Benchmark: Spread operator vs apply vs normal vs destruct - V2

Script Preparation code:

function test(a , b , c , d , e) { return a + b + c + d + e }
var using = (new Array(5)).fill(null).map((e, i) => (i));
var [a , b , c , d , e] = using

Tests:

spread
test(...using);
apply
test.apply(null, using)
normal
test(using[0] , using[1] , using[2] , using[3] , using[4])
destruct
test(a , b, c , d , e)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread
apply
normal
destruct

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Purpose**

The main goal of this benchmark is to compare the performance differences between four ways to call a function with arbitrary arguments dynamically:

1. Using the spread operator (`...`).
2. Using the `apply()` method.
3. Using the normal function call syntax (i.e., passing each argument separately).
4. Using destructuring assignment (`[a, b, c, d, e] = using`).

**Options Compared**

The benchmark compares these four options by executing a simple function `test(a, b, c, d, e)` with different argument-passing methods.

* **Spread Operator**: Uses the spread operator to pass an array of arguments to the function.
	+ Pros: concise and expressive syntax.
	+ Cons: may incur overhead due to array creation and manipulation.
* **Apply Method**: Uses the `apply()` method to pass an array of arguments to the function.
	+ Pros: can be more efficient than spread operator, as it avoids array creation.
	+ Cons: less readable and more complex syntax.
* **Normal Function Call Syntax**: Passes each argument separately when calling the function.
	+ Pros: easy to read and understand, no additional overhead.
	+ Cons: may not be as concise or expressive.
* **Destructuring Assignment**: Uses destructuring assignment to assign values from an array to separate variables.
	+ Pros: can be more efficient than normal function call syntax, as it avoids unnecessary variable creation.
	+ Cons: less readable and more complex syntax.

**Library Used**

The benchmark uses the `Array` class and its methods (e.g., `fill()`, `map()`) which are part of the JavaScript standard library. No additional libraries are required.

**Special JS Feature or Syntax**

The benchmark utilizes ES6 features such as:

* The spread operator (`...`)
* Destructuring assignment (`[a, b, c, d, e] = using`)
* Template literals (not explicitly shown in this example)
* Arrow functions (not explicitly shown in this example)

**Benchmark Preparation Code**

The preparation code creates an array `using` with 5 null values and maps it to an array of numbers from 0 to 4. It then passes the resulting array to the function using each of the four methods being compared.

**Other Alternatives**

If you wanted to test alternative approaches, you could consider:

* Using other spread operator variants (e.g., `...[a, b, c]`)
* Using a different method for generating random arguments (e.g., using `Math.random()`)
* Adding more complex function calls or argument types to the benchmark
* Comparing performance with different JavaScript engines or versions

Keep in mind that the specific alternatives will depend on your goals and requirements.

Latest run results:

Run details: (Test run date: 8 months 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
spread	11364941.0 Ops/sec
apply	11115246.0 Ops/sec
normal	4394981.5 Ops/sec
destruct	4463496.0 Ops/sec

Related benchmarks:

Spread operator vs apply vs normal vs destruct - V2 (version: 0)

Compare the differing ways you can call a function with arbitrary arguments dynamically

Comparing performance of: spread vs apply vs normal vs destruct

Created: 8 months ago by: Guest

Jump to the latest result

spread

apply

normal

destruct

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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