Benchmark: Wrapped slice - MeasureThat.net

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

var max1 = 100000; // 100,000 (100 Thousand)
var max2 = 10000000; // 10,000,000 (10 Million)
var max3 = 100000000; // 100,000,000 (100 Million)

var arr1 = [];
//for (var i = 0; i <= max1; i++) { arr1.push(i); }

var arr2 = [];
for (var i = 0; i <= max2; i++) { arr2.push(i); }

var arr3 = [];
//for (var i = 0; i <= max3; i++) { arr3.push(i); }

function myClone(arr) {
  return arr.slice(0)
}

Tests:

Native
arr2.slice(0)
wrapped
myClone(arr2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
wrapped

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents a benchmark test on MeasureThat.net, which allows users to create and run JavaScript microbenchmarks. The benchmark in question is testing the performance of slicing an array using two approaches: native and wrapped.

**What is being tested?**

The benchmark is comparing the execution time of two methods:

1. **Native**: Using the built-in `slice()` method to extract a subset of elements from an array.
2. **Wrapped**: Wrapping the `slice()` method in a custom function called `myClone()`, which takes an array as input and returns a new array with the first element removed.

**Options compared**

The benchmark is comparing two approaches:

1. **Native approach**: Using the built-in `slice()` method to slice the array.
2. **Wrapped approach**: Wrapping the `slice()` method in the custom `myClone()` function to clone the array.

**Pros and cons of each approach:**

**Native approach**

Pros:

* Faster execution time, as it leverages the optimized C++ implementation under the hood.
* Less memory overhead, as no additional object creation is required.

Cons:

* May not work correctly with arrays that have complex logic or custom iterators.
* Limited control over the cloning process.

**Wrapped approach**

Pros:

* Provides more control over the cloning process, allowing for custom logic or transformations to be applied.
* Can be useful when working with complex or customized array data structures.

Cons:

* Slower execution time due to the overhead of function call and object creation.
* More memory overhead, as a new object is created and cloned.

**Library: Lodash**

The benchmark includes an external library called Lodash, which provides a `slice()` method. However, since the native approach uses the built-in `slice()` method, Lodash is not actually used in this specific benchmark.

**Special JS feature or syntax**

There doesn't appear to be any special JavaScript features or syntax being tested in this benchmark. The code only uses basic array operations and functions.

**Other alternatives**

If you want to explore alternative approaches or optimize the existing ones, here are some options:

1. **Use a library like `Array.prototype.slice.call()`**: This method can provide better performance than `slice()`, especially when working with large arrays.
2. **Optimize the custom cloning function**: Consider using techniques like caching, memoization, or parallel processing to improve the performance of the wrapped approach.
3. **Explore alternative array data structures**: Depending on your specific use case, you may be able to achieve better performance by using alternative data structures like linked lists or hash tables.

Keep in mind that these alternatives will depend on your specific requirements and the characteristics of your codebase.

Related benchmarks:

Wrapped slice (version: 0)

Comparing performance of: Native vs wrapped

Created: 5 years ago by: Guest

Jump to the latest result

Native

wrapped

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):