Benchmark: split...reduce vs split...map

Script Preparation code:

var classArray = [];
for (var i=0; i < 10000; i++) {
  classArray.push('class-name');
}
var classes = classArray.join(' ' );

Tests:

split...reduce
classes.split(' ').reduce((acc, item)=>{ return { ...acc, [item]: true, }; }, {});
split...map
classes.split(' ').map(item=> ({[item]: true}));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
split...reduce
split...map

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 JSON data and explain what is being tested.

**Benchmark Definition**

The benchmark is comparing two approaches to manipulate an array of class names: `split()`, `map()`, and their respective reducers (`reduce()`). The goal is to determine which approach is faster.

**Script Preparation Code**

The script preparation code creates an array of 10,000 class names using the `join()` method. This array will be used for both test cases.

**Html Preparation Code**

There is no HTML preparation code provided in this benchmark definition.

**Individual Test Cases**

There are two test cases:

1. **`split...reduce`**: This test case uses the `split()` method to split the class names into an array, and then applies a reducer function using `reduce()`. The reducer creates an object with each class name as a key.
2. **`split...map`**: This test case uses the `split()` method to split the class names into an array, and then applies the `map()` method to create a new array of objects with each class name as a key.

**Library Usage**

Neither of these test cases uses any external libraries or frameworks.

**Special JavaScript Features/Syntax**

There are no special JavaScript features or syntax used in this benchmark. The code is straightforward and idiomatic JavaScript.

**Other Alternatives**

If you were to write this benchmark from scratch, you could consider the following alternatives:

* Using `Array.prototype.forEach()` instead of `reduce()`.
* Using a custom loop instead of `map()`.
* Splitting the class names into an array using a different method (e.g., `split(' ', 100)`, which would split on 100 occurrences of whitespace).
* Adding additional tests to compare other methods for processing arrays, such as `Array.prototype.filter()` or `Array.prototype.every()`.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **`reduce()`**: Pros:
	* Can be more concise and expressive than a custom loop.
	* Can handle complex data structures with ease.
	* Can be used to perform aggregations or transformations.
Cons:
	* Can be slower due to the overhead of function calls and state management.
	* May not be suitable for very large datasets.
2. **`map()`**: Pros:
	* Is generally faster than `reduce()` for simple transformations.
	* Returns a new array without modifying the original data.
	* Can handle multiple iterations or loops easily.
Cons:
	* Does not provide access to the original data (i.e., it returns a new array).
	* May require additional processing steps to achieve the desired result.
3. **Custom Loop**: Pros:
	* Can be optimized for performance by using a tight loop and minimizing function calls.
	* Provides direct access to the original data.
Cons:
	* Can be more verbose and less expressive than `reduce()` or `map()`.
	* May require additional memory allocation and management.

In this specific benchmark, both approaches are likely to produce similar results. However, the choice of which one to use depends on the specific requirements and constraints of your project.

Related benchmarks:

split...reduce vs split...map (version: 0)

Comparing performance of: split...reduce vs split...map

Created: 5 years ago by: Guest

Jump to the latest result

split...reduce

split...map

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):