Benchmark: Array.reduce vs for loop vs Array.forEach v2

Script Preparation code:

// Create an array of 1000 random intergers between 1 and 10000
var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(10000)));
}

function doRedeuce(pArray) {
	return pArray.reduce((accum, curr) => accum + curr);
}

function doLoop(pArray) {
	let accum = 0;
	
  	for(let intCtr=0; intCtr < pArray.length; intCtr++) {
		accum += pArray[intCtr];
	}
	
  	return accum;
}

function doForEach(pArray) {
	let accum = 0;

  	pArray.forEach(item => {
		accum += item;
	});
  
  return accum;
}

Tests:

reduce
var redeuceResult=0; redeuceResult = doRedeuce(arrRandom);
for loop
var loopResult=0; loopResult = doLoop(arrRandom);
forEach
var forEachResult=0 forEachResult = doForEach(arrRandom)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for loop
forEach

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what is tested on MeasureThat.net.

**Benchmark Definition**

The benchmark is designed to compare the performance of three different approaches for summing an array of random integers:

1. `Array.reduce`
2. For loop
3. `Array.forEach`

The script preparation code creates an array of 1000 random integers between 1 and 10,000, and defines three functions: `doRedeuce`, `doLoop`, and `doForEach`. These functions perform the summing operations using the respective approaches.

**Options Compared**

The benchmark compares the performance of each approach:

* `Array.reduce`: uses the built-in `reduce` method to sum the array elements.
* For loop: uses a traditional for loop to iterate through the array elements and add them up.
* `Array.forEach`: uses the `forEach` method to iterate through the array elements, but in this case, it's modified to also perform the summing operation.

**Pros and Cons of Each Approach**

1. **`Array.reduce`**:
	* Pros: concise, efficient, and easy to read.
	* Cons: may not be as intuitive for some developers, especially those without experience with functional programming.
2. **For loop**:
	* Pros: straightforward, easy to understand, and suitable for complex logic.
	* Cons: can be slower due to the overhead of explicit looping and conditional statements.
3. **`Array.forEach`**:
	* Pros: concise, efficient, and suitable for simple transformations or operations.
	* Cons: may not be as intuitive for summing operations, especially without modification.

**Library Used**

None is explicitly mentioned in the provided code, but `Math.floor` is used to generate random numbers. However, there are no libraries that are imported or referenced in the script preparation code.

**Special JS Feature/Syntax**

No special JavaScript features or syntaxes are used in this benchmark. The focus is on comparing different approaches for summing an array of integers.

**Other Alternatives**

To measure the performance of these approaches, you could also consider:

* Using a similar benchmarking framework like Benchmark.js or js-benchmark
* Adding more test cases to cover different scenarios, such as sorting or searching algorithms
* Comparing the performance with other languages or frameworks that implement similar methods (e.g., Python's built-in `sum` function)

Keep in mind that MeasureThat.net is a specialized platform designed specifically for JavaScript benchmarking. If you're interested in comparing the performance of these approaches in a different context, you may want to consider alternative tools and frameworks.

Related benchmarks:

Array.reduce vs for loop vs Array.forEach v2 (version: 0)

A test summing 1000 random numbers, 1 - 10000

Comparing performance of: reduce vs for loop vs forEach

Created: 7 years ago by: Registered User

Jump to the latest result

reduce

for loop

forEach

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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