Benchmark: JS remove test - MeasureThat.net

Tests:

filter
const items = [...Array(100).keys()] const newItems = items.filter(idx => idx !== 55)
splice
const items = [...Array(100).keys()] const removeIdx = items.findIndex( item => item === 55) items.splice(removeIdx, 1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter
splice

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 what's tested in the provided benchmark and explain the different approaches, pros and cons.

**Benchmark Definition**

The benchmark is created using MeasureThat.net, which allows users to create and run JavaScript microbenchmarks. The basic structure of a benchmark consists of a script preparation code, an HTML preparation code, and a benchmark definition. In this case, both fields are empty.

**Individual Test Cases**

There are two test cases:

1. **Filter Method**
The benchmark definition is:
```
const items = [...Array(100).keys()]
const newItems = items.filter(idx => idx !== 55)
```
This code creates an array of numbers from 0 to 99 and then uses the `filter()` method to create a new array that excludes the number 55. The filter method is a widely used JavaScript function for filtering arrays.

2. **Splice Method**
The benchmark definition is:
```
const items = [...Array(100).keys()]
const removeIdx = items.findIndex(item => item === 55)
items.splice(removeIdx, 1)
```
This code creates an array of numbers from 0 to 99 and then uses the `findIndex()` method to find the index of the number 55. The `splice()` method is then used to remove the element at that index from the array.

**Comparison**

Both approaches achieve the same result, but they use different methods:

* **Filter Method**: This approach creates a new array with the filtered elements, which can be more efficient for large datasets.
* **Splice Method**: This approach modifies the original array by removing the element, which can be less efficient because it requires shifting all subsequent elements to fill the gap.

**Pros and Cons**

* **Filter Method**:
	+ Pros: More memory-efficient, faster for large datasets.
	+ Cons: Creates a new array, potentially slower if not optimized correctly.
* **Splice Method**:
	+ Pros: Modifies the original array in place, can be faster for small datasets.
	+ Cons: Requires shifting all subsequent elements, less efficient for large datasets.

**Library Usage**

None of the code snippets use any external libraries. Both approaches rely on built-in JavaScript functions and methods.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in these benchmarks. They are straightforward examples that demonstrate common array manipulation techniques.

**Other Alternatives**

For large datasets, other approaches like `map()` or `reduce()` could be used instead of `filter()` or `splice()`. For example:
```
const newItems = items.map(idx => idx !== 55);
// or
const sum = items.reduce((acc, item) => acc + (item !== 55), 0);
```
However, these alternatives may have different performance characteristics and use more memory.

For even faster performance, using native WebAssembly or AssemblyScript could be an option, but this is a more advanced topic and requires a deeper understanding of web development and optimization techniques.

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/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Windows

View result in a separate tab

Test name	Executions per second
filter	112461.0 Ops/sec
splice	126983.4 Ops/sec

Related benchmarks:

JS remove test (version: 0)

Comparing performance of: filter vs splice

Created: one year ago by: Guest

Jump to the latest result

filter

splice

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):