Benchmark: Destructure reference vs pass param

Tests:

Destructure
const obj = {a: { e: { i: 6 } } }; const destructureInternally = ({a: { e: { i }}}) => { console.log(i); } console.log(destructureInternally(obj));
Pass Param
const obj = {a: { e: { i: 6 } } }; const { a: { e: { i }}} = obj; const passParam = (param) => { console.log(param); } console.log(passParam(i));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Destructure
Pass Param

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.

Let's break down the provided JSON and explain what is tested, the options compared, pros and cons of those approaches, library usage, special JavaScript features, and alternatives.

**Benchmark Definition**

The benchmark definition represents a test case that compares two approaches: destructuring internally and passing parameters. The question being asked is which approach is cheaper (in terms of memory allocation).

**Options Compared**

Two options are compared:

1. **Destructure Internally**: This approach uses the destructuring syntax to extract properties from an object within the same function. It creates a new scope for the extracted variables, which may help with optimization.
2. **Pass Param**: This approach passes the extracted value as an argument to a separate function.

**Pros and Cons**

* **Destructure Internally**:
	+ Pros: Creates a new scope, potentially avoiding global variable pollution and improving code organization. It also allows for easier reuse of the extracted properties in other parts of the code.
	+ Cons: May introduce additional overhead due to the creation of a new scope, which could impact performance.
* **Pass Param**:
	+ Pros: Avoids creating a new scope, potentially reducing overhead. However, it may lead to global variable pollution and reduced code organization.
	+ Cons: Passes the extracted value as an argument, which may incur additional overhead due to function calls.

**Library Usage**

There is no library usage mentioned in the provided JSON.

**Special JavaScript Features**

The benchmark tests use the following special JavaScript features:

* Destructuring syntax ( introduced in ECMAScript 2015)
* Global variable logging ( using `console.log()`)

If test users special JS feature or syntax, it's likely that the benchmark is designed to evaluate performance under specific conditions.

**Alternative Approaches**

Other alternative approaches could be:

* Using `Object.assign()` or other spread operators to pass properties as arguments
* Creating a new object with extracted properties using `Object.create()`
* Using a different data structure, such as an array or set, to represent the extracted values

Keep in mind that these alternatives might not be as efficient or readable as the tested approaches.

**Benchmark Preparation Code**

The provided JSON does not include preparation code for the benchmark. Typically, this code would:

* Set up the test environment
* Initialize variables and data structures
* Define any necessary constants or functions

In this case, the preparation code is explicitly set to `null`, indicating that no specific setup is required.

**Benchmark Result**

The provided JSON includes the latest benchmark results for two test cases: "Destructure" and "Pass Param". The results show:

* The number of executions per second (FPS) for each test case
* The browser, device platform, operating system, and raw UA string for each execution

These results will help users compare performance across different browsers and platforms.

Related benchmarks:

Destructure reference vs pass param (version: 0)

Both create new references in memory. Which is cheaper?

Comparing performance of: Destructure vs Pass Param

Created: 2 years ago by: Guest

Jump to the latest result

Destructure

Pass Param

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):