Benchmark: slice VS splice VS shift for fixed size list of objects

Script Preparation code:

var list = [];
for (let i = 0; i < 1000; i++) {
  list.push({'s':'usd', 'p':i});
}

Tests:

slice
list.push({'s':'cad', 'p':13}) list.push({'s':'cad', 'p':14}) list.push({'s':'cad', 'p':15}) if(list.length>1000){ list=list.slice(list.length-1000) }
splice
list.push({'s':'cad', 'p':13}) list.push({'s':'cad', 'p':14}) list.push({'s':'cad', 'p':15}) if(list.length>1000){ list.splice(0, list.length-1000); }
shift -while
list.push({'s':'cad', 'p':13}) list.push({'s':'cad', 'p':14}) list.push({'s':'cad', 'p':15}) while(list.length>1000){ list.shift(); }
shift-for
list.push({'s':'cad', 'p':13}) list.push({'s':'cad', 'p':14}) list.push({'s':'cad', 'p':15}) for(let i=0; i<(list.length-998); i++){ list.shift(); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
splice
shift -while
shift-for

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0

Browser/OS: Firefox 80 on Windows

View result in a separate tab

Test name	Executions per second
slice	90892.2 Ops/sec
splice	5907759.0 Ops/sec
shift -while	6332127.0 Ops/sec
shift-for	6390568.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring performance differences between various approaches in JavaScript is crucial, especially when working with large data structures like arrays.

**Benchmark Overview**

The benchmark, as described in the JSON file, compares four different methods for removing elements from an array:

1. `slice()`
2. `splice()`
3. A while loop (`shift()` repeatedly)
4. A for loop (`shift()` once per iteration)

These methods are compared on a fixed-size list of 1000 objects.

**Options Compared**

The options being compared are:

* `slice()`: Returns a shallow copy of the array, leaving the original array unchanged.
	+ Pros: Creates a new array without modifying the original, potentially more efficient than modifying the original.
	+ Cons: May be slower due to creating a new array and copies data.
* `splice()`: Modifies the original array by removing elements at the specified index.
	+ Pros: Modifies the original array in place, potentially faster than creating a new one.
	+ Cons: Can modify the original array, which might not be desirable for some use cases.
* While loop (`shift()` repeatedly):
	+ Pros: Efficiently removes elements from the end of the array without modifying the original.
	+ Cons: May not be suitable for large arrays due to repeated function calls and potential performance overhead.
* For loop (`shift()` once per iteration):
	+ Pros: Similar to the while loop, but potentially more readable and maintainable.
	+ Cons: May have similar performance issues as the while loop.

**Libraries and Features**

There are no external libraries used in this benchmark. However, JavaScript features like `const` and `let` are implicitly used, which can affect performance due to scope and reassignment limitations.

**Special JS Features or Syntax (None)**

No special JavaScript features or syntax are used in this benchmark.

**Other Alternatives**

Alternative approaches for removing elements from an array could include:

* Using `filter()` instead of `shift()`/`splice()`: This method creates a new array with only the desired elements, which can be faster than modifying the original array.
* Using `map()` and then reducing()` instead of `for` loops: These methods can be more concise and potentially faster for large arrays.

However, these alternatives might not directly compare to the four options being tested in this benchmark.

Related benchmarks:

slice VS splice VS shift for fixed size list of objects (version: 0)

Comparing performance of: slice vs splice vs shift -while vs shift-for

Created: 4 years ago by: Guest

Jump to the latest result

slice

splice

shift -while

shift-for

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):