Benchmark: filterMap vs reduce

Tests:

filterMap
const arr = new Array(52400).fill(null).map(() => Math.random()); for(let i = 0; i < 1000; i++) { const res = arr.filter(el => el % 2 === 0).map(el => el + 1).sort((a, b) => a - b); }
reduce
const arr = new Array(52400).fill(null).map(() => Math.random()); for(let i = 0; i < 1000; i++) { const res = arr.reduce((acc, el) => { if(el % 2 === 0) { acc.push(el + 1); } return acc; }, []).sort((a, b) => a - b); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filterMap
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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Let's break down what's being tested in this JavaScript microbenchmark.

**Benchmark Definition**

The benchmark is comparing two approaches to process an array of random numbers: `filterMap` and `reduce`.

**Options Compared**

The benchmark is testing the following options:

* `filterMap`: Using the `Array.prototype.filter()` method to filter out odd numbers, then using `Array.prototype.map()` to increment each remaining number by 1, and finally sorting the resulting array.
* `reduce`: Using the `Array.prototype.reduce()` method to accumulate an array of incremented even numbers.

**Pros and Cons**

* **filterMap**:
	+ Pros: Simple, readable code, easy to understand. It's a common pattern in JavaScript for filtering and mapping arrays.
	+ Cons: May be slower than `reduce` due to the overhead of calling `map()` multiple times, as it creates new arrays in each iteration.
* **reduce**:
	+ Pros: Can potentially be faster since it avoids creating new arrays by accumulating results directly into the accumulator. This approach can also reduce memory allocation.
	+ Cons: Code may be less readable and more difficult to understand for those unfamiliar with `reduce()`. It requires a different mindset to process data using an accumulator.

**Library/External Functionality**

Neither of these options uses any external libraries or functions beyond built-in JavaScript methods. However, it's worth noting that if you're using ES6+ syntax, the `async/await` keywords might be used in some scripts, but this is not related to the specific problem being tested.

**Special JS Features/Syntax**

Neither of these options uses any special JavaScript features or syntax beyond what's described above. However, it's worth noting that modern JavaScript engines like WebAssembly, and Node.js might introduce new concepts such as async and await, which would be difficult to test in a simple benchmark.

**Alternatives**

Other alternatives for processing arrays include:

* **forEach()**: A method that executes the provided function on each element of an array. It's not typically used for filtering or mapping data.
* **for...of loops**: Loops that iterate over arrays without needing explicit indexing. They can be a good alternative to `reduce()` and other accumulator-based methods.
* **Array.prototype.every()`, `Array.prototype.some()`: Methods that check if all/any elements in an array pass a certain condition, respectively.

In conclusion, this benchmark is comparing two simple approaches for processing arrays: filtering out odd numbers, mapping each remaining number by 1, and sorting the resulting array. Both methods have their pros and cons, and understanding these differences can help developers choose the most suitable approach depending on performance requirements and code readability preferences.

Related benchmarks:

filterMap vs reduce (version: 0)

Comparing performance of: filterMap vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filterMap

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):