Benchmark: flatMap() vs filter().map() vs reduce vs for with indexed for

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 : [])
reduce
arr.reduce((acc, n) => { if (n % 3) return n/100; return acc; }, []);
for loop
const doubled = [] const len = arr.length for (let i = 0; i < len; ++i) { const number = arr[i]; if (number % 3) doubled.push(number / 100) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter().map()
flatMap()
reduce
for loop

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents a benchmark comparison between four different approaches: `flatMap()`, `filter().map()`, `reduce`, and `for` loop.

**Benchmark Definition**

The benchmark is designed to test how each approach performs when filtering, mapping, and reducing an array. The input data consists of an array `arr` with 100 elements, where each element is the number itself. The goal is to apply a transformation to this array, applying the modulo operator (`%`) to filter out odd numbers, then dividing the result by 100.

**Options Compared**

The benchmark compares the performance of four different approaches:

1. `flatMap()`: This method applies the callback function to each element in the array, returning an array with the same length as the original array, but with elements transformed.
2. `filter().map()`: This approach uses `Array.prototype.filter()` to create a new array with only the elements that pass the test implemented by the provided function (in this case, the modulo operator), and then uses `Array.prototype.map()` to apply the transformation to each remaining element.
3. `reduce()`: This method applies a callback function to each element in the array, accumulating a value as it iterates through the array.
4. `for` loop: A traditional approach using a loop to iterate over the elements of the array and perform the transformation.

**Pros and Cons**

Here's a brief summary of each approach:

1. **flatMap()**: This method is concise and efficient, but its performance can be affected by the number of elements in the original array.
2. **filter().map()**: This approach is more verbose, but it avoids modifying the original array, which can lead to better performance for large datasets.
3. **reduce()**: This method is concise and easy to read, but its performance can be slower than other approaches due to the overhead of accumulating a value.
4. **for` loop**: This traditional approach provides explicit control over the iteration process, but it can be more verbose and error-prone.

**Libraries and Special Features**

None of the provided code snippets use any external libraries or special JavaScript features beyond the standard Array methods (e.g., `filter()`, `map()`, `reduce()`).

**Other Alternatives**

If you're looking for alternative approaches, consider:

1. Using `Array.prototype.forEach()` with a callback function to iterate over the array.
2. Employing a more functional programming style using libraries like Lodash or Ramda.
3. Utilizing parallel processing techniques using Web Workers or async/await.

When choosing an approach, consider factors such as performance requirements, code readability, and maintainability.

I hope this explanation helps you understand the benchmark and its various approaches!

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Comparing performance of: filter().map() vs flatMap() vs reduce vs for loop

Created: 3 years ago by: Guest

Jump to the latest result

filter().map()

flatMap()

reduce

for loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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