Benchmark: Flat vs forEach + push

Script Preparation code:

var myArray = [];
var x = 2000;
while (myArray.length < x) {
  myArray.push([{a: 'b'}])
}

Tests:

Flat
myArray .map((each) => { return each.map((deet) => { return { subject: deet.a }; }); }) .flat();
forEach + push
let newArr = [] myArray.forEach((each) => { return each.forEach((deet) => { newArr.push({ subject: deet.a }) }) })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Flat
forEach + push

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36

Browser/OS: Chrome 125 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Flat	22107.3 Ops/sec
forEach + push	57770.7 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the benchmark and explain what's being tested, along with the pros and cons of each approach.

**Benchmark Overview**

The benchmark compares two approaches to flatten an array:

1. `flat()`: A built-in method in JavaScript arrays that returns a new array containing all elements from the original array.
2. `forEach()` + `push()`: A manual approach using the `forEach()` method to iterate over each element and push it into a new array.

**What is being tested?**

The benchmark measures the performance of these two approaches on a large input array (`myArray` with 2000 elements) in different browsers (Chrome 125).

**Options Compared:**

* `flat()`: This approach uses the built-in method to flatten the array.
	+ Pros:
		- Fast and efficient, as it's implemented in native code.
		- Avoids manual iteration and array manipulation.
	+ Cons:
		- May not work as expected if the input array contains non-array elements or if the `flat()` method is called recursively (e.g., with a nested array).
* `forEach()` + `push()`: This approach uses the `forEach()` method to iterate over each element and push it into a new array.
	+ Pros:
		- Works for arrays of any type, including non-array elements.
		- Can be more flexible when working with complex data structures.
	+ Cons:
		- Slower than `flat()`, as it involves manual iteration and array manipulation.

**Library Used:**

There is no library explicitly mentioned in the benchmark code. However, if you're interested in exploring libraries that provide similar functionality, some popular options include:

* Lodash (`_.flatten()`) or Ramda (`R.flatten()`) for functional programming styles.
* Underscore.js (`_flatten()`), Backbone.js ( `_flatten`), or Mojs library for basic array manipulation.

**Special JS Feature/Syntax:**

There is no special JavaScript feature or syntax used in this benchmark. The code only uses standard JavaScript features, such as arrays, loops, and the `forEach()` method.

**Other Considerations:**

* **Input size**: The benchmark uses a large input array (`2000` elements) to test performance.
* **Browser**: The benchmark is run in Chrome 125, which may have different performance characteristics compared to other browsers or versions.
* **Device Platform and Operating System**: The benchmark runs on a Mac OS X 10.15.7 desktop device.

**Alternative Approaches:**

Other approaches to flatten an array include:

* Using `Array.prototype.map()` with the `reduce()` method (e.g., `myArray.map((each) => each.reduce((acc, deet) => [...acc, { subject: deet.a }], [])`).
* Utilizing libraries like Immutable.js or a custom implementation using a recursive function.

Keep in mind that each approach has its trade-offs, and the best choice depends on your specific use case and requirements.

Related benchmarks:

Flat vs forEach + push (version: 0)

Comparing performance of: Flat vs forEach + push

Created: 4 years ago by: Guest

Jump to the latest result

Flat

forEach + push

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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