Benchmark: flatMap vs flat+map 2

Script Preparation code:

var arr = Array(500).fill('test')

Tests:

flatmap
arr.flatMap((x) => [x, x])
map+flat
arr.map((x) => [x, x]).flat()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatmap
map+flat

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/134.0.0.0 Safari/537.36

Browser/OS: Chrome 134 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
flatmap	73025.9 Ops/sec
map+flat	75806.9 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Definition and Script Preparation Code**

The benchmark definition is a JSON object that specifies the test case: `flatMap vs flat+map 2`. The script preparation code is a simple JavaScript snippet that creates an array with 500 elements, all filled with the string 'test'. This array will be used as input for both test cases.

**Individual Test Cases**

There are two individual test cases:

1. **flatMap**: The first test case uses the `flatMap` method on the `arr` array. `flatMap` is a method that creates a new array by performing an operation on each element of the original array and returning an array of the results. In this case, it's using a callback function to create a new array with the elements of the original array duplicated.
2. **map+flat**: The second test case uses two separate methods: `map` and then `flat`. `map` applies a given function to each element of an array and returns a new array with the results, while `flat` flattens an array that contains nested arrays.

**Options Compared**

In this benchmark, we have two options:

* **flatMap**: Using `flatMap` to create a new array by performing an operation on each element.
* **map+flat**: Using `map` and then `flat` to create a new array by first mapping the original array and then flattening the result.

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

* **flatMap**:
	+ Pros: Can be faster because it avoids the overhead of creating an intermediate array.
	+ Cons: May not work as expected if the callback function returns an array, because `flatMap` will recursively call itself on that array. In this benchmark, it works fine, but it's worth noting.
* **map+flat**:
	+ Pros: More flexible because you can use different functions for mapping and flattening.
	+ Cons: Can be slower because it creates two intermediate arrays (one for mapping and one for flattening).

**Library Used**

There is no explicit library mentioned in the benchmark definition or individual test cases. However, both `flatMap` and `flat` methods are part of the ECMAScript standard.

**Special JS Feature or Syntax**

Neither of these test cases uses any special JavaScript features or syntax that's not well-supported across most modern browsers.

**Other Alternatives**

If you're interested in exploring other alternatives, here are a few:

* Using `reduce` instead of `flatMap` could be another option. `reduce` applies a function to each element of an array and returns a single value.
* Using libraries like Lodash or Ramda might provide additional functionality for working with arrays, but these test cases are designed to demonstrate the built-in `flatMap` and `flat` methods.

In summary, this benchmark compares two approaches to flattening an array: using `flatMap` directly versus using `map` followed by `flat`. The results suggest that `flatMap` can be faster in this specific case.

Related benchmarks:

flatMap vs flat+map 2 (version: 0)

Comparing performance of: flatmap vs map+flat

Created: one year ago by: Guest

Jump to the latest result

flatmap

map+flat

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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