Benchmark: test arr rapidity

Script Preparation code:

var arr = [{ lorem: 'ipsum'}, { dolor: 'sit'}, { foo: 'bar' }, { bar: 'foo' } ]

Tests:

splice
const test = arr.splice(2, 1)
filter
const test = arr.filter((x, index) => index !== 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
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 dive into the world of JavaScript microbenchmarks!

**Benchmark Definition**

The provided benchmark definition is a JSON object that represents a simple array with four elements:
```json
{
  "Name": "test arr rapidity",
  "Description": null,
  "Script Preparation Code": "var arr = [{ lorem: 'ipsum'}, { dolor: 'sit'}, { foo: 'bar' }, { bar: 'foo' } ]",
  "Html Preparation Code": null
}
```
The script preparation code creates an array `arr` with four elements, which will be used as the input for subsequent test cases.

**Individual Test Cases**

There are two individual test cases defined in the benchmark:
```json
[
  {
    "Benchmark Definition": "const test = arr.splice(2, 1)",
    "Test Name": "splice"
  },
  {
    "Benchmark Definition": "const test = arr.filter((x, index) => index !== 2)",
    "Test Name": "filter"
  }
]
```
These test cases measure the performance of two different operations on the `arr` array:

1. **splice**: This test case measures the time it takes to execute `arr.splice(2, 1)`, which removes the third element from the array.
2. **filter**: This test case measures the time it takes to execute `arr.filter((x, index) => index !== 2)`, which filters out the third element from the array.

**Options Compared**

In both cases, we have two options:

1. **Original implementation**: The original way of implementing the operation using the array's built-in methods.
2. **Optimized implementation**: An optimized version of the operation that is likely to be faster.

**Pros and Cons of Each Approach**

Let's analyze each option:

### Splice

* **Original implementation**
	+ Pros: Simple, intuitive, and easy to understand.
	+ Cons:
		- Can be slow for large arrays due to the overhead of modifying the array's internal structure.
		- Creates a new array with fewer elements, which can lead to memory fragmentation issues.
* **Optimized implementation**
	+ Pros:
		- Faster execution time due to reduced overhead and more efficient memory management.
		- May be able to take advantage of SIMD instructions or other optimization techniques.
	+ Cons: Less intuitive, may require additional setup or configuration.

### Filter

* **Original implementation**
	+ Pros:
		- Simple and easy to understand.
		- Does not modify the original array.
	+ Cons:
		- Can be slow for large arrays due to the overhead of iterating over the elements.
		- Creates a new array with filtered elements, which can lead to memory fragmentation issues.
* **Optimized implementation**
	+ Pros:
		- Faster execution time due to reduced overhead and more efficient memory management.
		- May be able to take advantage of SIMD instructions or other optimization techniques.
	+ Cons: Less intuitive, may require additional setup or configuration.

**Library Used**

In both test cases, no specific library is used. However, the `splice` method does modify the original array, which means it's not a safe operation for some use cases (e.g., when working with shared data structures).

**Special JS Feature/Syntax**

None of the test cases use any special JavaScript features or syntax.

**Other Alternatives**

Some alternative approaches that could be considered:

* **Using a library**: There are libraries like Lodash or Ramda that provide optimized implementations for common array operations.
* **Using SIMD instructions**: For large arrays, using SIMD instructions (e.g., SSE or AVX) can significantly improve performance.
* **Using a Just-In-Time (JIT) compiler**: Some JavaScript engines, like SpiderMonkey, use JIT compilation to optimize performance-critical code.

Keep in mind that these alternatives may require additional setup, configuration, or expertise, and may not be suitable for all use cases.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36

Browser/OS: Chrome 108 on Linux

View result in a separate tab

Test name	Executions per second
splice	19168504.0 Ops/sec
filter	13982911.0 Ops/sec

Related benchmarks:

test arr rapidity (version: 0)

Comparing performance of: splice vs filter

Created: one year ago by: Guest

Jump to the latest result

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):