Benchmark: JOSH's flatMap() vs map().filter()

Script Preparation code:

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

Tests:

map().filter()
arr.map(x => x * 2).filter(x => x % 3 === 0)
flatMap()
arr.flatMap(x => x % 3 === 0 ? [x * 2] : [])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36

Browser/OS: Chrome 108 on Linux

View result in a separate tab

Test name	Executions per second
map().filter()	311.3 Ops/sec
flatMap()	82.6 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to help explain the JavaScript microbenchmark on MeasureThat.net.

**Benchmark Definition**

The benchmark measures the performance difference between two approaches: using `map()` with a callback function followed by `filter()`, and using `flatMap()` directly. The test case creates an array of 100,000 elements using a loop and initializes it as `arr`.

**Options Compared**

Two options are compared:

1. **`map().filter()`**: This approach uses the `map()` method to transform each element in the array, applying a callback function that multiplies each number by 2. The resulting transformed array is then passed to the `filter()` method to filter out elements whose product is not divisible by 3.
2. **`flatMap()`**: This approach uses the `flatMap()` method directly on the original array, which flattens an array of arrays into a single array and applies the same callback function as in the first option.

**Pros and Cons**

### `map().filter()`

Pros:

* Easy to understand and implement
* Well-supported by most JavaScript engines

Cons:

* May incur additional overhead due to intermediate results (transformed array)
* Can be slower compared to native functions like `flatMap()` if performance is critical

### `flatMap()`

Pros:

* Native function, optimized for performance
* Reduces intermediate results, making it potentially faster

Cons:

* Less intuitive and less supported by older JavaScript engines or libraries that don't understand `flatMap()`
* May require more careful handling of edge cases (e.g., handling empty arrays)

**Library/Function Used**

`map()` and `filter()` are built-in methods in JavaScript. `flatMap()`, on the other hand, was introduced in ECMAScript 2019.

**Special JS Feature/Syntax**

This benchmark uses the following special feature:

* **Arrow functions**: The callback functions used in both test cases are defined using arrow functions (`x => x * 2`), which were introduced in ECMAScript 2015.
* **Template literals**: The `arr[i] = i++` assignment statement uses template literals, which provide a concise way to embed expressions inside string literals.

**Other Alternatives**

If you need to optimize performance-critical code, other alternatives might include:

1. **Native WebAssembly (WASM) functions**: If supported by your JavaScript engine or runtime.
2. **Specialized libraries**: Libraries like `mathjs` or `numjs`, which offer optimized mathematical operations.
3. **Alternative programming paradigms**: Consider using languages with native support for parallelism, concurrency, or functional programming (e.g., Rust, Haskell).

Keep in mind that these alternatives might require changes to your code and might not be as widely supported as native JavaScript functions.

Related benchmarks:

JOSH's flatMap() vs map().filter() (version: 0)

flatMap vs map filter

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

Created: one year ago by: Guest

Jump to the latest result

map().filter()

flatMap()

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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