Benchmark: flatMap() vs filter().map() vs reduce() test

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1E7) arr[i] = i++;

Tests:

filter().map()
arr.filter(x => x % 2).map(x => x/100)
flatMap()
arr.flatMap(x => x % 2 ? x/100 : [])
reduce()
arr.reduce((newArray, x) => { if (x % 2) { newArray.push(x / 100) } return newArray }, [])

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:

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/109.0.0.0 Safari/537.36

Browser/OS: Chrome 109 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
filter().map()	2.0 Ops/sec
flatMap()	1.1 Ops/sec
reduce()	4.4 Ops/sec

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 definition and test cases.

**Benchmark Definition:**

The benchmark is comparing three different approaches to flatten an array:

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

Each approach has its own strengths and weaknesses, which we'll discuss later.

**Test Cases:**

There are three individual test cases, each representing one of the three approaches mentioned above. Here's a brief description of each:

1. `filter().map()`: This approach uses the `filter()` method to remove elements that don't meet a certain condition (in this case, numbers divisible by 2), and then applies the `map()` method to transform the remaining elements.
2. `flatMap()`: This approach uses the `flatMap()` method, which is a more concise way of flattening an array compared to the traditional `filter().map()` approach.
3. `reduce()`: This approach uses the `reduce()` method to accumulate the transformed elements into a single output array.

**Library:**

None of the test cases explicitly use any external libraries beyond what's built-in to JavaScript (e.g., `Array.prototype.filter()`, `Array.prototype.map()`, etc.).

**Special JS Features/Syntax:**

There are no special JavaScript features or syntax used in these test cases. However, it's worth noting that some browsers might optimize the execution of certain methods (e.g., `flatMap()`), which could affect the benchmark results.

**Pros and Cons of Each Approach:**

Here's a brief summary:

1. **flatMap()**: This approach is more concise and efficient than traditional `filter().map()`, as it avoids the overhead of multiple function calls. However, its performance might be affected by the browser's optimization for this method.
	* Pros: More concise, potentially faster
	* Cons: Might rely on browser optimizations
2. **filter().map()**: This approach is more explicit and easier to understand than `flatMap()`. It also avoids relying on browser optimizations.
	* Pros: Easier to understand, avoids browser optimization dependencies
	* Cons: Less concise, potentially slower
3. **reduce()**: This approach can be less efficient due to the overhead of accumulating elements into an accumulator array. However, it provides more flexibility and control over the transformation process.
	* Pros: More flexible, easier to customize
	* Cons: Potentially slower, less concise

**Other Considerations:**

When comparing these approaches, consider factors like:

* Code readability and maintainability
* Browser support and optimization
* Potential performance differences
* Control over the transformation process

In this benchmark, the results suggest that `flatMap()` might be slightly faster than `filter().map()`, but the difference is relatively small. The `reduce()` approach is slower due to its accumulation overhead.

**Alternatives:**

If you're looking for alternatives or more specialized approaches:

* Instead of `flatMap()`, you could use `Array.prototype.map()` and then apply a transformation function to each element (e.g., using `map((x) => x/100)`).
* For custom transformations, consider using a combination of `filter()`, `map()`, and other array methods.
* If performance is critical, you might want to explore optimized libraries or frameworks that provide more efficient array manipulation functions.

Related benchmarks:

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

flatMap vs filter map vs reduce()

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

Created: one year ago by: Guest

Jump to the latest result

filter().map()

flatMap()

reduce()

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):