Benchmark: filter vs reduce Birynek

Script Preparation code:

a=[];
for(i=0;i<1000;i++) a.push(Number(i)/1000);
var filtering=x=>(x % 2) === 0;

Tests:

filter
sumFilter = a.filter(aNumber=> filtering(aNumber)).length
reduce
var sumReduce = 0; a.reduce((sumReduce, aNumber) => filtering(aNumber) ? sumReduce + 1 : sumReduce,0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
reduce

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and its results.

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches: filtering and reducing an array in JavaScript. The script preparation code generates an array `a` with 1000 elements, where each element is a number divided by 1000. A function `filtering(x)` checks if the number is even (i.e., `x % 2 === 0`). Two test cases are created:

1. **Filter**: Uses the `filter()` method to create a new array with only the even numbers from the original array.
2. **Reduce**: Uses the `reduce()` method to accumulate a sum of 1 for each even number in the original array.

**Options Compared**

The benchmark compares two options:

1. **Filtering (Array.prototype.filter())**: This approach creates a new array with the filtered elements and returns its length.
2. **Reducing (Array.prototype.reduce())**: This approach accumulates a sum of 1 for each even number in the original array and returns the final sum.

**Pros and Cons**

* **Filtering (Array.prototype.filter())**:
	+ Pros:
		- Easy to understand and implement.
		- Returns a new array with the filtered elements, which can be useful for further processing.
	+ Cons:
		- Creates a new array, which can lead to increased memory usage.
		- May not be as efficient as reducing, especially for large arrays.
* **Reducing (Array.prototype.reduce())**:
	+ Pros:
		- Accumulates the sum in place, reducing memory usage.
		- Can be more efficient than filtering for large arrays.
	+ Cons:
		- Requires a callback function to handle each element, which can be complex to understand and implement.

**Library Usage**

In this benchmark, no libraries are used. The `filter()` and `reduce()` methods are part of the native JavaScript API.

**Special JS Features or Syntax**

The benchmark uses arrow functions (`aNumber => filtering(aNumber)`) for both filtering and reducing. Arrow functions provide a concise way to define small, anonymous functions.

**Latest Benchmark Results**

According to the latest results, Chrome 119 on a Mac OS X 10.15.7 desktop executes the `reduce()` test case approximately 13908 times per second, while the `filter()` test case executes approximately 13688 times per second.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

1. **Using `Array.prototype.forEach()`**: Instead of using `filter()` or `reduce()`, you could use `forEach()` to iterate over the array and perform some operation on each element.
2. **Using a custom loop**: You could write a custom loop to iterate over the array and perform some operation on each element, avoiding the use of built-in methods like `filter()` or `reduce()`.
3. **Using parallel processing**: If you have access to modern JavaScript engines that support parallel processing (e.g., Web Workers), you could divide the array into smaller chunks and process them concurrently.

These alternatives may offer different trade-offs in terms of performance, memory usage, and code complexity. However, they might not be as efficient or intuitive as using native methods like `filter()` or `reduce()`.

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/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter	13688.4 Ops/sec
reduce	13908.0 Ops/sec

Related benchmarks:

filter vs reduce Birynek (version: 0)

Comparing performance of: filter vs reduce

Created: one year ago by: Guest

Jump to the latest result

filter

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):