Benchmark: spread array performance (vs slice, splice, concat)asd

Tests:

Splice
const testArray = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]; const newTestArray = testArray.splice(0, 1);
Slice
const testArray = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]; const newTestArray = testArray.slice();
Concat
const testArray = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]; const newTestArray = testArray.concat();
Spread
const testArray = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]; const newTestArray = [1, ...testArray];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Splice
Slice
Concat
Spread

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 dive into the world of JavaScript microbenchmarks.

**Benchmark Overview**

The provided JSON represents a benchmark test on MeasureThat.net, which compares the performance of three different approaches to create a new array from an existing one: `splice`, `slice`, and `concat`. Additionally, there's also a "Spread" approach using the spread operator (`...`).

**Test Case Breakdown**

Each test case is defined by a JSON object containing:

*   `Benchmark Definition`: The code snippet that defines the benchmark. This includes creating an array of 17 elements, and then applying one of the three approaches to create a new array.
*   `Test Name`: A human-readable name for the test case.

Here's a brief explanation of each approach:

*   **Splice**: This method modifies the original array by removing the first element (`splice(0, 1)`). The `newTestArray` is created with one fewer element than the original array.
*   **Slice**: This method creates a shallow copy of the original array using the `slice()` method. The `newTestArray` is an independent array that references the same elements as the original array.
*   **Concat**: Similar to `splice`, but instead of removing an element, it appends a new element to the end of the array (`concat()`). The `newTestArray` is created with one more element than the original array.
*   **Spread**: This approach uses the spread operator (`...`) to create a new array by copying elements from the original array. The `newTestArray` is an independent array that references the same elements as the original array.

**Library and Special Features**

None of these approaches rely on external libraries, but they do utilize some JavaScript-specific features:

*   `splice()`, `slice()`, and `concat()` are built-in methods for manipulating arrays.
*   The spread operator (`...`) is a feature introduced in ECMAScript 2018 (ES2018).

**Pros and Cons of Each Approach**

Here's a brief summary of the pros and cons of each approach:

*   **Splice**: Pros - efficient, concise. Cons - modifies the original array, might be less readable.
*   **Slice**: Pros - creates an independent copy, predictable results. Cons - potentially slower due to memory allocation.
*   **Concat**: Pros - straightforward, easy to understand. Cons - might lead to performance issues if the array is large and repeated concatenations occur.
*   **Spread**: Pros - concise, readable. Cons - might be less efficient than `splice` or `slice`, as it involves creating a new array.

**Other Alternatives**

In addition to these approaches, other methods could be used to create a new array from an existing one, such as:

*   Using the `Array.from()` method and providing an iterator function.
*   Utilizing the `map()` method with an identity function (`x => x`).
*   Creating a new array using a `for...of` loop.

However, these alternatives might not be as commonly used or optimized for performance in JavaScript applications.

That's a brief overview of the benchmark!

Related benchmarks:

spread array performance (vs slice, splice, concat)asd (version: 0)

Comparing performance of: Splice vs Slice vs Concat vs Spread

Created: 4 years ago by: Guest

Jump to the latest result

Splice

Slice

Concat

Spread

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):