Benchmark: slice VS splice VS shift: who is the fastest to keep constant size'huang

Script Preparation code:

var list = [];
for (var i = 0; i < 10000; i++) {
  let object = {};
  for (var j = 0; j < 300; j++)
  {
    object[j] = 0.01
  }
  list.push(object);
}

Tests:

slice
list.push('slice'); list = list.slice(10);
splice
list.push('splice'); list.splice(0, 10);
shift
list.push('splice'); list.shift();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
splice
shift

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

I'd be happy to help explain what's being tested in the provided JSON benchmark.

**Benchmark Overview**

The benchmark is designed to measure the performance of three different methods for removing elements from an array in JavaScript: `slice`, `splice`, and `shift`. The goal is to determine which method is the fastest while maintaining a constant size.

**Script Preparation Code**

The script preparation code creates an empty array, `list`, and then populates it with 10000 objects, each containing 300 properties set to 0.01. This large dataset will be used to test the performance of each method.

**Individual Test Cases**

There are three individual test cases:

1. **slice**: The benchmark definition is `list.push('slice'); list = list.slice(10);`. This creates a new array by slicing the original array and assigns it back to the `list` variable.
2. **splice**: The benchmark definition is `list.push('splice'); list.splice(0, 10);`. This removes the first 10 elements from the array using the `splice` method.
3. **shift**: The benchmark definition is `list.push('shift'); list.shift();`. This removes the first element from the array using the `shift` method.

**Pros and Cons of Each Approach**

1. **slice**:
	* Pros: Creates a new array, does not modify the original array, can be faster for large datasets.
	* Cons: Allocates new memory, can be slower than in-place methods like `splice` or `shift`.
2. **splice**:
	* Pros: Modifies the original array, can be faster for large datasets due to fewer allocations.
	* Cons: Modifies the original array, may have performance implications if used with very large datasets.
3. **shift**:
	* Pros: Modifies the original array, can be faster than `splice` or `slice` for small datasets.
	* Cons: Removes elements from the end of the array, may not be suitable for all use cases.

**Library**

There is no explicit library mentioned in the benchmark, but it's likely that the standard JavaScript array methods are being used. If there was an external library involved, it would have been explicitly noted.

**Special JS Feature or Syntax**

None are mentioned specifically.

**Other Considerations**

When choosing between these methods, consider the trade-off between memory usage and performance. For small datasets, `shift` might be sufficient. For larger datasets, `splice` might be a better choice due to fewer allocations. However, for very large datasets, creating a new array using `slice` might still be the fastest option.

**Alternatives**

Other methods that could be used to remove elements from an array include:

* Using the `filter()` method with a callback function
* Using the `map()` method with a callback function
* Using the `forEach()` method with a callback function

However, these methods may not be as efficient as the original three methods mentioned in the benchmark.

Related benchmarks:

slice VS splice VS shift: who is the fastest to keep constant size'huang (version: 0)

100k list splice and shift win, they mutate list slice loose, it creates a copy of list 7.5x slower

Comparing performance of: slice vs splice vs shift

Created: 4 years ago by: Registered User

Jump to the latest result

slice

splice

shift

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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