Benchmark: apply vs ... - MeasureThat.net

Script Preparation code:

var a = {
	fn(a1, a2, a3, a4) {
		return a1 + a2 + a3 + a4;
	}
}
var args = [0,1,3,4];

Tests:

apply
a.fn.apply(a, args);
...
a.fn(...args);
direct call
a.fn(args[0], args[1], args[2], args[3])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
apply
...
direct call

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
apply	5716095.0 Ops/sec
...	8114901.5 Ops/sec
direct call	3798353.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the explanation of the provided benchmark.

**Benchmark Overview**

The benchmark measures the performance difference between using `apply()` and calling the function directly (using the dot notation or spread syntax) for a given JavaScript function. The test cases are designed to isolate this specific aspect of the language, allowing users to compare the performance of these two approaches on their own code.

**Script Preparation Code**

The script preparation code defines a function `fn` with four arguments, and an array `args` containing four values. This setup allows us to focus solely on the `apply()` vs direct call comparison without introducing other variables or functions that might impact performance.

```javascript
var a = {
    fn(a1, a2, a3, a4) {
        return a1 + a2 + a3 + a4;
    }
};
var args = [0, 1, 3, 4];
```

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark does not consider any DOM-related or browser-specific factors.

**Library Usage**

The `apply()` method is part of the JavaScript standard library. Its purpose is to provide a way to invoke a function with a given this value and arguments list. In this benchmark, it's used to pass the `args` array as both the `this` argument and the arguments list to the `fn` function.

**Special JS Features or Syntax**

The benchmark does not use any special JavaScript features or syntax beyond what's standard in modern browsers.

**Options Compared**

There are two main options being compared:

1. **Using `apply()`**: This approach passes the entire `args` array as both the `this` argument and the arguments list to the function.
2. **Direct Call (using dot notation)**: This approach calls the function directly using the dot notation, passing each element of the `args` array individually.

**Pros and Cons**

**Using `apply()`**

Pros:

* Can be more concise and expressive, especially when working with functions that need to pass a lot of data as arguments.
* Can reduce boilerplate code in some cases.

Cons:

* Can lead to confusing behavior if not used carefully (e.g., using the wrong this value or passing the wrong arguments).
* May incur additional overhead due to the extra arguments and potential function invocation.

**Direct Call**

Pros:

* Is generally faster, as it avoids the overhead of invoking `apply()`.
* Is more straightforward to read and understand, especially for simple functions with few arguments.

Cons:

* Can be less concise and more verbose than using `apply()`, especially when dealing with many arguments.
* May lead to more code duplication if not used carefully (e.g., defining a wrapper function).

**Other Considerations**

The performance difference between `apply()` and direct calls is generally small, and the choice of approach often depends on coding style, readability concerns, and specific use case requirements. The benchmark provides a fair comparison, allowing users to determine which approach is best suited for their needs.

If you're interested in exploring alternative approaches or optimizing your own JavaScript code, consider looking into other techniques, such as:

* **Function currying**: Breaking down function calls into smaller, more manageable pieces.
* **Caching and memoization**: Storing the results of expensive function calls to avoid recalculating them.
* **Optimizing loop performance**: Techniques like loop unrolling, fusion, or using SIMD instructions (if available).

Related benchmarks:

apply vs ... (version: 0)

Comparing performance of: apply vs ... vs direct call

Created: one year ago by: Guest

Jump to the latest result

apply

...

direct call

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):