Benchmark: reduce.concat() vs map.flat()

Tests:

reduce + Array.prototype.concat
var params = [[ 1, 2 ], [ "hello", true, 7 ]]; var other = params.reduce((acc, val) => acc.concat(val), []);
Array.prototype.flat
var params = [[1, 2, params], [ "hello", true, 7 ]]; var other = params.map((x) => x).flat();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce + Array.prototype.concat
Array.prototype.flat

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36

Browser/OS: Chrome 103 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce + Array.prototype.concat	13401788.0 Ops/sec
Array.prototype.flat	1699353.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested.

**Benchmark Definition**

The benchmark compares two approaches to concatenate arrays: `reduce.concat()` and `map().flat()`. The new ES6 spread operator (`...`) is also used as an alternative approach.

**Options Compared**

1. **`reduce.concat()`**: This method uses the `concat()` function to concatenate arrays. It's a traditional way of combining arrays, but it can lead to performance issues due to the creation of intermediate arrays.
2. **`map().flat()`**: This approach uses the `map()` function to create a new array with the results of applying a provided function to each element in the original array. The `flat()` method then flattens the resulting array, removing any nested arrays.
3. **New ES6 spread operator (`...`)**: This operator allows you to spread the elements of an array into individual arguments of a function call.

**Pros and Cons**

* **`reduce.concat()`**:
	+ Pros: Simple and easy to understand.
	+ Cons: Can lead to performance issues due to intermediate array creation, especially for large arrays.
* **`map().flat()`**:
	+ Pros: More efficient than `reduce.concat()` since it avoids creating intermediate arrays. It's also more flexible, as you can apply different functions to each element in the array.
	+ Cons: Requires using two functions (`map()` and `flat()`) and understanding their behavior.
* **New ES6 spread operator (`...`)**:
	+ Pros: Concise and readable way to concatenate arrays. It's also faster than traditional concatenation methods since it avoids creating intermediate arrays.
	+ Cons: Not as widely supported in older browsers or versions of JavaScript.

**Library Usage**

None of the options listed above use any external libraries.

**Special JS Feature/Syntax**

* The `flat()` method is a built-in JavaScript function introduced in ECMAScript 2019 (ES10).
* The new ES6 spread operator (`...`) was introduced in ECMAScript 2015 (ES6).

**Other Alternatives**

If you need to concatenate arrays, there are other approaches you can consider:

1. `Array.prototype.push()`: Concatenating arrays using `push()` is another option. However, this method modifies the original array and returns the new length of the array.
2. `Set.concat()`: In JavaScript 17, a new function called `Set.concat()` was introduced, which concatenates sets. However, this approach is not applicable to arrays.

For more complex operations or when working with large datasets, consider using libraries like Lodash or Ramda, which provide various utility functions for array manipulation and other data-related tasks.

I hope this explanation helps! Let me know if you have any further questions.

Related benchmarks:

reduce.concat() vs map.flat() (version: 0)

Compare the new ES6 spread operator with the traditional concat() method

Comparing performance of: reduce + Array.prototype.concat vs Array.prototype.flat

Created: 2 years ago by: Guest

Jump to the latest result

reduce + Array.prototype.concat

Array.prototype.flat

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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