Benchmark: filter + forEach vs forEach + guard

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 sumFilterForEach = 0, sumForEachGuard = 0;

Tests:

filter + forEach
arr.filter(n => n % 2 == 0).forEach(n => sumForEachGuard += someFn(n));
forEach + guard
arr.forEach(n => { if (n % 2 != 0) return; sumForEachGuard += someFn(n); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + forEach
forEach + guard

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36

Browser/OS: Chrome 111 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter + forEach	743.9 Ops/sec
forEach + guard	815.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is testing two different approaches to perform an operation on an array: `arr.filter()` with subsequent `forEach()`, and `forEach()` with a guard clause (`if` statement) that skips certain iterations.

**Options Compared**

Two options are compared:

1. **filter + forEach**: This approach filters the array first, removing all elements that don't meet a certain condition (in this case, even numbers). Then, it iterates over the filtered array using `forEach()`, applying the function `someFn()` to each element and accumulating the results.
2. **forEach + guard**: This approach directly iterates over the original array using `forEach()`, but with an added `if` statement that skips iterations if a certain condition isn't met (in this case, odd numbers). The function `someFn()` is still applied to each element in the iteration.

**Pros and Cons**

Here are some pros and cons of each approach:

* **filter + forEach**
	+ Pros:
		- More efficient filtering out irrelevant elements.
		- Fewer iterations over the array, which can lead to faster execution times.
	+ Cons:
		- Creates an intermediate filtered array, which may consume extra memory.
		- May require more computational resources to create and manipulate the filtered array.
* **forEach + guard**
	+ Pros:
		- Avoids creating an intermediate array, which can reduce memory usage.
		- Can be more efficient for small arrays or cases where filtering is not necessary.
	+ Cons:
		- Iterates over the entire original array, even if some elements don't meet the condition.
		- May lead to slower execution times due to unnecessary iterations.

**Library and Special JS Feature**

There is no explicit library mentioned in the benchmark definition. However, `forEach()` is a built-in JavaScript method that iterates over an array or other iterable object.

**Test Case Considerations**

The test cases use the same array `arr` with 12,345 elements, but only iterate over a portion of it using `someFn()`. This allows for a controlled comparison between the two approaches without affecting performance.

**Other Alternatives**

Some alternative approaches to consider:

* Using `map()` instead of `filter()` and `forEach()`, which would create a new array with transformed values.
* Using `reduce()` instead of `forEach()` to accumulate results, which could be more efficient for large arrays.
* Applying optimization techniques like caching or memoization to the `someFn()` function.

Keep in mind that these alternatives might not directly compare to the original benchmark, as they may have different performance characteristics or requirements.

Related benchmarks:

filter + forEach vs forEach + guard (version: 0)

Comparing performance of: filter + forEach vs forEach + guard

Created: one year ago by: Guest

Jump to the latest result

filter + forEach

forEach + guard

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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