Benchmark: asdsss - MeasureThat.net

Script Preparation code:

var obj={};
function d(){
  for(let i = 0; i < 100; i++){
    obj[i] = {
      0: 1,
      1: 2,
      2: 3,
      3: 4,
      4: 5,
      5: 6,
      6: 7
    }
  }
};
d();
function ss(o) {
	return o[0] * o[1] * o[2] * o[3] *o[4] * o[5] * o[6]      
}

Tests:

1
Object.values(obj).forEach((item) => { ss(item) });
2
Object.values(obj).map((item) => { ss(item) });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1
2

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

I'd be happy to help you understand the JavaScript microbenchmark on MeasureThat.net.

**Benchmark Definition**

The benchmark definition is a JSON object that represents a JavaScript function `d` and another function `ss`. The `d` function creates an array of objects with six properties (i.e., 0 through 5) and assigns them values. The `ss` function takes an object as input, multiplies its values together, and returns the result.

The script preparation code is:
```javascript
var obj = {};
function d() {
  for (let i = 0; i < 100; i++) {
    obj[i] = {
      0: 1,
      1: 2,
      2: 3,
      3: 4,
      4: 5,
      5: 6,
      6: 7
    }
  }
}
d();
function ss(o) {
  return o[0] * o[1] * o[2] * o[3] * o[4] * o[5] * o[6]
}
```
**Benchmark Options**

The benchmark measures the execution time of two options for processing the `obj` array:

1. **forEach**: The code uses `Object.values(obj).forEach((item) => {\r\n            ss(item)\r\n        });`. This method executes a callback function once for each element in the array.
2. **map**: The code uses `Object.values(obj).map((item) => {\r\n            ss(item)\r\n        });`. This method creates a new array with the results of applying a provided function on every element in this array.

**Pros and Cons**

* **forEach**:
	+ Pros: Generally simpler to understand and implement.
	+ Cons: Returns no value (i.e., `undefined`) for each element processed, which might be inefficient if used directly. In this case, the result is discarded using a single-assignment statement.
* **map**:
	+ Pros: Returns an array of results, allowing for easier processing or manipulation of individual elements. In this case, the result is kept and passed to `ss`.
	+ Cons: Creates an additional array with intermediate results, which can be memory-intensive.

In this specific benchmark, both methods are suitable because we only care about the execution time, not the output values. However, if you needed to process each element individually (e.g., for side effects or conditional statements), `forEach` might be a better choice.

**Library**

There is no explicit library mentioned in the benchmark definition. The code relies on built-in JavaScript methods and functions.

**Special JS Feature/Syntax**

None are explicitly used in this benchmark.

**Other Alternatives**

If you wanted to test alternative approaches, some possible options could be:

1. **reduce**: Instead of using `forEach` or `map`, you could use `Object.values(obj).reduce((acc, item) => acc * ss(item), 1)` to calculate the product in a single pass.
2. **for...of loop**: You could use `for (const value of Object.values(obj)) {\r\n            const result = ss(value);\r\n        }` instead of `forEach`.
3. **array methods with early returns**: If you needed more control over the processing flow, you could use early returns in a `map`-style loop.

Keep in mind that these alternatives might affect performance, memory usage, or code readability, so it's essential to consider your specific use case and optimization goals.

Related benchmarks:

asdsss (version: 0)

Comparing performance of: 1 vs 2

Created: 6 years ago by: Guest

Jump to the latest result

1

2

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):