Benchmark: flatMap vs reduce v1.1

Script Preparation code:

var arrayOfArray = Array(1_000).fill(Array(1_000).fill(0))

Tests:

reduce
[...arrayOfArray].reduce((acc, arr) => { acc.push(...arr); return acc; }, [])
flatMap
arrayOfArray.flatMap(arr => arr)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
flatMap

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/139.0.0.0 Safari/537.36

Browser/OS: Chrome 139 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	255.6 Ops/sec
flatMap	96.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark is testing two methods: `reduce` and `flatMap`, which are part of the Array prototype in JavaScript. The purpose of this benchmark is to compare the performance of these two methods for a specific use case: flattening an array of arrays.

**Script Preparation Code**

The script preparation code creates a large array `arrayOfArray` filled with 1000 sub-arrays, each containing 1000 zeros. This is likely done to ensure that both methods are being tested on a substantial dataset.

**Html Preparation Code**

There is no HTML preparation code provided, which suggests that this benchmark is focused solely on JavaScript performance and does not require any specific web page layout or rendering.

**Individual Test Cases**

There are two test cases:

1. **"reduce"`**: This test case uses the `reduce` method to flatten the array of arrays. The implementation passes an accumulator (`acc`) to which it pushes each element from the current sub-array, and then returns the updated accumulator.
2. **"flatMap"`**: This test case uses the `flatMap` method to flatten the same array of arrays. The implementation takes a callback function that returns each element of the current sub-array.

**Library Usage**

There is no explicit library usage mentioned in the benchmark definition or individual test cases.

**Special JS Features/Syntax**

Neither the `reduce` nor `flatMap` methods use any special JavaScript features or syntax.

**Pros and Cons of Approaches**

Here's a brief overview of the pros and cons of each approach:

1. **"reduce"`**:
	* Pros:
		+ More control over the flattening process, as it allows the developer to specify an accumulator function.
		+ Can be more memory-efficient for large datasets, since it avoids creating intermediate arrays.
	* Cons:
		+ Requires more code and logic to implement the accumulator function.
		+ May have higher overhead due to the need to manage the accumulator variable.
2. **"flatMap"`**:
	* Pros:
		+ More concise and straightforward implementation compared to `reduce`.
		+ Often faster due to the optimized implementation in modern JavaScript engines.
	* Cons:
		+ Less control over the flattening process, as it relies on the internal implementation of `flatMap`.

**Other Alternatives**

If you need to flatten an array of arrays but don't want to use either `reduce` or `flatMap`, some alternative approaches include:

1. Using a simple loop: You can use a traditional loop to iterate over each sub-array and push its elements into a new array.
2. Utilizing `Array.prototype.flat()` (available in modern browsers and Node.js): This method is designed specifically for flattening arrays and can be more efficient than custom implementations.

In summary, the benchmark is comparing the performance of two methods for flattening an array of arrays: `reduce` and `flatMap`. While both approaches have their pros and cons, `flatMap` is often preferred due to its conciseness and potential speed advantages.

Related benchmarks:

flatMap vs reduce v1.1 (version: 7)

Comparing performance of: reduce vs flatMap

Created: 3 years ago by: Registered User

Jump to the latest result

reduce

flatMap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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