Benchmark: To dict: 2 iterations vs new array and unique on each iteration inside

Script Preparation code:

window.array = Array.from({ length: 100000 }, (_, i) => i)

Tests:

2 iterations
const dict = array.reduce((acc, item) => { if (!acc[item]) { acc[item] = []; } acc[item] = [...new Set([...acc[item], item])]; return acc; }, {}) Object.keys(dict).forEach(key => { dict[key] = [...new Set(dict[key])] }) console.log(dict);
On each
const dict = array.reduce((acc, item) => { if (!acc[item]) { acc[item] = []; } acc[item] = [...new Set([...acc[item], item])]; return acc; }, {}) console.log(dict);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
2 iterations
On each

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 explain what's being tested.

**Benchmark Definition JSON**

The benchmark defines two test cases:

1. **"To dict: 2 iterations vs new array and unique on each iteration inside"**: This is the main benchmark definition. It describes a script that creates an object (`dict`) by iterating over an array of 100,000 elements. The script uses `Array.prototype.reduce()` to create the object.
2. The script preparation code sets up a global variable `window.array` with an array of 100,000 elements generated using `Array.from()`.
3. The HTML preparation code is empty.

**Individual Test Cases**

There are two test cases:

1. **"2 iterations"`**: This test case runs the benchmark definition twice.
2. **"On each"`**: This test case runs the benchmark definition only once.

**Options Compared**

The benchmark compares three options:

1. **Using `reduce()` with array iteration**: The script uses `Array.prototype.reduce()` to create the object, which iterates over the array on each element.
2. **Creating a new array and using `Set` for uniqueness**: The script creates a new array and uses `Set` to ensure uniqueness of elements inside the array.
3. **No optimization (i.e., just creating the object)**: This option is not explicitly compared, but it's implied as a baseline.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Using `reduce()` with array iteration**:
	* Pros: Can be efficient for small arrays, uses built-in JavaScript function.
	* Cons: Iterates over the entire array on each element, can be slow for large arrays.
2. **Creating a new array and using `Set` for uniqueness**:
	* Pros: Can be efficient for large arrays, avoids iterating over the entire array.
	* Cons: Creates an additional array, which can be slower than using `reduce()` directly.
3. **No optimization (i.e., just creating the object)**:
	* Pros: Simple and straightforward approach.
	* Cons: May not be as efficient as optimized approaches.

**Library and Purpose**

The script uses the built-in JavaScript function `Array.prototype.reduce()`. This function is used to reduce an array to a single value, applying a callback function to each element in the array.

**Special JS Feature or Syntax**

This benchmark does not use any special JavaScript features or syntax. However, it assumes that the browser supports modern JavaScript features like `Arrow functions` (`=>`) and template literals (`\r\n\t...`).

**Other Alternatives**

Other alternatives for creating an object from an array could include:

1. **Using a loop**: Instead of using `reduce()`, you could use a simple loop to iterate over the array.
2. **Using `forEach()`**: You could use `Array.prototype.forEach()` instead of `reduce()`.
3. **Using a library like Lodash**: If you need more advanced functionality, you might consider using a library like Lodash, which provides a range of utility functions for working with arrays.

Keep in mind that the best approach will depend on your specific use case and performance requirements.

Related benchmarks:

To dict: 2 iterations vs new array and unique on each iteration inside (version: 0)

Comparing performance of: 2 iterations vs On each

Created: 2 years ago by: Guest

Jump to the latest result

2 iterations

On each

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):