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

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 reduceCallback(accum, curr) {
	return accum+curr;
}

function doRedeuce(pArray) {
	return pArray.reduce(reduceCallback);
}
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;
	});
}

function baseEach(collection, iteratee) {
  if (collection == null) {
    return collection
  }
  if (!isArrayLike(collection)) {
    return baseForOwn(collection, iteratee)
  }
  const length = collection.length
  const iterable = Object(collection)
  let index = -1

  while (++index < length) {
    if (iteratee(iterable[index], index, iterable) === false) {
      break
    }
  }
  return collection
}
function lodashReduce(collection, iteratee, accumulator) {
  const func = Array.isArray(collection) ? arrayReduce : baseReduce
  const initAccum = arguments.length < 3
  return func(collection, iteratee, accumulator, initAccum, baseEach)
}
function arrayReduce(array, iteratee, accumulator, initAccum) {
  let index = -1
  const length = array == null ? 0 : array.length

  if (initAccum && length) {
    accumulator = array[++index]
  }
  while (++index < length) {
    accumulator = iteratee(accumulator, array[index], index, array)
  }
  return accumulator
}
function doLodashReduce (pArray) {
  return lodashReduce(pArray, reduceCallback)
}

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for loop
forEach
lodash.reduce

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/90.0.4430.85 Safari/537.36

Browser/OS: Chrome 90 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	60430.7 Ops/sec
for loop	1102021.4 Ops/sec
forEach	419619.9 Ops/sec
lodash.reduce	407492.8 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 benchmark and its various components.

**Benchmark Overview**

The test aims to compare the performance of four different methods for summing up an array of random numbers:

1. `Array.reduce`
2. For loop
3. `Array.forEach`
4. Lodash's `reduce` function (using the `lodash.reduce` method)

**Method Comparisons**

Here's a brief overview of each method and their pros/cons:

### 1. Array.reduce

* **Description**: Uses the reduce() method to iterate over the array, accumulating the sum.
* **Pros**:
	+ Efficient for summing up arrays with a small number of elements.
	+ Can be used for more complex operations by passing custom callback functions.
* **Cons**:
	+ May have performance issues when dealing with very large arrays due to its iterative nature.

### 2. For Loop

* **Description**: Uses a traditional for loop to iterate over the array, accumulating the sum.
* **Pros**:
	+ Can be more intuitive for developers familiar with traditional loops.
	+ Can handle very large arrays efficiently since it avoids array iteration.
* **Cons**:
	+ May have a higher overhead due to the need to manage index variables.

### 3. Array.forEach

* **Description**: Uses the forEach() method to iterate over the array, accumulating the sum.
* **Pros**:
	+ Similar to Array.reduce but with a more concise syntax.
	+ Can be used for more complex operations by passing custom callback functions.
* **Cons**:
	+ May have performance issues when dealing with very large arrays due to its iterative nature.

### 4. Lodash's reduce

* **Description**: Uses the `lodash.reduce` method to iterate over the array, accumulating the sum.
* **Pros**:
	+ Efficient and well-tested implementation of the reduce algorithm.
	+ Can handle complex operations by passing custom callback functions.
* **Cons**:
	+ Requires Lodash library inclusion, which may add unnecessary overhead.

**Other Considerations**

When evaluating these methods, consider the following factors:

* **Array size**: For very large arrays, methods with lower overhead (e.g., for loop) might be more efficient.
* **Number of operations**: If you need to perform multiple reductions or complex operations, using a custom callback function with Array.reduce or Lodash's reduce might be beneficial.

**Individual Test Cases**

Each test case measures the performance of one specific method:

1. `reduce`
2. For loop
3. `forEach`
4. Lodash's `reduce`

These individual tests provide insights into the performance of each method in isolation, making it easier to understand which approach is most suitable for your specific use cases.

**Alternative Methods**

If you need alternative methods for summing up arrays:

* **Using Array.prototype.map()**: You can calculate the square of each element and then sum them using `Array.prototype.reduce()` or a custom callback function.
* **Using a library like MathJS**: MathJS provides an efficient and optimized implementation of mathematical functions, including array operations.

These alternatives might offer better performance or features for specific use cases.

Related benchmarks:

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

A test summing 1000 random numbers, 1 - 10000

Comparing performance of: reduce vs for loop vs forEach vs lodash.reduce

Created: 4 years ago by: Guest

Jump to the latest result

reduce

for loop

forEach

lodash.reduce

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):