Benchmark: flatMap vs mapFlat

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1E5) arr[i] = i++;

Tests:

flatMap
arr.flatMap(x => x % 3 ? x/100 : [])
mapFlat
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
flatMap
mapFlat

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Definition**

The benchmark is designed to compare two different approaches: `flatMap` and `map Flat`. The goal is to measure the performance difference between these two methods.

**Script Preparation Code**

The script preparation code creates an array `arr` with 100,000 elements. This will be used as input for the microbenchmarking tests.

```javascript
var arr = [];
var i = 0;

while (i <= 1E5) arr[i] = i++;
```

This code uses a simple loop to populate the array, which is not optimized for performance. In a real-world scenario, you'd want to use a more efficient data structure and initialization method.

**Html Preparation Code**

There is no HTML preparation code provided, so we'll assume that the benchmark runs directly in the browser without any additional overhead.

**Individual Test Cases**

The benchmark consists of two test cases:

1. **flatMap**
```javascript
arr.flatMap(x => x % 3 ? x/100 : [])
```
This method uses `flatMap` to flatten an array of arrays, where each inner array contains a boolean value and either the original number or an empty array.

2. **mapFlat**
```javascript
arr.map(x => x % 3 ? x/100 : []).flat(1)
```
This method uses `map` followed by `flat` to achieve the same result as the `flatMap` method.

**Library Usage**

In both test cases, a library function is used:

* In the `flatMap` case, `Array.prototype.flatMap()` is used.
* In the `mapFlat` case, the spread operator (`...`) and `Array.prototype.flat()` are used.

The purpose of these libraries is to provide more concise and expressive ways to manipulate arrays. However, in this microbenchmarking context, we're only interested in the performance differences between different approaches.

**JavaScript Features**

There are no special JavaScript features or syntax mentioned in the benchmark definition or test cases. We can focus on the standard language features without any additional complexities.

**Alternatives**

If you want to compare alternative methods, here are a few options:

* Use `reduce` instead of `flatMap`: `arr.reduce((acc, x) => acc.concat(x % 3 ? x/100 : []))`
* Use a custom implementation without using built-in library functions: `let result = []; for (const x of arr) { if (x % 3 === 0) result.push(x/100); } return result;`

Keep in mind that these alternatives may have different performance characteristics and might not be as efficient as the original `flatMap` and `mapFlat` implementations.

**Pros and Cons**

Here are some pros and cons of each approach:

* **Original `flatMap` method**: Pros - concise and expressive, likely to be highly optimized by engines. Cons - may be slower than alternative methods due to library overhead.
* **Original `map Flat` method**: Pros - avoids unnecessary library functions, potentially faster than original `flatMap`. Cons - less concise and expressive than the original `flatMap`.
* **Custom implementation using reduce**: Pros - avoids library function overhead, potentially faster. Cons - more verbose and less efficient than the original `flatMap`.

Ultimately, the choice of approach depends on your specific use case, performance requirements, and personal preference.

Let me know if you have any further questions or need clarification!

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36

Browser/OS: Chrome 109 on Windows

View result in a separate tab

Test name	Executions per second
flatMap	68.9 Ops/sec
mapFlat	66.8 Ops/sec

Related benchmarks:

flatMap vs mapFlat (version: 0)

Comparing performance of: flatMap vs mapFlat

Created: one year ago by: Guest

Jump to the latest result

flatMap

mapFlat

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):