Benchmark: asfsdfc - MeasureThat.net

Tests:

asdasd
function flat(arr) { return arr.reduce((acc, elem) => { if (Array.isArray(elem)) { return [...acc, ...flat(elem)] } else { return [...acc, elem] } }, []) } const x = flat([ [1,2,3], [ [4,5,6], [7,8,9], [[[[[10,11,12]]], [13,14,15]]] ], [[[[[[[[[[[16,17,18]]]]]]]]]]] ]);
sdddd
const flatten = (arr) => arr.reduce((acc, x) => { Array.isArray(x) ? flatten(x).forEach(y => acc.push(y)) : acc.push(x) return acc; }, []) const x = flatten([ [1,2,3], [ [4,5,6], [7,8,9], [[[[[10,11,12]]], [13,14,15]]] ], [[[[[[[[[[[16,17,18]]]]]]]]]]] ]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
asdasd
sdddd

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36

Browser/OS: Chrome 123 on Windows

View result in a separate tab

Test name	Executions per second
asdasd	178933.9 Ops/sec
sdddd	200276.3 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmarking test.

**What is being tested?**

The provided JSON represents two JavaScript microbenchmarks, each measuring the performance of flattening an array with nested arrays using different approaches.

**Options compared:**

There are two options being compared:

1. **Flat/Reduce (asdasd)**: This approach uses the `flat()` method and a callback function to flatten the array.
2. **Flatten/Reduce (sdddd)**: This approach uses an arrow function with `flatten()` to flatten the array.

**Pros and Cons of each approach:**

1. **Flat/Reduce (asdasd)**
	* Pros:
		+ Can be more concise and expressive, as it uses a single method call.
		+ May be optimized by the JavaScript engine for performance.
	* Cons:
		+ Less readable, as it relies on the `flat()` method's behavior to flatten arrays.
2. **Flatten/Reduce (sdddd)**
	* Pros:
		+ More explicit and readable, as it uses an arrow function with a clear intention (flattening).
		+ May be easier to understand for developers familiar with functional programming concepts.
	* Cons:
		+ Requires the `flatten()` method, which may not exist in older JavaScript versions or browsers.

**Library usage:**

The `flatten()` method is used in both benchmarks. However, it's not a built-in JavaScript method. It's likely a utility function provided by a library (e.g., Lodash) to flatten arrays.

If you're interested in exploring alternative implementations of the `flatten()` method or using different libraries for flattening, you can search for existing alternatives online.

**Special JS features/syntax:**

There are no special JavaScript features or syntax used in these benchmarks. The code is written in standard ECMAScript syntax and does not include any experimental or proposed language features (e.g., async/await, decorators).

**Other alternatives:**

If you're looking for alternative approaches to flattening arrays, here are a few examples:

1. Using `Array.prototype.concat()`: You can use the `concat()` method to concatenate arrays instead of flattening.
2. Using `Array.prototype.slice()` and recursion**: You can use `slice()` to extract elements from an array and then recursively call itself to flatten nested arrays.
3. Using a library like Lodash's `flattenDeep()` function: If you're willing to include an external library, Lodash provides a robust flattening function that handles arrays with varying depths.

Keep in mind that these alternatives may have different performance characteristics or be more readable than the original implementations used in the benchmarks.

Related benchmarks:

asfsdfc (version: 1)

sdfsdfsdfsdf

Comparing performance of: asdasd vs sdddd

Created: 2 years ago by: Registered User

Jump to the latest result

asdasd

sdddd

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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