Benchmark: flatMap() vs filter().map() vs reduce() vs for-loop v1

Script Preparation code:

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

Tests:

filter().map()
arr.filter(x => x % 12).filter(x => x % 5).filter(x => x % 3).map(x => x/100)
flatMap()
arr.flatMap(x => x % 12 && x % 5 && x % 3 ? x/100 : [])
reduce()
arr.reduce((newArray, x) => { if (x % 12 && x % 5 && x % 3) { newArray.push(x / 100) } return newArray }, [])
for loop
let newArray = [] for(var i = 0; i < arr.length; i++){ if (arr[i] % 12 && arr[i] % 5 && arr[i] % 3) { newArray.push(arr[i] / 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

Latest run results:

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

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I'll provide an explanation of the provided JSON benchmark data, its options, pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is called "flatMap() vs filter().map() vs reduce() vs for-loop v1". It compares the performance of four different approaches:

1. `flatMap()`: A method that flattens an array by mapping each element to a new array.
2. `filter().map()`: A method that first filters out elements and then maps over the remaining array.
3. `reduce()`: A method that applies a reduction function to an accumulator and each element in the array, going from left to right, so as to reduce the array to a single output value.
4. `for-loop`: A traditional loop using a variable to iterate over the array.

**Benchmark Preparation Code**

The preparation code creates an array `arr` with 100000 elements, where each element is assigned a value using a while loop: `var i = 0; while (i <= 1E5) arr[i] = i++;`.

**Options Compared**

The benchmark compares the performance of the four approaches on the generated array. The tests are designed to filter out elements that do not meet certain conditions and then perform some operation on the remaining elements.

* `flatMap()`: Filters out elements and then maps over the remaining array, using a conditional expression (`x % 12 && x % 5 && x % 3 ? x/100 : []`).
* `filter().map()`: First filters out elements that do not meet the conditions (`arr.filter(x => x % 12).filter(x => x % 5).filter(x => x % 3)`), and then maps over the remaining array using a function (`x => x / 100`).
* `reduce()`: Applies a reduction function to an accumulator and each element in the array, going from left to right, pushing elements that meet the conditions into a new array (`arr.reduce((newArray, x) => { ... }`, initial value `[])`).
* `for-loop`: Iterates over the array using a traditional loop, filtering out elements that do not meet the conditions and pushing elements that do into a new array (`let newArray = []; for (var i = 0; i < arr.length; i++) { if (arr[i] % 12 && arr[i] % 5 && arr[i] % 3) { newArray.push(arr[i] / 100); } }`).

**Pros and Cons of Each Approach**

Here's a brief summary of the pros and cons of each approach:

* `flatMap()`: Pros: Concise, expressive syntax. Cons: May be less readable for those unfamiliar with this method.
* `filter().map()`: Pros: Easy to understand, widely used pattern. Cons: May incur unnecessary overhead due to function call overhead.
* `reduce()`: Pros: Suitable for reducing an array to a single output value. Cons: Can be complex and hard to debug.
* `for-loop`: Pros: Highly predictable, easy to optimize. Cons: May be less concise than other approaches.

**Other Considerations**

* The benchmark uses a Linux x86_64 device platform and Chrome 103 browser, which may impact the results.
* The tests are designed to filter out elements that do not meet certain conditions (`x % 12 && x % 5 && x % 3`), which affects the number of iterations.
* The benchmark does not account for potential side effects or concurrent execution.

**Alternatives**

Other alternatives to these approaches include:

* `forEach()`: An alternative to `for-loop`, but less predictable and harder to optimize.
* `Array.prototype.some()` with a callback function: A concise way to filter out elements, but may incur performance overhead.
* Using `Promise.all()` or `async/await` for concurrent execution: Can improve performance in some cases, but adds complexity.

Keep in mind that the choice of approach depends on the specific requirements and constraints of your use case.

Related benchmarks:

flatMap() vs filter().map() vs reduce() vs for-loop v1 (version: 0)

flatMap vs filter map vs reduce()

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

Created: 4 years ago by: Guest

Jump to the latest result

filter().map()

flatMap()

reduce()

for loop

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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