Benchmark: abcdefg - MeasureThat.net

Script Preparation code:

var users = [
	{ 'user': 'joey',  'age': 32 },
 	{ 'user': 'ross',    'age': 41 },
  	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },
      	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },
    	{ 'user': 'chandler', 'age': 39 },

]

Tests:

a
const result = users.filter((item, index) => index !== 3);
b
const result = users.splice(3, 1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
a
b

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 what's being tested in this benchmark.

**What is being tested?**

MeasureThat.net is testing the performance of two different approaches for removing or replacing elements from an array in JavaScript:

1. `filter()`: This method creates a new array with all elements that pass the test implemented by the provided function.
2. `splice()`: This method modifies the original array and returns the removed element.

**Options being compared**

The benchmark is comparing the performance of these two approaches on a sample dataset of user objects, where each user object has an "age" property. The `filter()` approach will create a new array with all users except the one at index 3, while the `splice()` approach will remove the user at index 3 and return them.

**Pros and cons of each approach**

1. `filter()`:
	* Pros: Efficient for small to medium-sized datasets, easy to understand and implement.
	* Cons: Creates a new array, which can be memory-intensive for large datasets. Also, it can be slower than `splice()` due to the overhead of creating a new array.
2. `splice()`:
	* Pros: Modifies the original array in-place, which can be more efficient for large datasets. Also, it returns the removed element, which can be useful in some cases.
	* Cons: Slower for small to medium-sized datasets due to the overhead of modifying the original array.

**Other considerations**

When choosing between `filter()` and `splice()`, consider the following:

* If you need to process the removed element or modify the original array, `splice()` might be a better choice.
* If memory efficiency is crucial and you're working with large datasets, `filter()` might be a better option (although it creates a new array).
* If readability and simplicity are more important than raw performance, `filter()` might be a better choice.

**Library**

There is no external library being used in this benchmark. The `users` array is a simple JavaScript array created directly in the script preparation code.

**Special JS feature or syntax**

There are no special JS features or syntax being used in this benchmark. Both `filter()` and `splice()` are standard JavaScript methods that are widely supported.

**Alternatives**

If you're looking for alternative approaches to remove or replace elements from an array, consider:

* Using a library like Lodash (which has a `_.without()` method) or Ramda (which has a `R.remove()` function).
* Using a data structure like a Set or Map instead of an array.
* Using a different programming paradigm, like using recursion to remove elements from the array.

Keep in mind that these alternatives might have their own trade-offs in terms of performance, memory usage, and readability.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36

Browser/OS: Chrome 93 on Linux

View result in a separate tab

Test name	Executions per second
a	8760311.0 Ops/sec
b	10172367.0 Ops/sec

Related benchmarks:

abcdefg (version: 0)

abcdefg

Comparing performance of: a vs b

Created: 3 years ago by: Guest

Jump to the latest result

a

b

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):