Benchmark: map vs forEach vs for loop

Script Preparation code:

var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(100)));
}
function doRedeuce(pArray) {
	return pArray.reduce(function(accum, curr) {return accum+curr});
}
function doLoop(pArray) {
	var accum = 0;
	for(var intCtr=0; intCtr<pArray.length; intCtr++) {
		accum += pArray[intCtr];
	}
	return accum;
}

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for loop

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 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 test that compares three approaches for summing up an array of random numbers:

1. `map()`
2. `forEach()`
3. A traditional `for` loop

The benchmark is defined in two parts:

* **Script Preparation Code**: This code generates an array of 1000 random numbers and defines two functions: `doRedeuce()` (which uses the `reduce()` method) and `doLoop()` (which uses a traditional `for` loop).
* **Html Preparation Code**: This is empty, which means that the benchmark test doesn't require any HTML modifications.

**Options Compared:**

The three options being compared are:

1. `map()`
2. `forEach()`
3. Traditional `for` loop

**Pros and Cons of Each Approach:**

1. **map():**
	* Pros:
		+ Shorter code
		+ Less memory allocation (since it doesn't create a new array)
	* Cons:
		+ May incur additional overhead due to the creation of an intermediate array (although this is usually negligible)
2. `forEach():`
	* Pros:
		+ Similar to `map()` in terms of code length and performance
	* Cons:
		+ Not as efficient as a traditional `for` loop, since it creates an event handler for each iteration
3. **Traditional `for` loop:**
	* Pros:
		+ Typically the fastest option, especially for large datasets
		+ Minimal overhead
	* Cons:
		+ Longest code

**Library Used:**

There is no library used in this benchmark test.

**Special JS Feature or Syntax:**

None of the options use any special JavaScript features or syntax.

**Other Alternatives:**

If you want to compare more approaches, some alternatives could be:

1. Using `Array.prototype.forEach()` instead of `forEach()`
2. Using a library like Lodash's `tail` function (which is similar to `reduce()`)
3. Using a different iteration method, such as using `Array.prototype.reduceRight()`

Note that the choice of alternative options depends on the specific use case and performance requirements.

**Benchmark Preparation Code:**

Here is the benchmark preparation code in JavaScript:
```javascript
var arrRandom = [];
for (var intCtr = 0; intCtr < 1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(100)));
}

function doRedeuce(pArray) {
  return pArray.reduce(function (accum, curr) {
    return accum + curr;
  });
}

function doLoop(pArray) {
  var accum = 0;
  for (var intCtr = 0; intCtr < pArray.length; intCtr++) {
    accum += pArray[intCtr];
  }
  return accum;
}
```
And here is the individual test cases:
```javascript
var redeuceResult = 0;
redeuceResult = doRedeuce(arrRandom);

var loopResult = 0;
loopResult = doLoop(arrRandom);
```

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36

Browser/OS: Chrome 84 on Mac OS X 10.15.5

View result in a separate tab

Test name	Executions per second
reduce	955924.8 Ops/sec
for loop	1080904.0 Ops/sec

Related benchmarks:

map vs forEach vs for loop (version: 0)

A test summing 1000 random numbers

Comparing performance of: reduce vs for loop

Created: 6 years ago by: Guest

Jump to the latest result

reduce

for loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):