Benchmark: reduce.concat() vs flat(1)

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.flat(1);

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: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36

Browser/OS: Chrome 87 on Windows

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Test name	Executions per second
reduce + Array.prototype.concat	2855951.5 Ops/sec
Array.prototype.flat	599191.7 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is comparing two approaches to concatenate arrays in JavaScript:

1. `reduce()` with `concat()`
2. The new ES6 spread operator (`...`)

These two approaches are compared because they have different performance characteristics, especially for large datasets.

**Options Compared**

The two options being compared are:

* **Option 1: reduce() + Array.prototype.concat**
	+ This approach uses the `reduce()` method to iterate over an array and concatenate its elements using the `concat()` method. The `reduce()` method is called with an accumulator function that concatenates the elements of each array in turn.
	+ Pros:
		- Can handle nested arrays
	+ Cons:
		- May be slower due to the overhead of calling `concat()`
* **Option 2: Array.prototype.flat(1)**
	+ This approach uses the `flat()` method with a depth parameter set to 1, which flattens the array by one level. The `flat()` method is designed to handle nested arrays more efficiently than `reduce()` and `concat()`.
	+ Pros:
		- Faster and more efficient for large datasets
	+ Cons:
		- May not work as expected with deeply nested arrays

**Library Used**

The `reduce()` and `concat()` methods are built-in JavaScript methods, which means they don't require any external libraries.

**Special JS Feature/Syntax**

The ES6 spread operator (`...`) is a relatively new feature in JavaScript that allows for more concise array creation. It's not as widely supported as older syntaxes, but it's gaining popularity.

**Benchmark Preparation Code and Test Cases**

The benchmark preparation code consists of two test cases:

1. The first test case creates an array `params` with nested arrays and uses the `reduce()` method to concatenate its elements using the `concat()` method.
2. The second test case creates a similar array `params` but uses the `flat(1)` method instead.

The benchmark runs each test case multiple times (the exact number is not specified) and measures the execution time for each run.

**Other Alternatives**

Some alternative approaches to concatenate arrays in JavaScript include:

* Using `Array.prototype.forEach()` and pushing elements onto an accumulator array
* Using a simple loop with `push()`
* Using a library like Lodash, which provides its own `concat()` method

However, these alternatives are not as efficient or convenient as the built-in methods used in the benchmark.

In summary, this benchmark compares two approaches to concatenate arrays in JavaScript: `reduce()` with `concat()` and the new ES6 spread operator (`...`). The `flat(1)` method is shown to be faster and more efficient for large datasets.

Related benchmarks:

reduce.concat() vs flat(1) (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: 4 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 2 months ago):