Benchmark: flatMap() vs map().flat()

Script Preparation code:

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

Tests:

map().flat()
arr.map(x => [x/100]).flat()
flatMap()
arr.flatMap(x => [x/100])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

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/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
map().flat()	349.7 Ops/sec
flatMap()	488.4 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark compares the performance of two approaches to flatten an array in JavaScript: `map()` followed by `.flat()`, and `flatMap()`.

**Options Compared**

Two options are compared:

1. **`map()` followed by `.flat()`**: This approach creates a new array with the result of applying the mapping function (`x => [x/100]`) to each element of the original array, and then flattens the resulting array using the `.flat()` method.
2. **`flatMap()`**: This approach uses the `flatMap()` method, which is a more modern and concise way to flatten an array in JavaScript.

**Pros and Cons**

1. **`map()` followed by `.flat()`**:
	* Pros: This approach allows for more control over the transformation of each element in the original array.
	* Cons: It requires creating an intermediate array, which can be memory-intensive for large datasets.
2. **`flatMap()`**:
	* Pros: This approach is more concise and efficient, as it eliminates the need for an intermediate array.
	* Cons: It may not provide as much control over the transformation of each element in the original array.

**Library**

None

**Special JS Feature or Syntax**

No special features or syntax are used in this benchmark. However, `flatMap()` is a modern JavaScript feature that was introduced in ECMAScript 2019 (ES10).

**Benchmark Preparation Code**

The script preparation code creates an array `arr` with a large number of elements (`1E5`) and populates it with values from `0` to `100`. This creates a scenario where the performance difference between `map()` followed by `.flat()` and `flatMap()` can be observed.

**Individual Test Cases**

Two test cases are provided:

1. **`arr.map(x => [x/100]).flat()`**: This test case applies the mapping function (`x => [x/100]`) to each element of the original array using `map()`, and then flattens the resulting array using `.flat()`.
2. **`arr.flatMap(x => [x/100])`**: This test case uses the `flatMap()` method to flatten the original array directly.

**Latest Benchmark Result**

The latest benchmark result shows that:

* **`flatMap()`** performs better than **`map()` followed by `.flat()`**, with an execution rate of 119.8224868774414 executions per second compared to 54.77621841430664.
* The performance difference is likely due to the efficiency of the `flatMap()` method, which avoids creating an intermediate array.

**Other Alternatives**

If you want to explore alternative approaches or libraries, here are a few options:

1. **Use `reduce()`**: Instead of using `map()` and `.flat()`, you can use the `reduce()` method to flatten an array by reducing it to a single value.
2. **Use a library like Lodash**: If you want to explore more efficient or concise solutions, consider using a library like Lodash, which provides various utility functions, including ones for flattening arrays.
3. **Use a streaming approach**: For very large datasets, you may want to consider using a streaming approach, where data is processed in small chunks rather than all at once. This can help avoid memory issues and improve performance.

I hope this explanation helps!

Related benchmarks:

flatMap() vs map().flat() (version: 0)

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

Created: 4 years ago by: Guest

Jump to the latest result

map().flat()

flatMap()

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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