Benchmark: fast deep clone array

Script Preparation code:

var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(10000)));
}

Tests:

for
const newArr = []; for (const v of arrRandom) { newArr.push(v); }
reducer
arrRandom.reduce((arr, item, i) => { arr[i] = item; return arr; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
reducer

Fastest: N/A

Slowest: N/A

Latest run results:

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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 measuring JavaScript performance!

**What is being tested?**

The provided benchmark measures how quickly two different approaches can create a deep clone of an array:

1. The `for` loop approach: This method iterates over the original array using a `for` loop and pushes each element to a new array.
2. The reducer approach: This method uses the `Array.prototype.reduce()` method to create a new array, where each element is assigned from the original array.

**Options being compared**

The benchmark compares two approaches:

1. **For loop approach**: Using a traditional `for` loop to iterate over the array and create a copy.
2. **Reducer approach**: Utilizing the `Array.prototype.reduce()` method to create a new array, which is a more functional programming way of doing things.

**Pros and Cons**

**For Loop Approach:**

Pros:

* Wide browser support
* Familiar syntax for many developers

Cons:

* Can be slow due to unnecessary iterations
* May not perform well with large arrays

**Reducer Approach:**

Pros:

* More concise and expressive syntax
* Efficient use of memory
* Suitable for large datasets

Cons:

* Less intuitive for some developers
* Requires understanding of the `reduce()` method
* May not work in older browsers that don't support it

**Other considerations**

* The benchmark uses a random array with 1000 elements to test performance. This size is likely sufficient to demonstrate differences between the two approaches.
* No other libraries or features are used beyond what's inherent to JavaScript.

Now, let's take a look at some specific details from the provided benchmark definition and results:

**Library/Functionality Used**

The `Array.prototype.reduce()` method is used in the reducer approach. This method applies a function against an accumulator and each element in the array (from left-to-right) to reduce it to a single output value.

**Special JS Feature/Syntax**

None are explicitly mentioned, but using `const` and `let` variables for variable declarations is a good practice in modern JavaScript.

**Other Alternatives**

For creating deep clones of arrays, other methods can be used:

1. **Using the spread operator (`...`)**: `newArr = [...arrRandom];`
2. **Using the spread operator with `Array.from()`**: `newArr = Array.from(arrRandom);`
3. **Using a library like Lodash's `_cloneDeep()` function**

Keep in mind that these alternatives may have different performance characteristics or trade-offs depending on your specific use case.

I hope this explanation has been helpful!

Related benchmarks:

fast deep clone array (version: 0)

Comparing performance of: for vs reducer

Created: 5 years ago by: Guest

Jump to the latest result

for

reducer

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):