Benchmark: flatMap() vs filter().map()asdsadsaasdsad

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 30) arr[i] = i++;

Tests:

filter().map()
arr.filter(x => x % 3).map(x => x/100)
flatMap()
arr.flatMap(x => x % 3 ? x/100 : [])
map.flat
arr.map(x => x % 3 ? x/100 : []).flat(1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter().map()
flatMap()
map.flat

Fastest: N/A

Slowest: N/A

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 definition and test cases to understand what is being tested.

**Benchmark Definition:**

The benchmark is comparing three different approaches:

1. `filter()`: This method creates a new array with only the elements that pass the test implemented by the provided function.
2. `flatMap()`: This method is similar to `map()` but returns an array of arrays instead of an array of single values. It's used when you want to transform each element of an array into multiple values, or when you need more control over how the elements are transformed.
3. `map().flat(1)`: This approach uses the `map()` method to create a new array with the results, and then calls the `flat()` method on it to flatten the resulting array.

**Pros and Cons:**

* `filter()`: Pros:
	+ Lightweight, as it only creates a new array with the filtered elements.
	+ Easy to understand and implement.
Cons:
	+ Can be slower than `flatMap()` for large datasets due to the overhead of creating multiple intermediate arrays.
* `flatMap()`: Pros:
	+ Can be faster than `filter()` for large datasets because it avoids the overhead of creating multiple intermediate arrays.
Cons:
	+ May have a higher memory footprint due to the creation of multiple array elements.
	+ Requires more complex logic to implement, especially when dealing with conditional transformations.
* `map().flat(1)`: Pros:
	+ Provides a convenient way to flatten an array after mapping it using `map()`.
Cons:
	+ May be slower than `flatMap()` for large datasets due to the overhead of creating and flattening intermediate arrays.

**Library Usage:**

The benchmark uses the following libraries:

* None explicitly mentioned, but `Array.prototype.map()` and `Array.prototype.flat()` are being used. These methods are part of the ECMAScript standard and do not require any additional libraries.

**Special JS Features/Syntax:**

There is no special JavaScript feature or syntax being tested in this benchmark.

**Other Alternatives:**

If you're looking for alternative approaches to flatten an array, you could consider using `reduce()` with a callback function that concatenates the elements into a single array. Here's an example:
```javascript
arr.map(x => x % 3 ? x/100 : []).reduce((acc, val) => acc.concat(val), []);
```
However, this approach can be less readable and maintainable than using `flatMap()`.

Latest run results:

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

User agent: Mozilla/5.0 (Linux; Android 12; DCO-LX9; HMSCore 6.12.2.302) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.88 HuaweiBrowser/14.0.2.311 Mobile Safari/537.36

Browser/OS: Chrome 99 on Android

View result in a separate tab

Test name	Executions per second
filter().map()	1901760.5 Ops/sec
flatMap()	182680.8 Ops/sec
map.flat	180410.6 Ops/sec

Related benchmarks:

flatMap() vs filter().map()asdsadsaasdsad (version: 0)

flatMap vs filter map

Comparing performance of: filter().map() vs flatMap() vs map.flat

Created: one year ago by: Guest

Jump to the latest result

filter().map()

flatMap()

map.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):