Benchmark: flatMap() vs filter().map() vs reduce() v5

Script Preparation code:

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

Tests:

filter().map()
arr.filter(x => x % 12 && x % 5 && 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 }, [])

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 break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares the performance of three different methods for filtering, mapping, and reducing an array:

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

These methods are all used to process a large array (`arr`) that is populated with values using a loop.

**Options Compared**

The benchmark compares the execution speed of each method for the following conditions:

* Filtering: Only values that meet certain conditions (i.e., multiples of 12, 5, and 3) are included in the processing.
* Mapping: The filtered values are then multiplied by 100.

**Pros and Cons**

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

1. `filter()` followed by `map()`:
	* Pros: Easy to understand and implement, can be parallelized for better performance.
	* Cons: Creates two intermediate arrays, which can lead to higher memory usage.
2. `flatMap()`:
	* Pros: Reduces the number of intermediate arrays created, can be more efficient in terms of memory usage.
	* Cons: Less intuitive for developers who are not familiar with this method.
3. `reduce()`:
	* Pros: Can be more efficient than filtering and mapping, as it uses a single loop to accumulate results.
	* Cons: May require additional setup and understanding of the reducer function.

**Library Used**

There is no explicit library used in this benchmark. However, it's worth noting that some browsers may have built-in optimizations or polyfills for these methods that could affect the results.

**Special JS Features/Syntax**

None mentioned.

**Other Considerations**

* The benchmark uses a large array (`arr`) to simulate a real-world scenario where data is processed in bulk.
* The `while` loop used to populate the array can introduce some variability in the results due to its nondeterministic nature.
* The benchmark is run on multiple browsers and devices, which helps to ensure that the results are representative of different use cases.

**Alternatives**

Other methods for filtering, mapping, and reducing arrays include:

1. `forEach()`: Similar to `map()` but does not return a new array.
2. `every()` and `some()`: Used for filtering with boolean conditions.
3. `Array.prototype.every()` and `Array.prototype.some()`: Similar to `every()` and `some()`, but chained methods that can be used with other methods like `map()` or `reduce()`.

For reducing arrays, additional libraries like Lodash or Ramda may be used for more complex reduction operations.

Keep in mind that the choice of method depends on the specific use case, performance requirements, and personal preference.

Related benchmarks:

flatMap() vs filter().map() vs reduce() v5 (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):