Benchmark: flatten - MeasureThat.net

Script Preparation code:

const arr = [1, 2, [3, 4], 5, [6, [7, 8, [9, [10, 11], 12]]]];

Tests:

1
const flttn = (arr) => { if(!Array.isArray(arr)) return []; const fltn = (arr) => arr.reduce((acc, el) => Array.isArray(el) ? acc.concat(fltn(el)) : acc.concat(el), []); return fltn(arr); };
2
const scalar = v => !Array.isArray(v); const flatten = (deep, flat = []) => { if (deep.length === 0) return flat; let [head, ...tail] = deep; if (scalar(head)) { return flatten(tail, flat.concat(head)); } else { return flatten(tail, flat.concat(flatten(head))); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1
2

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks, which are small tests designed to measure the performance of specific parts of code.

The provided benchmark measures two different approaches for flattening an array with nested arrays: `flatten` (defined in test case 1) and another implementation (defined in test case 2). The goal is to determine which approach is faster on a particular machine.

**Test Case 1: `flatten`**

In this test case, the `flatten` function uses recursion to flatten the array. Here's a breakdown of what's happening:

* The function takes an input array `arr`.
* If the input array is not an array (i.e., it's a scalar value), it returns an empty array.
* Otherwise, it calls itself recursively on each element of the array that is also an array (`Array.isArray(el)`).
* It concatenates the flattened sub-arrays with the original elements.

**Pros and Cons**

The recursive approach has both advantages and disadvantages:

Advantages:

* Easy to understand and implement.
* Can handle nested arrays of arbitrary depth.

Disadvantages:

* May lead to a high number of function calls, which can increase overhead due to call stack management and potential memory allocation.
* Recursive functions can be slower than iterative approaches due to the repeated creation of stack frames.

**Test Case 2: `scalar`**

In this test case, the `flatten` function uses a non-recursive approach that takes advantage of the `Array.isArray()` method. Here's what happens:

* The function takes an input array `deep`.
* If the length of `deep` is 0 (i.e., it's an empty array), it returns the accumulated flattened sub-array.
* Otherwise, it extracts the first element and its index in the array (`head` and `tail`).
* It checks if the head value is a scalar using the `scalar()` function. If so, it concatenates `head` to the result and recursively calls itself on the tail with an updated accumulator (concatenated with `flat.concat(head)`).
* If the head value is not a scalar, it recursively calls itself on the tail with an updated accumulator.

**Pros and Cons**

This approach has some advantages over the recursive one:

Advantages:

* Avoids creating unnecessary stack frames.
* Can take advantage of the built-in `Array.isArray()` method to reduce function call overhead.

Disadvantages:

* Requires a separate scalar checking function, which adds complexity.
* May still lead to slower performance due to array concatenation operations.

**Library and Special JS Features**

Neither of these test cases uses a specific library, but they do utilize some native JavaScript features:

* `Array.isArray()`: used to check if an object is an array.
* Array methods (e.g., `concat()`, `[head, ...tail]`): used for array manipulation.

**Other Alternatives**

There are several alternative approaches to flattening arrays with nested arrays. Some examples include:

* Using a queue-based data structure to process elements in order, without recursion or array concatenation.
* Employing iterative algorithms that use bit manipulation or other low-level optimization techniques.
* Utilizing specialized libraries like Lodash or Ramda, which provide optimized and highly efficient implementations of array flattening operations.

However, these alternatives are often more complex to implement and may not be suitable for simple benchmarking purposes.

Related benchmarks:

flatten (version: 0)

Comparing performance of: 1 vs 2

Created: 9 years ago by: Guest

Jump to the latest result

1

2

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):