Benchmark: forEach vs filter vs for

Script Preparation code:

var arr = [];
for (var i = 0; i < 12345; i++) {
  arr[i] = i;
}

function someFn(i) {
  return (i * 3 * 8 / 1200 * 0.002 / 40 * 0.2);
}

var sumForEach = 0,
  sumReduce = 0,
  sumMap = 0,
  sumFilter = 0,
  sumFor = 0;

Tests:

forEach
arr.forEach(item => sumForEach += someFn(item));
reduce
sumReduce = arr.reduce((lastValue, item) => { return sumReduce += someFn(item); });
map
arr.map(item => (sumMap += someFn(item)));
filter
arr.filter(item => (sumFilter += someFn(item)));
for
for (var j = 0; j < arr.length; j++) { sumFor += arr[j]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
forEach
reduce
map
filter
for

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

The provided benchmark is designed to compare the performance of four different approaches for calculating the sum of an array: `forEach`, `reduce`, `map`, and `for`. Each approach is implemented in a separate test case, which will be explained below.

**Script Preparation Code**

Before diving into the individual test cases, let's examine the script preparation code:
```javascript
var arr = [];
for (var i = 0; i < 12345; i++) {
  arr[i] = i;
}

function someFn(i) {
  return (i * 3 * 8 / 1200 * 0.002 / 40 * 0.2);
}
```
This code creates an array `arr` with 12345 elements, each initialized to its index value. The function `someFn` is defined, which calculates a specific value based on the input `i`.

**Individual Test Cases**

Now, let's explore each test case:

1. **forEach**: `arr.forEach(item => sumForEach += someFn(item));`
This implementation uses the `forEach` method to iterate over the array and accumulate the sum in the variable `sumForEach`. The `forEach` callback function is called for each element, passing the current element as an argument.
2. **reduce**: `sumReduce = arr.reduce((lastValue, item) => {\r\n  return sumReduce += someFn(item);\r\n});`
This implementation uses the `reduce` method to iterate over the array and accumulate the sum in the variable `sumReduce`. The `reduce` callback function is called for each element, passing the current element as an argument.
3. **map**: `arr.map(item => (sumMap += someFn(item)));`
This implementation uses the `map` method to create a new array with the results of applying the `someFn` function to each element. The sum is accumulated in the variable `sumMap`.
4. **for**: `for (var j = 0; j < arr.length; j++) {\r\n  sumFor += arr[j];\r\n}`
This implementation uses a traditional `for` loop to iterate over the array and accumulate the sum in the variable `sumFor`.

**Pros and Cons of Each Approach**

Here's a brief analysis of each approach:

* **forEach**: Pros: Simple, intuitive. Cons: Can be slower due to additional overhead.
* **reduce**: Pros: Efficient for accumulating sums, handles null/undefined values. Cons: Requires explicit callback function definition.
* **map**: Pros: Creates new array with results, efficient for transformations. Cons: Creates an additional memory allocation.
* **for**: Pros: Control over iteration, no dependencies on built-in methods. Cons: More verbose, slower.

**Library and Special JS Features**

In this benchmark, none of the libraries or special JavaScript features are explicitly used beyond what is provided by the standard library. However, the use of `forEach`, `reduce`, `map`, and `for` loops does demonstrate understanding of these built-in methods.

**Other Alternatives**

If you were to reimplement this benchmark with alternative approaches, consider the following:

* **Use a more efficient summing function**: Instead of using the standard arithmetic operations for calculating the sum, look into using optimized functions like `Array.prototype.reduceRight()` or implementing a custom summing algorithm.
* **Use parallel processing**: If available, use parallel processing techniques to iterate over the array in multiple threads or processes, potentially improving performance.
* **Minimize memory allocation**: Consider using a streaming approach instead of creating an intermediate array with all elements precomputed.

By understanding the strengths and weaknesses of each approach, you can better optimize your own JavaScript code for optimal performance.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36

Browser/OS: Chrome 116 on Windows

View result in a separate tab

Test name	Executions per second
forEach	162.8 Ops/sec
reduce	169.7 Ops/sec
map	168.6 Ops/sec
filter	120.5 Ops/sec
for	67.9 Ops/sec

Related benchmarks:

forEach vs filter vs for (version: 0)

Comparing performance of: forEach vs reduce vs map vs filter vs for

Created: one year ago by: Guest

Jump to the latest result

forEach

reduce

map

filter

for

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):