Benchmark: slice and Spread syntax vs splice and Spread syntax

Tests:

slice
const arrayData = new Array(1000).fill('abcdefghijklmnopqrstuvwxyz') const newArray = [...arrayData.slice(0, 500), 'new name',...arrayData.slice(50) ]
splice
const arrayData = new Array(1000).fill('abcdefghijklmnopqrstuvwxyz') const tempArray = [...arrayData ] tempArray.splice(500, 0 , 'new name') const newArray = [...tempArray]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
splice

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark definition and test cases to understand what is being tested.

**Benchmark Definition**

The benchmark tests two different approaches for creating a new array with sliced elements:

1. **Slice and Spread syntax**: This approach uses the `slice()` method to extract a subset of elements from an array, followed by the spread operator (`...`) to include additional elements in the resulting array.
2. **Splice and Spread syntax**: This approach uses the `splice()` method to insert new elements at a specific position in the array, followed by the spread operator (`...`) to include existing elements in the resulting array.

**Options Compared**

The two approaches are compared based on their performance, which is measured as the number of executions per second. The test aims to determine which approach is faster and more efficient for creating new arrays with sliced elements.

**Pros and Cons of Each Approach**

1. **Slice and Spread syntax**:
	* Pros: This approach creates a new array with a subset of elements from the original array, avoiding any potential modifications to the original data.
	* Cons: Creating an intermediate array using `slice()` might incur additional memory allocation and copying overhead.
2. **Splice and Spread syntax**:
	* Pros: This approach avoids creating an intermediate array by modifying the original array in place, which can be more memory-efficient for large datasets.
	* Cons: Modifying the original data using `splice()` might lead to unexpected side effects or performance issues if the original data is not expected to change.

**Library and Purpose**

None of the provided benchmark definitions explicitly uses any libraries. However, it's worth noting that both approaches rely on JavaScript built-in methods (`slice()` and `splice()`) to manipulate arrays.

**Special JS Features or Syntax**

The benchmark definition does not use any special JavaScript features or syntax beyond standard array methods. If you were to modify the code to include experimental features like `BigInt` or `BigInt64Array`, that would be a different story!

**Other Alternatives**

If you wanted to explore alternative approaches for creating new arrays with sliced elements, some alternatives could be:

1. Using `Array.prototype.map()` and `Array.prototype.concat()`
2. Employing a custom loop-based approach
3. Utilizing libraries like Lodash or Ramda for array manipulation

However, these alternatives might not necessarily offer significant performance improvements over the standard approaches used in this benchmark.

In summary, the provided benchmark definition tests two common approaches for creating new arrays with sliced elements: using `slice()` and spread syntax versus using `splice()` and spread syntax. The test aims to determine which approach is faster and more efficient, while considering pros and cons of each approach.

Related benchmarks:

slice and Spread syntax vs splice and Spread syntax (version: 1)

Comparing performance of: slice vs splice

Created: 2 years ago by: Registered User

Jump to the latest result

slice

splice

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):