Benchmark: forEach vs reduce vs map vs filter vs for3

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

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

Latest run results:

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

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Let's break down the provided JSON and explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark measures the performance of different JavaScript array methods: `forEach`, `reduce`, `map`, `filter`, and a custom `for` loop. The script preparation code creates an array with 12,345 elements and defines a function `someFn` that calculates a value based on the input index.

**Test Cases**

Each test case corresponds to one of the array methods:

1. **forEach**: Iterates over the array using the `forEach` method, adding the result of `someFn(item)` to the `sumForEach` variable.
2. **reduce**: Reduces the array by iterating over it and accumulating a sum using the `reduce` method with an initial value of 0.
3. **map**: Creates a new array by mapping each element in the original array, adding the result of `someFn(item)` to the `sumMap` variable.
4. **filter**: Filters the array by creating a new array that includes only elements for which `someFn(item)` is truthy, and adds the count to the `sumFilter` variable.
5. **for**: Uses a traditional `for` loop to iterate over the array, adding each element's value to the `sumFor` variable.

**Library Used**

None of these methods use any external libraries.

**Special JavaScript Features**

* None are explicitly mentioned in this benchmark.

Now, let's analyze the pros and cons of each approach:

1. **forEach**:
	* Pros: Easy to read and write, doesn't require explicit loop control.
	* Cons: Can be slower than other methods due to function call overhead.
2. **reduce**:
	* Pros: Efficient for accumulating sums or reducing arrays, can take advantage of optimized implementations in the browser.
	* Cons: Requires an initial value and a callback function, which may add complexity for some use cases.
3. **map**:
	* Pros: Creates a new array with transformed elements, useful for parallel processing or caching.
	* Cons: Can be slower than `forEach` due to creating a new array.
4. **filter**:
	* Pros: Efficiently filters out unwanted elements, can be combined with other methods (e.g., `map` and `reduce`).
	* Cons: May require additional memory allocation for the filtered array.

The custom `for` loop is generally slower than the built-in array methods due to its more explicit nature.

**Other Alternatives**

If you're looking for alternative approaches, consider:

1. **Array.prototype.every()**: Similar to `filter`, but returns a boolean value indicating whether all elements meet the condition.
2. **Array.prototype.some()**: Returns a boolean value indicating whether at least one element meets the condition.
3. **`for...of` loop**: A more modern and efficient alternative to traditional `for` loops, suitable for iterating over arrays.

These alternatives might offer better performance or readability in certain scenarios, but their usage depends on specific requirements and the trade-offs between simplicity, efficiency, and memory usage.

Related benchmarks:

forEach vs reduce vs map vs filter vs for3 (version: 0)

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

Created: 6 years ago by: Guest

Jump to the latest result

forEach

reduce

map

filter

for

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):