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

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:

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Definition**

The benchmark measures the performance of three different approaches: `flatMap()`, `filter()` and `map()`, and `reduce()`. The script preparation code creates an array `arr` with 100,000 elements using a while loop. Each test case applies a filter condition to the array using one of the three approaches, then maps or reduces the resulting filtered array.

**Approaches Compared**

1. **flatMap()**: This method combines filtering and mapping into a single operation. It returns a new array with the same length as the original array, containing only the elements that pass the test.
2. **Filter() & Map()**: This approach first filters the array using `filter()` and then maps each element to a new value using `map()`. The resulting array is a new array with different elements than the original array.
3. **Reduce()**: This method applies a reduction function to each element of the array, accumulating an output value.

**Pros and Cons**

1. **flatMap()**:
	* Pros: Efficient use of memory (returns a new array with the same length as the original), can be faster for large datasets.
	* Cons: May not be suitable for small datasets or arrays with few elements, as it creates unnecessary intermediate arrays.
2. **Filter() & Map()**:
	* Pros: Suitable for small datasets or arrays with few elements, as it avoids creating unnecessary intermediate arrays.
	* Cons: May be slower than `flatMap()` due to the overhead of creating multiple array operations.
3. **Reduce()**:
	* Pros: Can be more efficient than `map()` and `filter() & map()` for large datasets, as it only requires a single pass through the array.
	* Cons: May not be suitable for all use cases, as the reduction function must be carefully designed to produce the desired output.

**Library Used**

None. The benchmark does not use any external libraries or dependencies beyond JavaScript's built-in functionality.

**Special JS Feature or Syntax**

The benchmark uses a feature of modern JavaScript called "arrow functions" (`=>`) which are concise ways to define small, single-purpose functions. This is used in the filter and map operations.

**Considerations**

When choosing between these approaches, consider the size of your dataset, the complexity of your filtering and mapping logic, and the memory constraints of your application.

**Other Alternatives**

Some alternative approaches you might consider include:

* Using `forEach()` instead of `map()`
* Using a `Set` or other data structure to store unique elements
* Using a more efficient algorithm for reducing an array (e.g. using a single loop and accumulator variables)

In general, the choice of approach will depend on the specific requirements of your use case and the characteristics of your dataset.

Related benchmarks:

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

flatMap() vs filter() & map() vs reduce()

Comparing performance of: filter & map vs flatMap vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filter & map

flatMap

reduce

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):