Benchmark: Ranged array mapping

Script Preparation code:

var arr_length = 30;

Tests:

Jobsity
const result = [...Array(arr_length) + 1].map(x => ({ number: x })); result.shift();
Simplified
const result = [...Array(arr_length)].map(x => ({ number: x + 1 }));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Jobsity
Simplified

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is essential for optimizing and fine-tuning code, especially when it comes to microbenchmarks like the ones provided by MeasureThat.net.

**Benchmark Definition**

The benchmark definition consists of two test cases:

1. **Jobsity**: This test case uses the spread operator (`...`) to create an array of `arr_length` elements plus one. It then maps over this array, creating a new object with a single property called "number" and assigns it to the variable `result`. Finally, it removes the first element from the resulting array using the `shift()` method.
2. **Simplified**: This test case also uses the spread operator (`...`) to create an array of `arr_length` elements, but maps over this array instead of adding one extra element.

**Options Compared**

In both test cases, two main options are compared:

* **With extra element (`+ 1`) vs. without extra element**: This option tests the performance difference between including an additional element in the array and excluding it.
* **Mapping with object creation vs. simple mapping**: The first test case uses object creation to map over the array, while the second test case uses a simpler mapping approach.

**Pros and Cons**

Here's a brief overview of each option:

1. **With extra element (`+ 1`) vs. without extra element**:
	* With an extra element: Can be beneficial for algorithms that require an additional element in the input data.
	* Without an extra element: May improve performance when working with large datasets, as it reduces memory allocation and deallocation overhead.
2. **Mapping with object creation vs. simple mapping**:
	* Object creation: Provides more flexibility and control over the resulting objects but can be slower due to the overhead of creating new objects.
	* Simple mapping: Can be faster for small to medium-sized arrays, as it avoids the overhead of object creation.

In general, including an extra element or using a simple mapping approach might provide performance benefits in certain scenarios. However, the actual impact depends on the specific use case and dataset size.

**Library Usage**

Neither test case uses any libraries beyond the built-in JavaScript features (e.g., `Array.prototype` methods). The spread operator (`...`) is used to create arrays with additional elements or without them, while the `map()` method is used for array transformations.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax mentioned in either test case. Both examples use standard JavaScript syntax and do not rely on any advanced language features.

**Alternative Approaches**

If you're interested in exploring alternative approaches, here are a few suggestions:

* **Using `for...of` loops**: Instead of `map()`, consider using `for...of` loops for iteration over arrays.
* **Implementing custom mapping logic**: For simple cases, consider implementing custom mapping logic using bitwise operations or arithmetic to avoid object creation overhead.
* **Testing with different array sizes**: Experiment with varying array lengths to observe the impact on performance.

Keep in mind that MeasureThat.net is designed specifically for microbenchmarking JavaScript code, so these alternatives might not provide meaningful results.

Related benchmarks:

Ranged array mapping (version: 0)

Comparing performance of: Jobsity vs Simplified

Created: 2 years ago by: Registered User

Jump to the latest result

Jobsity

Simplified

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):