Benchmark: forEach vs reduce vs map vs filter vs for (v2)

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

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:

No previous run results

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

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**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmark, where users can compare the performance of different approaches to iterate over an array of 12345 elements and calculate a complex expression involving some `someFn` function.

**Approaches Compared**

1. **forEach**: Iterates over the array using the `forEach` method, which calls the provided callback function for each element in the array.
2. **reduce**: Uses the `reduce` method to iterate over the array, applying a reduction function to each element and accumulating the result.
3. **map**: Applies the `map` method to create a new array with transformed elements, where each transformed element is calculated by calling the provided callback function for each original element in the array.
4. **filter**: Uses the `filter` method to create a new array containing only the elements that pass the provided test (in this case, a simple check of an expression).
5. **for** loop: Iterates over the array using a traditional `for` loop, accessing each element individually.

**Pros and Cons**

* **forEach**: Easy to use, but can be less efficient than other approaches since it creates additional function calls.
	+ Pros: Simple syntax, easy to read and write.
	+ Cons: May incur additional overhead due to function call overhead.
* **reduce**: More expressive and flexible than `forEach`, as it allows for cumulative calculations.
	+ Pros: Can perform complex calculations efficiently, good for accumulating values.
	+ Cons: Steeper learning curve, may be slower for simple cases.
* **map**: Creates a new array with transformed elements, which can be beneficial if the resulting array is needed.
	+ Pros: Creates new array with transformed elements, potentially useful in certain scenarios.
	+ Cons: Requires additional memory allocation and copying of data.
* **filter**: Efficiently filters out unwanted elements, but may require multiple iterations.
	+ Pros: Fast filtering performance, suitable for large datasets.
	+ Cons: May not be as easy to read or understand due to the complexity of the test function.

**Library Usage**

In this benchmark, no external libraries are explicitly mentioned. However, it is worth noting that some libraries (e.g., Lodash) can simplify certain operations like `reduce` and `map`.

**Special JavaScript Features/Syntax**

This benchmark does not exploit any special JavaScript features or syntax beyond standard ECMAScript 2015+ syntax.

**Alternatives**

If you were to reimplement this benchmark, consider the following alternatives:

* **iterators**: Instead of using methods like `forEach`, `reduce`, and `map`, use iterators (e.g., `for...of`) for a more low-level, iterative approach.
* **native loops**: Use native C++/Java-style loops instead of JavaScript loops to gain better performance optimization potential.
* **multi-threading**: Consider parallelizing the execution using multiple threads or processes to explore multi-threading performance advantages.

Related benchmarks:

forEach vs reduce vs map vs filter vs for (v2) (version: 0)

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

Created: 4 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):