Benchmark: flatMap vs filter.map vs reduce

Script Preparation code:

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

Tests:

filter().map()
arr.filter(x => x !== 0 && x % 3 && x !== 666).map(x => Math.round(x * Math.sqrt(x/100)))
flatMap()
arr.flatMap(x => (x !== 0 && x % 3 && x !== 666) ? Math.round(x * Math.sqrt(x/100)) : [])
reduce()
arr.reduce((acc, x) => (x !== 0 && x % 3 && x !== 666) ? [...acc, Math.round(x * Math.sqrt(x/100))] : acc, [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

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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 definition and test cases.

**Benchmark Definition**

The benchmark is testing three different approaches to transform an array of numbers:

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

All three approaches aim to apply a transformation function to each element in the array, but they differ in their approach.

**Options Compared**

* `flatMap()`: This method returns a new array with the results of applying the provided function on every element in the original array.
* `filter().map()`: This is a two-step approach where `filter()` removes elements that don't match the provided condition, and then `map()` applies the transformation function to the remaining elements.
* `reduce()`: This method executes a user-defined function on each element of the array (from left-to-right) so as to reduce it to a single output value.

**Pros and Cons**

* **flatMap():**
	+ Pros: More efficient for large arrays since it avoids creating an intermediate array with filtered elements.
	+ Cons: May not be suitable for smaller arrays or when the transformation function is expensive, as it still creates a new array.
* **filter().map():**
	+ Pros: Suitable for both small and large arrays. It's also easy to understand and maintain.
	+ Cons: Creates an intermediate array with filtered elements, which can be memory-intensive for large datasets.
* **reduce():**
	+ Pros: Can handle complex transformations and is suitable for smaller arrays or when the transformation function is expensive.
	+ Cons: May not be as efficient as `flatMap()` for large arrays due to the overhead of repeatedly calling the reduction function.

**Library Usage**

There are no explicit libraries used in this benchmark. However, some JavaScript features may be implicitly relied upon by certain approaches (e.g., the use of arrow functions in `filter().map()`).

**Special JS Features or Syntax**

The use of template literals (`x !== 0 && x % 3 && x !== 666`) and the spread operator (`...acc`) are examples of JavaScript features used in these test cases. The use of template literals allows for concise string interpolation, while the spread operator creates a new array by copying elements from an existing array.

**Other Alternatives**

For this specific benchmark, there aren't many alternatives that would be considered suitable replacements. However, some other approaches to transforming arrays might include:

* Using `forEach()` and applying the transformation function directly: This approach avoids creating intermediate arrays but may have performance implications due to the overhead of repeatedly calling a callback function.
* Utilizing `Array.prototype.forEach` with a custom loop or using `Array.prototype.forEachIndexed`: These alternatives can be more efficient than `map()` for certain use cases, but they might be less suitable for other scenarios.

In summary, this benchmark tests three common methods for transforming arrays in JavaScript: `flatMap()`, `filter().map()`, and `reduce()`. Each approach has its pros and cons, which are relevant to choosing the best method for specific use cases.

Related benchmarks:

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

flatMap vs filter map

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

Created: 4 years ago by: Guest

Jump to the latest result

filter().map()

flatMap()

reduce()

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):