Benchmark: flatMap() vs filter().map() 2

Script Preparation code:

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

Tests:

filter().map()
arr.filter(x => x % 3).map(x => x/100)
flatMap()
arr.flatMap(x => x % 3 ? x/100 : null)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

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 and explain what's being tested.

**Benchmark Definition**

The benchmark is designed to compare two approaches: `filter().map()` and `flatMap()`. Both methods are used to process an array of numbers, filtering out elements that meet a certain condition (in this case, numbers that leave a remainder when divided by 3) and then mapping the remaining elements to their quotient when divided by 100.

**Options being compared**

The two options being compared are:

1. `filter().map()`
2. `flatMap()`

These methods differ in how they handle the filtering and mapping process.

**Pros and Cons of each approach:**

1. `filter().map()`:
	* Pros:
		+ More explicit and readable code
		+ Can be useful when you need to perform additional logic before mapping (e.g., adding a prefix or suffix)
	* Cons:
		+ Creates an intermediate array that needs to be traversed, which can lead to performance issues for large datasets
2. `flatMap()`:
	* Pros:
		+ Returns an iterator directly, avoiding the need for an intermediate array and potentially reducing memory usage
		+ Can be more efficient when dealing with large datasets
	* Cons:
		+ May require additional library support or understanding of its internal workings

**Library used**

In this benchmark, `flatMap()` is a built-in JavaScript method introduced in ECMAScript 2019 (ES2020). It's part of the Array prototype.

**Other considerations:**

When choosing between `filter().map()` and `flatMap()`, consider the following:

* If you need to perform additional logic before mapping, `filter().map()` might be a better choice.
* If you're working with large datasets and want to optimize performance, `flatMap()` could be a more efficient option.

**Alternative approaches**

While `flatMap()` is not commonly used in older browsers or JavaScript environments without built-in support, other alternatives can be employed:

1. Using a library like Lodash (which has a `flatMap` function) or Ramda (which has an `over` function that achieves similar behavior)
2. Implementing the logic manually using `reduce()`, `forEach()`, and `map()`
3. Utilizing Web Workers for parallel processing, which can help optimize performance

**Additional context**

In older browsers or environments without built-in support for `flatMap()`, the alternative approaches would be more complex and may require additional libraries or custom implementations.

I hope this explanation helps software engineers understand the benchmark's purpose and compare these two methods effectively!

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/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter().map()	557.0 Ops/sec
flatMap()	150.2 Ops/sec

Related benchmarks:

flatMap() vs filter().map() 2 (version: 0)

flatMap vs filter map

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

Created: one year ago by: Guest

Jump to the latest result

filter().map()

flatMap()

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):