Benchmark: filter + map vs reduce 123

Script Preparation code:

var values = []
for(i=0; i<10000; i++){ values.push(1) }

Tests:

filter + map
values.filter(x => x % 2 === 0).map((x) => x * 2)
reduce
values.reduce((acc, x) => { if (x % 2 === 0) { acc.push(x * 2); } return acc; }, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + map
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36

Browser/OS: Chrome 101 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter + map	37942.6 Ops/sec
reduce	31086.6 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**What is being tested?**

The provided JSON represents a benchmark test that compares the performance of two approaches to process an array of numbers: filtering and mapping, versus reducing.

In the script preparation code, 10,000 elements are created in an array called `values`. The goal is to filter out even numbers from this array, perform some operation on the remaining odd numbers (in this case, doubling them), and then map the resulting values to a new array.

**Options compared:**

Two options are being compared:

1. **Filtering and Mapping (`filter + map`)**: This approach uses `Array.prototype.filter()` to remove even numbers from the `values` array, followed by `Array.prototype.map()` to create a new array with doubled odd numbers.
2. **Reducing (`reduce`)**: Instead of filtering and mapping, this approach uses `Array.prototype.reduce()` to accumulate the doubled odd numbers in an accumulator array.

**Pros and Cons of each approach:**

1. **Filtering and Mapping (`filter + map`)**:
	* Pros:
		+ More readable and maintainable code.
		+ Separation of concerns (filtering and mapping can be done independently).
	* Cons:
		+ May involve more function calls, leading to overhead due to stack size limits.
2. **Reducing (`reduce`)**:
	* Pros:
		+ Can be more efficient in terms of memory usage, as it only accumulates the necessary data.
	* Cons:
		+ More complex code can lead to harder debugging and maintenance.

**Library used:**

None are explicitly mentioned in the provided JSON. However, `Array.prototype.filter()`, `Array.prototype.map()`, and `Array.prototype.reduce()` are standard methods of the JavaScript Array prototype.

**Special JS feature or syntax:**

The `const x = 1` and `let i = 0` declarations are implicit (using var instead) but it is not explicitly used in benchmark definition. The arrow function (`x => x * 2`) is a modern JavaScript feature that allows for concise syntax.

**Other alternatives:**

There might be other approaches to solve this problem, such as:

1. Using `Array.prototype.forEach()` and creating a new array with doubled odd numbers.
2. Using a custom iterative loop instead of the built-in methods.
3. Using a library like Lodash or Ramda for more functional programming-style solutions.

However, these alternatives are not explicitly mentioned in the provided JSON, and it's likely that the focus is on comparing the performance of `filter + map` versus `reduce`.

Related benchmarks:

filter + map vs reduce 123 (version: 0)

Comparing performance of: filter + map vs reduce

Created: 2 years ago by: Guest

Jump to the latest result

filter + map

reduce

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):