Benchmark: Array destructing vs object

Tests:

Array destructing
function f(n) { if (n <= 2) { return [1, 1] } const [a, b] = f(n - 1) const [c, d] = f(n - 2) return [a + b, c + d] } f(15)
Object
function f(n) { if (n <= 2) { return {0:1,1:1} } const {0:a, 1:b} = f(n - 1) const {0:c, 1:d} = f(n - 2) return {0:a + b, 1:c + d} } f(15)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array destructing
Object

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
Array destructing	129087.9 Ops/sec
Object	72820.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and explore what's being tested in this benchmark.

**What is being tested?**

MeasureThat.net is testing two different approaches to implementing a recursive function that calculates the sum of elements at positions `n-1` and `n-2` from an initial value of 1. The test case uses JavaScript as the programming language.

The two approaches being compared are:

1. **Array destructuring**: This approach uses array destructuring to extract values from arrays. It takes advantage of the fact that in JavaScript, when you use array destructuring on an array literal, it creates a new array with the extracted values.
2. **Object access**: This approach uses object access to retrieve values from an object.

**Options compared**

In this benchmark, two options are being compared:

* Array destructuring
* Object access

**Pros and cons of each approach**

**Array destructuring:**

Pros:

* More concise and readable code
* Often faster than object access due to the compiler's ability to optimize array literals

Cons:

* Can be less efficient in certain scenarios, such as when dealing with large arrays or objects.
* May not work as expected if the input data is not a valid array literal.

**Object access:**

Pros:

* Can be more efficient than array destructuring for large datasets, as it avoids creating new arrays.
* Works even if the input data is not a valid array literal.

Cons:

* Code can be less readable and concise due to the need to explicitly access object properties.

**Other considerations**

In addition to the performance differences between the two approaches, there are other factors to consider when choosing between them:

* **Code maintainability**: Array destructuring can make code more readable and easier to understand, while object access can lead to more verbose code.
* **Browser support**: Both array destructuring and object access are supported in modern browsers, but it's essential to ensure that the code works across different browser versions.

**Library usage**

In this benchmark, no specific library is being used beyond the built-in JavaScript features mentioned above. However, MeasureThat.net does provide a way to use libraries or frameworks in custom benchmarks, allowing users to test their own implementations.

**Special JS feature or syntax**

There are no special JavaScript features or syntaxes being used in these tests. The code is standard JavaScript that should be familiar to most software engineers.

**Benchmark preparation code and JSON definitions**

The benchmark preparation code is empty (`"Script Preparation Code": null`), indicating that the test case relies on MeasureThat.net's infrastructure to execute the benchmark. The `Benchmark Definition` JSON defines the two test cases, each with a unique name and description.

In conclusion, this benchmark tests the performance of two different approaches to implementing a recursive function in JavaScript: array destructuring and object access. While both approaches have their pros and cons, array destructuring is often preferred for its conciseness and readability, while object access can be more efficient for large datasets.

Related benchmarks:

Array destructing vs object (version: 0)

Comparing performance of: Array destructing vs Object

Created: 5 years ago by: Registered User

Jump to the latest result

Array destructing

Object

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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