Benchmark: Filter and Map vs Reduce

Script Preparation code:

var array = Array.from(Array(1000)).map((x, i) => ({ count: i, isOdd: Boolean(i % 2) }))

Tests:

Filter and Map
array.filter(x => x.isOdd).map(x => ({ stringCount: x.count.toString() }))
Reduce
array.reduce((acc, x) => x.isOdd ? [...acc, { stringCount: x.count.toString() }] : acc, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter and Map
Reduce

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

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

**Benchmark Definition:**
The benchmark is designed to compare two approaches for processing an array of objects:

1. `Filter` and `Map`: This approach filters out even numbers from the array using `array.filter(x => x.isOdd)` and then maps each remaining odd number object to a new object with a `stringCount` property using `map(x => ({ stringCount: x.count.toString() }))`.
2. `Reduce`: This approach reduces the array to an array of objects containing the count and whether it's odd, using `array.reduce((acc, x) => x.isOdd ? [...acc, { stringCount: x.count.toString() }] : acc, [])`.

**Options Compared:**
The benchmark is comparing two approaches:

* Filter-Map (top-level approach)
	+ Filter: removes even numbers from the array
	+ Map: transforms each odd number object to a new object with a `stringCount` property
* Reduce: accumulates an array of objects containing count and parity information

**Pros and Cons:**

* **Filter-Map:**
	+ Pros:
		- Easier to understand and implement, as it's a more linear process.
		- Can be more efficient for smaller arrays or when the filter condition is simple.
	+ Cons:
		- May require additional memory allocation to store temporary results.
		- Can be slower for larger arrays due to the overhead of function calls.
* **Reduce:**
	+ Pros:
		- More concise and expressive, as it combines multiple operations into a single loop.
		- Can be more efficient for larger arrays, as it avoids creating intermediate arrays.
	+ Cons:
		- May be harder to understand and implement, especially for developers without experience with reduce methods.
		- Can be slower due to the overhead of function calls and potential stack overflows.

**Library Usage:**
The benchmark uses no external libraries. The `Array.from()` method is used to create an array from a primitive value (in this case, `1000`), which is a built-in JavaScript feature.

**Special JS Features/Syntax:**
There are no special JS features or syntax used in this benchmark. It only relies on standard JavaScript language features and methods.

**Other Alternatives:**
If you're looking for alternative approaches to process an array of objects, consider the following:

* Using `forEach()` instead of `map()`: This can be useful when you don't need to create a new array, but rather perform an action on each element.
* Using `every()` or `some()` with a callback function: These methods can be used to check if all elements in the array satisfy a certain condition (for `every()`) or if at least one element satisfies a condition (for `some()`).
* Using a loop instead of a built-in method: While this may not be as concise, it can be useful when working with legacy code or specific requirements.

Keep in mind that the choice of approach depends on the specific use case and performance requirements.

Latest run results:

Run details: (Test run date: 11 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
Filter and Map	130103.6 Ops/sec
Reduce	10763.4 Ops/sec

Related benchmarks:

Filter and Map vs Reduce (version: 0)

Comparing performance of: Filter and Map vs Reduce

Created: one year ago by: Registered User

Jump to the latest result

Filter and Map

Reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):