Benchmark: flatMap/reduce/loop

Script Preparation code:

var arr = [];
var i = 0;

while (i <= 1E6) arr[i] = i++;

Tests:

flatMap nested
arr.flatMap(i => i % 3 ? [i] : [])
flatMap
arr.flatMap(i => i % 3 ? i : [])
reduce
arr.reduce((a, i) => { if (i % 3) { a.push(i) } return a }, [])
loop
const newArr = [] for (const i of arr) { if (i % 3) { newArr.push(3) } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
flatMap nested
flatMap
reduce
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 break down the provided JSON and explain what's being tested, compared options, pros and cons of those approaches, and other considerations.

**Benchmark Definition**

The benchmark is defined by the `Script Preparation Code`, which initializes an array `arr` with 1 million elements using a while loop. The array is then modified using three different methods: `flatMap`, `reduce`, and a simple loop.

* `flatMap`: Creates a new array with the same number of elements as the original array, but only includes elements that pass a test.
* `reduce`: Applies a function to each element in the array, reducing it to a single value.
* Simple Loop: Iterates over the array using a for loop and pushes elements to a new array.

**Test Cases**

Each test case uses a different approach to modify the array. Here's what's being tested:

1. `flatMap nested`: Uses `flatMap` with an arrow function that checks if each element is divisible by 3, and if so, creates a new array with that element.
2. `flatMap`: Uses `flatMap` with an arrow function that simply returns the element if it's not divisible by 3.
3. `reduce`: Uses `reduce` with an arrow function that pushes elements to a new array if they're divisible by 3.
4. Simple Loop: Iterates over the array using a for loop and pushes elements to a new array if they're divisible by 3.

**Options Compared**

The options being compared are:

* `flatMap`: Creates a new array with filtered elements, which can be slower due to the overhead of creating a new array.
* `reduce`: Applies a function to each element in the array, reducing it to a single value. This approach is often faster but may not produce the same results as `flatMap` if the reducer function has side effects.
* Simple Loop: Iterates over the array using a for loop and pushes elements to a new array. This approach can be slower due to the overhead of the loop.

**Pros and Cons**

Here's a brief summary of each option:

* `flatMap`:
	+ Pros: Can be faster due to the use of the `Array.prototype.flatMap()` method, which is optimized for performance.
	+ Cons: Creates a new array with filtered elements, which can consume more memory.
* `reduce`:
	+ Pros: Can be faster and more concise than `flatMap`.
	+ Cons: May produce different results if the reducer function has side effects, and can be slower due to the overhead of applying a function to each element.
* Simple Loop:
	+ Pros: Can be easier to understand for those familiar with traditional loops.
	+ Cons: Can be slower due to the overhead of the loop.

**Other Considerations**

When choosing between these options, consider the following factors:

* Performance: If speed is critical, `flatMap` or the simple loop might be a better choice. However, if conciseness and ease of use are more important, `reduce` could be the way to go.
* Memory usage: If memory is a concern, `flatMap` might create an unnecessary new array, while the simple loop will only allocate memory for the new array as needed.
* Code readability: If code readability is crucial, the simple loop or `reduce` with a clear reducer function might be a better choice.

**Libraries and Special JS Features**

No libraries are used in this benchmark. However, it's worth noting that `flatMap` is supported by modern browsers and Node.js versions starting from 8.0.

There are no special JavaScript features used in this benchmark.

I hope this explanation helps software engineers understand the testing framework and options being compared in the MeasureThat.net benchmark.

Related benchmarks:

flatMap/reduce/loop (version: 0)

Comparing performance of: flatMap nested vs flatMap vs reduce vs loop

Created: 2 years ago by: Guest

Jump to the latest result

flatMap nested

flatMap

reduce

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):