Benchmark: arr&for - MeasureThat.net

Tests:

arr
console.log( new Array(10**3).fill(0).map((_, index) => index + 1).reduce((acc, val) => acc += val) );
for
let acc = 0; for (let i = 1; i <= 10**3; i += 1){ acc += i; } console.log(acc);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
arr
for

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and explore the JavaScript microbenchmarks.

**Benchmark Overview**

MeasureThat.net allows users to create and run JavaScript microbenchmarks, which test the performance of specific code snippets. The benchmark is represented by a JSON object that defines the script preparation code, HTML preparation code (which is empty in this case), and individual test cases.

In this example, there are two test cases: "arr" and "for". Each test case has a corresponding benchmark definition in the JSON object.

**Test Case 1: "arr"**

The first test case uses an array comprehension to create an array of numbers from 0 to 10^3 (1000) and then calculates the sum using the `reduce()` method.

```javascript
console.log(new Array(10**3).fill(0).map((_, index) => index + 1).reduce((acc, val) => acc += val));
```

This code is executed in a single line and involves creating an array, mapping over it, and then reducing the result to calculate the sum.

**Test Case 2: "for"**

The second test case uses a traditional `for` loop to iterate from 1 to 10^3 (1000) and calculates the sum of the numbers.

```javascript
let acc = 0;
for (let i = 1; i <= 10**3; i += 1){ 
    acc += i;
}
console.log(acc);
```

This code is executed in a single line but involves multiple iterations and assignments to calculate the sum.

**Library: `reduce()`**

The `reduce()` method is used in both test cases. It's a built-in JavaScript function that applies a reducer function to each element of an array, reducing it to a single value. In this case, the reducer function simply adds the current value to the accumulator (`acc`).

Pros:

* Efficient and concise way to calculate sums
* Works well with large arrays

Cons:

* Can be slower than traditional loops for very large datasets
* May not work as expected in certain browsers or environments

**Other Considerations**

When comparing these two approaches, consider the following factors:

* **Performance:** The `for` loop approach might perform better on older systems or those with limited resources.
* **Readability:** The array comprehension using `reduce()` is often more readable and concise.
* **Maintainability:** If you need to modify the sum calculation, both approaches are easily modifiable.

**Alternative Approaches**

Other alternatives for calculating sums include:

1. Using the `Array.prototype.reduce()` method with a custom reducer function.
2. Utilizing libraries like Lodash, which provides a `sum` function.
3. Leveraging functional programming techniques, such as using `reduceRight()` or `filter()`, to simplify the calculation.

For this specific benchmark, both approaches are well-suited and can be executed efficiently by modern browsers and JavaScript engines.

**Special JS Features**

None of the code snippets in these test cases utilize any special JavaScript features. They rely on standard language constructs and built-in functions.

Latest run results:

Run details: (Test run date: 3 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0

Browser/OS: Chrome 127 on Windows

View result in a separate tab

Test name	Executions per second
arr	51562.9 Ops/sec
for	134123.8 Ops/sec

Related benchmarks:

arr&for (version: 1)

Comparing performance of: arr vs for

Created: 3 months ago by: Registered User

Jump to the latest result

arr

for

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):