Benchmark: 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 % 3).map(x => x/100)
flatMap()
arr.flatMap(x => x % 3 ? x/100 : [])
reduce()
arr.reduce((acc, x) => { if (x % 3) acc.push(x/100) return acc }, [])

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):

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I'd be happy to explain the provided JavaScript microbenchmark.

**Benchmark Overview**

The benchmark compares three different approaches for transforming an array:

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

These transformations are applied to a large array, which is initialized with values from 0 to 100,000.

**Options Compared**

*   **`filter().map()`**: This approach uses the `Array.prototype.filter()` method to remove elements that don't meet a certain condition (in this case, numbers divisible by 3) and then applies the `Array.prototype.map()` method to transform the remaining elements.
*   **`flatMap()`**: This approach uses the `Array.prototype.flatMap()` method to transform the array in a single step. Unlike `map()`, which returns an array of arrays, `flatMap()` flattens the resulting array.
*   **`reduce()`**: This approach uses the `Array.prototype.reduce()` method to accumulate transformed elements into an accumulator array.

**Pros and Cons**

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

### Filter().map()

**Pros:**

*   Easy to understand and implement
*   Can be parallelized more easily

**Cons:**

*   More memory-intensive due to the creation of intermediate arrays
*   Slower than `flatMap()` for large inputs

### FlatMap()

**Pros:**

*   More memory-efficient, as it avoids creating intermediate arrays
*   Faster than `filter().map()` for large inputs

**Cons:**

*   Less intuitive and less widely used compared to other methods
*   May require more expertise in optimizing flat map usage

### Reduce()

**Pros:**

*   Can be parallelized easily using Web Workers or worker threads
*   Good for accumulating values, as it can modify the accumulator directly

**Cons:**

*   More complex and harder to understand for some developers
*   Requires proper handling of edge cases and initial values

**Library Usage**

The provided benchmark doesn't explicitly use any external libraries. However, if the `Array.prototype` methods (`filter()`, `map()`, and `reduce()`) were implemented in a custom way or with different optimizations, that would be an additional consideration.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in this benchmark.

**Other Alternatives**

If you wanted to explore alternative approaches for transforming arrays, here are some examples:

*   **`Array.prototype.forEach()`**: Instead of creating new arrays or modifying the original array, you can use `forEach()` to perform an action on each element.
*   **`Set()`**: You could also transform elements into a `Set`, which is optimized for fast lookup and insertion operations. However, this approach would require careful handling of duplicate values.
*   **Web Workers or worker threads**: As mentioned earlier, parallelizing array transformations using Web Workers or worker threads can provide significant performance improvements.

Keep in mind that these alternative approaches might have different trade-offs in terms of memory usage, computation complexity, and readability.

Related benchmarks:

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):