Benchmark: Lodash loop vs foreach vs map

Script Preparation code:

   var arr = [];
    for (var i = 0; i < 1000; i++) {
      arr[i] = i;
    }
  
    function someFn(i) {
      return i * 3 * 8;
    }

Tests:

foreach
_.forEach(function (item){ someFn(item); })
for
for (var i = 0, len = arr.length; i < len; i++) { someFn(arr[i]); }
map
_.map(item => someFn(item))

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: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36

Browser/OS: Chrome 92 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
foreach	1702653.1 Ops/sec
for	5000.9 Ops/sec
map	1349213.4 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its test cases.

**Benchmark Definition JSON**

The benchmark definition is a simple JavaScript function that prepares an array of 1000 elements, each containing a multiplier function `someFn`. The purpose of this benchmark is to compare the performance of three different approaches:

1. Using `_.forEach()` (Lodash's `forEach` method)
2. Using a traditional `for` loop
3. Using `_.map()` (Lodash's `map` method)

**Test Cases**

There are three test cases, each representing one of the above-mentioned approaches.

* **foreach**: Uses `_.forEach()` to iterate over the array and call the `someFn` function on each element.
* **for**: Uses a traditional `for` loop to iterate over the array and call the `someFn` function on each element.
* **map**: Uses `_.map()` to create a new array with transformed elements, applying the `someFn` function on each element.

**Options Compared**

The benchmark compares three different approaches:

1. **Lodash's `_forEach()` method**: This approach uses Lodash's `forEach` method to iterate over the array and call the `someFn` function on each element.
2. **Traditional `for` loop**: This approach uses a traditional `for` loop to iterate over the array and call the `someFn` function on each element.
3. **Lodash's `_map()` method**: This approach uses Lodash's `map` method to create a new array with transformed elements, applying the `someFn` function on each element.

**Pros and Cons**

Here are some pros and cons for each approach:

* **_.forEach()**: Pros:
	+ Simple and easy to read
	+ No need to declare an index variable
*	Cons:
		- Can be slower than traditional loops in certain cases
* **Traditional `for` loop**: Pros:
	+ Faster than Lodash's `_forEach()` method
	+ More control over iteration
*	Cons:
		- Requires declaring an index variable
		- May not be as readable as other approaches
* **_.map()**: Pros:
	+ Creates a new array with transformed elements
	+ Can be faster than traditional loops for large datasets
*	Cons:
		- Creates a new array, which may not be desirable in all cases
		- Can be slower than traditional loops for small datasets

**Library: Lodash**

Lodash is a popular JavaScript library that provides a collection of functional programming helpers. In this benchmark, Lodash's `forEach` and `map` methods are used to iterate over the array and transform elements.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax used in this benchmark, such as ES6 modules, async/await, or Promises. The code is written in vanilla JavaScript.

**Other Alternatives**

If you wanted to compare these approaches using a different method, you could use:

* `Array.prototype.forEach()`: This is the same approach as Lodash's `_forEach()` method.
* `while` loop: This would require manually incrementing an index variable and checking the end of the array.
* `reduce()`: This would create a new value on each iteration, but may not be desirable for all use cases.

Note that the choice of alternative will depend on your specific use case and performance requirements.

Related benchmarks:

Lodash loop vs foreach vs map (version: 0)

Comparing performance of: foreach vs for vs map

Created: 4 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 one month ago):