Benchmark: Ctrl: flatMap() vs filter().map() vs reduce() v2

Script Preparation code:

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

Tests:

filter & map
arr.filter(x => x % 5 && x % 3).map(x => x/100)
flatMap
arr.flatMap(x => (x % 5 && x % 3) ? x/100 : [])
reduce
arr.reduce((newArray, x) => { if (x % 5 && x % 3) { 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 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Linux

View result in a separate tab

Test name	Executions per second
filter & map	481.7 Ops/sec
flatMap	140.6 Ops/sec
reduce	542.4 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the provided benchmark definition and explain what's being tested, the options compared, their pros and cons, and other considerations.

**Benchmark Definition:**
The benchmark is testing three different approaches for filtering and transforming an array of numbers:

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

**Script Preparation Code:**
The script starts by creating a large array `arr` with values ranging from 0 to 100,000 using a while loop.

**Html Preparation Code:**
There is no HTML preparation code provided, which suggests that the benchmark is focused on JavaScript performance testing only.

**Individual Test Cases:**

### Filter & Map

* Benchmark Definition:
```javascript
arr.filter(x => x % 5 && x % 3).map(x => x/100)
```
This test case uses the `filter()` method to remove numbers that are not divisible by both 5 and 3, and then applies the `map()` method to divide the remaining numbers by 100.

### FlatMap

* Benchmark Definition:
```javascript
arr.flatMap(x => (x % 5 && x % 3) ? x/100 : [])
```
This test case uses the `flatMap()` method to remove numbers that are not divisible by both 5 and 3, and then flattens the resulting array into a single-level array.

### Reduce

* Benchmark Definition:
```javascript
arr.reduce((newArray, x) => {
  if (x % 5 && x % 3) {
    newArray.push(x / 100)
  }
  return newArray
}, [])
```
This test case uses the `reduce()` method to build a new array with numbers that are divisible by both 5 and 3, and then divides those numbers by 100.

**Pros and Cons:**

* `flatMap()`: This approach is concise and efficient, as it avoids creating intermediate arrays. However, it may not be supported in older browsers or JavaScript engines.
* `filter()` + `map()`: This approach is widely supported, but it creates intermediate arrays, which can lead to performance issues for large datasets.
* `reduce()`: This approach is efficient, as it only requires a single pass through the array. However, it may require more memory than the other approaches.

**Library:**
There is no library mentioned in the benchmark definition.

**Special JS Feature or Syntax:**
None of the test cases use any special JavaScript features or syntax beyond standard ES6+ syntax.

**Other Considerations:**

* The benchmark uses a large array to simulate real-world performance scenarios.
* The test cases are designed to compare the performance of different approaches for filtering and transforming arrays.
* The benchmark results can be used to compare the performance of different browsers, JavaScript engines, or devices.

**Alternatives:**
There are other alternatives for testing array transformations, such as:

* Using `Array.prototype.forEach()` with a callback function
* Using a custom iterator or generator function
* Using a third-party library like Lodash or Ramda

Related benchmarks:

Ctrl: flatMap() vs filter().map() vs reduce() v2 (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):