Benchmark: flatMap vs reduce vs loop performance (version: 1)

Script Preparation code:

var arr = Array.from({length:100000}, (v, i) => ({name: i, shouldNotBeFiltered: i % 2 === 0}));

Tests:

flatMap
arr.flatMap(({ name, shouldNotBeFiltered }) => (shouldNotBeFiltered ? [name] : []));
reduce
arr.reduce((acc, { name, shouldNotBeFiltered }) => { if (shouldNotBeFiltered) { const elementToPush = name; acc.push(elementToPush); } return acc; }, []);
loop
const result = []; for (const { name, shouldNotBeFiltered } of arr) { if (shouldNotBeFiltered) { const elementToPush = name; result.push(elementToPush); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap
reduce
loop

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down what's being tested in the provided JSON benchmark.

**What is being tested?**

The benchmark is comparing the performance of three different approaches to filter an array:

1. `flatMap`
2. `reduce`
3. `loop` (a traditional for loop)

Each approach is designed to push elements from the filtered array into a result array. The tests aim to measure which approach is the most efficient.

**Options compared:**

The benchmark compares the performance of these three approaches:

* **`flatMap`**: A built-in JavaScript method that flattens an array by mapping over it and returning an array with the results.
* **`reduce`**: A built-in JavaScript method that applies a reduction function to each element in the array, accumulating a result.
* **`loop`**: A traditional for loop that iterates over the filtered array and pushes elements into the result array.

**Pros and Cons of each approach:**

* **`flatMap`**:
	+ Pros:
		- Most concise and readable implementation
		- Built-in method, so it's likely to be optimized by JavaScript engines
	+ Cons:
		- May have overhead due to function call and return statements
		- Not as control-oriented as `reduce` or `loop`
* **`reduce`**:
	+ Pros:
		- More control-oriented than `flatMap`, allowing for custom accumulator logic
		- Built-in method, so it's likely to be optimized by JavaScript engines
	+ Cons:
		- May have more overhead due to function call and return statements
		- Can be less readable if not used correctly
* **`loop`**:
	+ Pros:
		- Most control-oriented approach, allowing for fine-grained control over iteration
		- No function call or return statement overhead
	+ Cons:
		- Least concise and readable implementation
		- May require more maintenance due to its verbosity

**Library usage:**

None of the provided benchmark code uses any external libraries. All implementations are pure JavaScript.

**Special JS features or syntax:**

The benchmark doesn't explicitly mention any special JavaScript features or syntax that might be relevant to this comparison (e.g., async/await, generator functions, etc.). The focus is on the performance difference between these three approaches.

**Other alternatives:**

If you'd like to explore alternative approaches, some additional options could include:

* Using `Array.prototype.filter()` instead of `flatMap` or `reduce`
* Implementing a custom filtering function using arrow functions or regular expressions
* Using other libraries like Lodash or Underscore.js for array manipulation

However, the benchmark is already focused on comparing these three core approaches (`flatMap`, `reduce`, and `loop`), which should provide a good starting point for understanding performance differences.

Latest run results:

Run details: (Test run date: 5 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36

Browser/OS: Chrome 125 on Windows

View result in a separate tab

Test name	Executions per second
flatMap	583.8 Ops/sec
reduce	2025.4 Ops/sec
loop	2816.1 Ops/sec

Related benchmarks:

flatMap vs reduce vs loop performance (version: 1) (version: 0)

Comparing performance of: flatMap vs reduce vs loop

Created: 5 months ago by: Guest

Jump to the latest result

flatMap

reduce

loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):