Benchmark: slice VS splice 500k from 1m

Script Preparation code:

var list = [];
for (var i = 0; i < 1000 * 1000; i++) {
  list.push(i);
}

Tests:

slice
list.push('slice'); list = list.slice(500 * 1000);
splice
list.push('splice'); list.splice(0, 500 * 1000);

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 and its various components.

**Benchmark Definition JSON**

The benchmark definition is a JSON object that contains information about the test case, including:

* `Name`: A unique name for the benchmark.
* `Description`: An optional description of the benchmark (which is empty in this case).
* `Script Preparation Code` and `Html Preparation Code`: These are code snippets that are executed before running the actual benchmark. In this case, the script prepares an array (`list`) with 1 million elements by pushing numbers from 0 to 999,999.

**Individual Test Cases**

The benchmark has two test cases:

1. **Test Case 1: "slice"**
	* The `Benchmark Definition` is a JavaScript statement that pushes the string `'slice'` onto the `list` array and then uses the `Array.prototype.slice()` method to create a new array containing every fifth element (500,000 elements) of the original array.
	* This test case measures the performance of using the `slice()` method to extract elements from an array.
2. **Test Case 2: "splice"**
	* The `Benchmark Definition` is similar to the first test case, but it uses the `Array.prototype.splice()` method instead of `slice()`. Instead of extracting every fifth element, it removes the first 500,000 elements from the original array.

**Library**

Neither of these test cases rely on a specific library. They are basic examples of JavaScript array manipulation methods.

**Special JS Feature/Syntax**

There is no special JavaScript feature or syntax used in these benchmark definitions. However, if we were to extend this benchmark to include other features, we might consider using:

* `Array.prototype.forEach()` or `Array.prototype.map()` for different types of transformations on the array.
* `Async/await` or callbacks for handling asynchronous operations.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Using `slice()`**: Pros:
	+ Efficient when extracting a contiguous subset of elements from an array.
	+ Often faster than using `splice()`.
Cons:
	+ Can be slower when extracting non-contiguous or random subsets of elements.
2. **Using `splice()`**:
	+ Can be faster for large arrays because it modifies the original array in-place, reducing garbage collection overhead.
Pros:
	* Can modify the original array directly, which might be beneficial in some scenarios.
Cons:
	* Generally slower than using `slice()` when extracting contiguous subsets of elements.

**Other Alternatives**

For large datasets or performance-critical code, you might also consider using:

1. **`Array.prototype.subarray()`**: A more modern method for accessing a subset of an array, which can be faster and more efficient than `splice()`.
2. **`Buffer slicing()`**: When working with buffers (e.g., in Node.js), you can use the `slice()` method on buffers to access specific subsets of data.

Keep in mind that these alternatives might not provide significant performance benefits unless you're dealing with extremely large datasets or compute-intensive operations.

Related benchmarks:

slice VS splice 500k from 1m (version: 0)

Comparing performance of: slice vs splice

Created: 5 years ago by: Guest

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):