Benchmark: Every Third Element

Script Preparation code:

function fillArrayWithNumbers(n) {
        var arr = Array.apply(null, Array(n));
        return arr.map(function (x, i) { return i });
    }

var sourceArray = fillArrayWithNumbers(1000);

Tests:

Push to new array
var targetArray = []; for (var i = 0; i < sourceArray.length; i = i+3) { targetArray.push(sourceArray[i]); };
Splice
for(var i = 0; i < sourceArray.length; i++) { sourceArray.splice(i+1,2); }
Filter
var targetArray = sourceArray.filter(function(_, i) { return (i % 3 == 0); })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Push to new array
Splice
Filter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared, and the pros/cons of different approaches.

**Benchmark Definition**

The benchmark is defined by a JSON object that contains the following information:

* `Name`: The name of the benchmark.
* `Description`: An optional description of the benchmark (not filled in this case).
* `Script Preparation Code`: A JavaScript function that prepares the script for execution. In this case, it generates an array with 1000 elements and returns a modified version of the array that increments its index by 3 when mapped to a new value.
* `Html Preparation Code`: An optional HTML code that prepares the page for execution (not filled in this case).

**Test Cases**

The benchmark consists of three test cases:

1. **Push to new array**: This test case pushes every third element from the `sourceArray` into a new array, `targetArray`. The benchmark measures how fast this operation is executed.
2. **Splice**: This test case uses the `splice()` method to remove two elements from the `sourceArray`, starting from the index that would be the next element after every third element in the original array. The benchmark measures how fast this operation is executed.
3. **Filter**: This test case uses the `filter()` method to create a new array containing only the elements of the `sourceArray` where the index modulo 3 equals 0 (i.e., every third element). The benchmark measures how fast this operation is executed.

**Options Compared**

The benchmark compares three different approaches:

1. **Push to new array**: This approach involves creating a new array and pushing elements from the original array into it.
2. **Splice**: This approach involves modifying the original array by removing elements using `splice()`.
3. **Filter**: This approach involves creating a new array containing only specific elements from the original array.

**Pros/Cons of Different Approaches**

Here are some pros and cons of each approach:

* **Push to new array**:
	+ Pros: Creates a new, independent array that is not affected by the original array. Can be more efficient for large datasets.
	+ Cons: Requires extra memory allocation and copying of data.
* **Splice**:
	+ Pros: Modifies the original array in place, reducing memory usage.
	+ Cons: Can be slower due to the overhead of modifying the array's internal state.
* **Filter**:
	+ Pros: Creates a new array without modifying the original array. Can be more efficient for small datasets.
	+ Cons: Requires extra memory allocation and copying of data.

**Library Usage**

The benchmark uses the `Array.prototype.map()` method, which is part of the JavaScript Array prototype. This method creates a new array with the results of applying a provided function to each element in the original array.

The `filter()` method is also used, which is another part of the JavaScript Array prototype. This method creates a new array containing only the elements that pass the test implemented by the provided function.

**Special JS Features or Syntax**

None of the benchmark cases use any special JavaScript features or syntax beyond what is part of the ECMAScript standard (e.g., `let`, `const`, arrow functions). However, the use of `map()`, `filter()`, and `splice()` methods are some of the more advanced features of the language.

**Other Alternatives**

If you were to implement a similar benchmark for other programming languages or libraries that support array operations, you might consider using:

* For languages without built-in support for array manipulation (e.g., Python):
	+ Create a new array or list and iterate over it using indexing or mapping functions.
	+ Use library functions like `numpy` in Python to perform element-wise operations on arrays.
* For libraries that provide similar functionality:
	+ If you're working with data structures that aren't arrays, consider creating a custom implementation of the desired operation.

Keep in mind that this is just a general guide, and the best approach will depend on the specific use case and requirements.

Latest run results:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36

Browser/OS: Chrome 52 on Windows 7

View result in a separate tab

Test name	Executions per second
Push to new array	13950.0 Ops/sec
Splice	1746399.4 Ops/sec
Filter	1909140.2 Ops/sec

Related benchmarks:

Every Third Element (version: 0)

Comparing performance of: Push to new array vs Splice vs Filter

Created: 8 years ago by: Guest

Jump to the latest result

Push to new array

Splice

Filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):