Benchmark: Array.reduce vs for loop vs Array.forEach experiments - Fix Foreach as function

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, start) {
	return pArray.reduce(function(accum, curr) { return accum + curr; }, start);
}
function doRedeuceArrowFunc(pArray, start) {
	return pArray.reduce((accum, curr) => accum + curr, start);
}
function doLoop(pArray) {
	var accum = 0;
	for(var intCtr=0; intCtr<pArray.length; intCtr++) {
		accum += pArray[intCtr];
	}
	return accum;
}
function doForEach(pArray) {
	var accum = 0;
	pArray.forEach(function(item){
		accum += item;
	});
}

Tests:

reduce (arrow function)
var reduceResult=0; redeuceResult = doRedeuceArrowFunc(arrRandom, 0);
for loop
var loopResult=0; loopResult = doLoop(arrRandom);
forEach
var forEachResult=0 forEachResult = doForEach(arrRandom)
reduce
var reduceResult=0; redeuceResult = doRedeuce(arrRandom, 0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce (arrow function)
for loop
forEach
reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**Benchmark Definition**

The provided JSON represents a benchmark that compares three different approaches to summing an array of 1000 random integers: `Array.prototype.reduce`, `for` loop, and `Array.prototype.forEach`.

**Options Compared**

1. **Array.prototype.reduce**: This method uses the reduce() function to iterate over the array and accumulate the values. There are two versions:
	* `doRedeuce(pArray, start)`: A traditional reducer function that takes an accumulator and a current value as arguments.
	* `doRedeuceArrowFunc(pArray, start)`: An arrow function-based reducer that uses the same logic as the traditional version but with a more concise syntax.
2. **for loop**: This approach uses a traditional `for` loop to iterate over the array and accumulate the values.
3. **Array.prototype.forEach**: This method iterates over the array using a callback function, but it does not provide an accumulation mechanism.

**Pros and Cons**

1. **Array.prototype.reduce**:
	* Pros:
		+ Concise syntax with arrow functions ( `doRedeuceArrowFunc` )
		+ Efficient execution, as it avoids unnecessary iterations
	* Cons:
		+ May be less intuitive for developers without experience with reduce()
2. **for loop**:
	* Pros:
		+ Well-known and widely supported
		+ Easy to implement and understand
	* Cons:
		+ May lead to performance issues if not implemented correctly (e.g., using `i < array.length` instead of `i >= 0`)
3. **Array.prototype.forEach**:
	* Pros:
		+ Easy to implement and understand
	* Cons:
		+ Does not provide an accumulation mechanism, making it less suitable for this benchmark

**Library: Array.prototype.reduce()**

The `reduce()` method is a built-in JavaScript method that applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single value. It's commonly used for tasks like summing arrays or aggregating data.

**Special JS feature: Arrow functions**

Arrow functions (`() => { }`) are a concise syntax introduced in ECMAScript 2015. They allow defining small, one-line functions without the `function` keyword. In this benchmark, arrow functions are used to implement the `doRedeuceArrowFunc` reducer function.

**Other Considerations**

* The benchmark assumes that the input array is not empty and contains at least one element.
* The use of `Math.random()` generates random integers for the array, which may affect performance due to the overhead of generating random numbers.
* The benchmark runs on a Chrome 69 browser with a Windows Desktop platform.

**Alternatives**

If you're looking for alternative approaches or libraries, consider:

1. Lodash: A popular utility library that includes functions like `reduce()` and others.
2. Ramda: A functional programming library that provides a `reduce()` function and other useful tools.
3. Manual iteration using `for` loops or `while` loops.

Keep in mind that the performance differences between these approaches may be small, depending on the specific use case and input data.

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36

Browser/OS: Chrome 69 on Windows

View result in a separate tab

Test name	Executions per second
reduce (arrow function)	514073.5 Ops/sec
for loop	795703.6 Ops/sec
forEach	488203.6 Ops/sec
reduce	592101.8 Ops/sec

Related benchmarks:

Array.reduce vs for loop vs Array.forEach experiments - Fix Foreach as function (version: 0)

A test summing 1000 random numbers, 1 - 10000

Comparing performance of: reduce (arrow function) vs for loop vs forEach vs reduce

Created: 6 years ago by: Guest

Jump to the latest result

reduce (arrow function)

for loop

forEach

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):