Benchmark: FindIndex(real findIndex) + splice vs filter

Script Preparation code:

var arr = new Array(15000);
arr.fill({ id: 0 });
arr = arr.map((el, idx) => el.id = idx);
var foo = Math.floor(Math.random() * 15000);

Tests:

FindIndex + splice
var index = arr.findIndex((el) => el === foo); var newArr = arr.splice(index, 1);
filter
var index = arr.filter(el => el !== foo)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
FindIndex + splice
filter

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition**

The benchmark measures the performance of two approaches: `findIndex` with `splice`, and `filter`. The script preparation code creates an array of 15,000 elements, fills it with IDs from 0 to 14,999, and then maps each element to its ID. A random index is generated using `Math.random() * 15000`.

**Options Compared**

The benchmark compares two approaches:

1. **FindIndex + Splice**: This approach finds the index of an element in the array that matches a given value (`foo`) using `findIndex()`, and then removes one element from the original array starting at that index using `splice()`.
2. **Filter**: This approach creates a new array containing only elements that do not match the given value (`foo`) using the `filter()` method.

**Pros and Cons of Each Approach**

1. **FindIndex + Splice**:
	* Pros: Can be faster for arrays with many duplicates, as it stops searching once it finds a match.
	* Cons: Creates a new array and modifies the original array, which can be memory-intensive and slower than other approaches.
2. **Filter**:
	* Pros: More straightforward and efficient, creates only one new array, and is generally faster for large arrays.
	* Cons: May require more CPU cycles to process each element in the filter callback.

**Library Usage**

The benchmark uses the `Math` library for the `random()` function and some basic arithmetic operations. The `Array.prototype.map()` method is also used, which is a part of the standard JavaScript library.

**Special JS Features/Syntax**

This benchmark does not explicitly use any special JavaScript features or syntax beyond what's available in standard JavaScript (ES6+). However, it relies on features like array methods (`map()`, `findIndex()`, `splice()`, and `filter()`), which are part of the modern JavaScript language.

**Alternatives**

Some alternative approaches could be:

* Using `indexOf()` instead of `findIndex()` to find the index of an element.
* Using a different data structure, like a hash table or a linked list, for faster lookups.
* Implementing a custom search algorithm with iterative searching and early termination.
* Using a Just-In-Time (JIT) compiler or a JavaScript engine with optimizations like inlining and caching.

For the specific comparison between `FindIndex + Splice` and `Filter`, other alternatives could be:

* Using `indexOf()` and slicing the array instead of `splice()`.
* Creating an intermediate array using `slice()` and modifying it.
* Using a different data structure, like a hash table or a trie, for faster lookups.

Keep in mind that these alternative approaches may have their own trade-offs and performance characteristics.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.5845.967 YaBrowser/23.9.1.967 Yowser/2.5 Safari/537.36

Browser/OS: Yandex Browser 23 on Windows

View result in a separate tab

Test name	Executions per second
FindIndex + splice	2350.0 Ops/sec
filter	22623616.0 Ops/sec

Related benchmarks:

FindIndex(real findIndex) + splice vs filter (version: 0)

Comparing performance of: FindIndex + splice vs filter

Created: one year ago by: Guest

Jump to the latest result

FindIndex + splice

filter

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):