

Jump to the latest result

Array.apply(null, Array(n))

[...Array(n)]

Array(n).fill(null)

Array.from({ length: n })

Array.from({ length:n }, (item, i) => i)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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