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

Script Preparation code:

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

Tests:

filter().map()
arr.filter(x => x % 3).map(x => x/100)
flatMap()
arr.flatMap(x => x % 3 ? x/100 : [])
reduce
arr.reduce((acc, n) => { if (n % 3) return n/100; return acc; }, []);
for loop
const doubled = [] for (const number of arr) { if (number % 3) doubled.push(number / 100) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

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

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the Benchmark Definition and its various test cases.

**Benchmark Overview**

The benchmark is designed to compare the performance of four different approaches to iterate over an array:

1. `flatMap()`
2. `filter().map()`
3. `reduce()`
4. `for` loop

Each approach will be tested by applying a specific transformation to the input array, which contains numbers from 0 to 100,000.

**Test Cases**

Here's a brief explanation of each test case:

1. **`flatMap()`**: This method applies an inner function to each element in the array and returns an array of results.
2. **`filter().map()`**: This approach uses `Array.prototype.filter()` to remove elements that don't meet a certain condition, followed by applying a transformation using `Array.prototype.map()`.
3. **`reduce()`**: This method applies a callback function to each element in the array and returns the accumulated result.
4. **`for` loop**: A traditional loop that iterates over the array using an index variable.

**Library Usage**

None of the test cases use any external libraries or frameworks. The transformations are built-in JavaScript methods or simple arithmetic operations.

**Special JS Features**

There is no special JavaScript feature or syntax used in these test cases. They rely solely on standard JavaScript language features and built-in methods.

**Options Comparison**

Here's a brief analysis of each approach, including their pros and cons:

* **`flatMap()`**: Pros: concise and expressive, efficient for simple transformations. Cons: may not be as intuitive for complex transformations.
* **`filter().map()`**: Pros: more flexible than `flatMap()`, can handle complex transformations using `map()`. Cons: less efficient due to the overhead of calling `Array.prototype.filter()` followed by `Array.prototype.map()`.
* **`reduce()`**: Pros: suitable for reducing arrays, can be used to accumulate results. Cons: may require more setup and understanding than other approaches.
* **`for` loop**: Pros: familiar and intuitive, allows for manual control over iteration. Cons: less concise and potentially slower due to the overhead of indexing.

**Other Alternatives**

Some alternative approaches that could be considered include:

* Using `Array.prototype.forEach()` instead of a traditional loop
* Utilizing more specialized libraries or frameworks, such as Array.prototype#every() or Array.prototype#some()
* Exploring other iteration methods, like using Web Workers for parallel processing

The benchmark can help determine which approach is most efficient and suitable for specific use cases.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36

Browser/OS: Chrome 103 on Linux

View result in a separate tab

Test name	Executions per second
filter().map()	452.6 Ops/sec
flatMap()	103.0 Ops/sec
reduce	1241.6 Ops/sec
for loop	1218.9 Ops/sec

Related benchmarks:

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

flatMap vs filter map vs reduce vs for looop

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

Created: 2 years ago by: Guest

Jump to the latest result

filter().map()

flatMap()

reduce

for loop

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):