Benchmark: Array loop vs foreach vs map fixed by bomi

Script Preparation code:

   var arr = [];
    for (var i = 0; i < 1000; i++) {
      arr[i] = i;
    }

Tests:

foreach
arr.forEach(function (item){ let result = item * 3 * 8 })
for
for (let i = 0; i < arr.length; i++) { let result = arr[i] * 3 * 8 }
map
arr.map(item => item * 3 * 8)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
foreach
for
map

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/100.0.4896.127 Safari/537.36

Browser/OS: Chrome 100 on Windows

View result in a separate tab

Test name	Executions per second
foreach	1142165.1 Ops/sec
for	6192.5 Ops/sec
map	292019.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is designed to compare the performance of three different approaches: `forEach`, `for` loops, and `map`. The tests are intended to measure the speed of each approach, rather than slowing them down intentionally.

**Options Compared**

In this benchmark, we have:

1. `forEach`: A method that executes a provided function for each element in an array.
2. `for` loop: A traditional loop that iterates over an array using a counter variable (`i`).
3. `map`: A method that creates a new array with the results of applying a provided function to each element in the original array.

**Pros and Cons**

Here's a brief overview of each approach:

1. `forEach`:
	* Pros: Easy to use, concise code, doesn't require explicit indexing.
	* Cons: May have performance overhead due to method call and iteration.
2. `for` loop:
	* Pros: Fastest execution, no method call or iteration overhead.
	* Cons: Requires more boilerplate code and can be verbose.
3. `map`:
	* Pros: Concise and expressive code, can be faster than `forEach` for large datasets.
	* Cons: May have performance overhead due to method call and creation of a new array.

**Other Considerations**

The benchmark aims to isolate the performance differences between these approaches by:

1. Using a fixed-size array (`arr`) with 1000 elements.
2. Applying the same mathematical operation (`item * 3 * 8`) to each element, ensuring that the results are consistent and don't affect the performance comparison.

**Test Cases**

The individual test cases are similar, but with slight variations:

1. `forEach`: Uses a function expression to perform the calculation on each item.
2. `for` loop: Uses a traditional `for` loop with an index variable (`i`) to access each element in the array.
3. `map`: Uses an arrow function to apply the same mathematical operation to each item.

**Library and Special JS Features**

In this benchmark, no libraries or special JavaScript features are used beyond what's standard in modern browsers. However, it's worth noting that some older browsers might not support certain features like `map` or arrow functions.

**Alternatives**

Other alternatives for iterating over arrays in JavaScript include:

1. `reduce`: A method that reduces an array to a single value by applying a provided function to each element.
2. Spreads (`...`): Can be used to iterate over an array, but is not as efficient as traditional loops or methods like `forEach`, `map`, and `for`.
3. `while` loop: Another traditional looping construct that can be used to iterate over an array.

Keep in mind that the choice of iteration method often depends on the specific requirements and constraints of a particular project.

Related benchmarks:

Array loop vs foreach vs map fixed by bomi (version: 0)

Makes the benchmark actually measure the speed of for/foreach/map instead of slowing them down.

Comparing performance of: foreach vs for vs map

Created: 2 years ago by: Guest

Jump to the latest result

foreach

for

map

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):