Benchmark: hb-2365-test (tes1t)

Script Preparation code:

const arr = Array(500).fill(null);
var parents = arr.map(() => {
    return {
        children: arr.map((value, index) => ({value, selected: index % 2})),
    };
});

Tests:

1
const out = []; parents.forEach(parent => { out.push(...parent.children.filter(child => child.selected).map(({value}) => value)); });
2
const out = []; parents.forEach(parent => { parent.children.forEach(child => child.selected && out.push(child.value)); });

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:

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definition and test cases to explain what's being tested, compared options, pros/cons, and other considerations.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark for comparing two approaches of filtering and mapping over an array of objects. The script preparation code creates an array `arr` with 500 null values and then maps each element to an object with `children` property, which is another array created by mapping over the original array.

**Options Being Compared**

There are two test cases being compared:

1. **Test Case 1**: Iterates over each parent in the `parents` array and pushes the values of the filtered children (only those with `selected` set to true) into an output array using `map()`, `filter()`, and another `map()` inside it.
2. **Test Case 2**: Iterates over each parent in the `parents` array and for each child, checks if `child.selected` is truthy (i.e., selected), then pushes the value of the child into an output array using a single `map()`.

**Pros/Cons of Each Approach**

1. **Test Case 1**: Using multiple `filter()` and `map()` functions can be slower due to function call overhead, especially if the arrays are large.
	* Pros: Can handle complex filtering and mapping logic.
	* Cons: Higher function call count, which might incur performance penalties.
2. **Test Case 2**: Using a single `map()` function with an implicit condition (i.e., checking `child.selected`) can be faster because it reduces the number of function calls.
	* Pros: Lower function call count, potentially faster execution.
	* Cons: May not be as readable or maintainable for complex filtering and mapping logic.

**Library and Purpose**

There is no explicit library mentioned in the provided JSON. However, the `map()` function is a built-in JavaScript method used to create a new array by performing an operation on each element of the original array.

**Special JS Feature/Syntax**

None of the test cases explicitly use special JavaScript features or syntax that require explanation.

**Other Considerations**

* The benchmark measures the number of executions per second (ExecutionsPerSecond) for each browser, which is a common metric used to compare performance.
* The `DevicePlatform` and `OperatingSystem` fields suggest that the benchmark might be interested in identifying devices and platforms that can run the test cases efficiently.

**Alternative Approaches**

Other approaches to solve this problem might include:

1. Using a library like Lodash or Ramda, which provide optimized versions of common array operations like filtering and mapping.
2. Implementing a custom iterative solution using loops instead of recursive functions, which could potentially be faster for very large arrays.
3. Utilizing WebAssembly or other low-level optimization techniques to further improve performance.

However, these alternatives are not directly related to the specific trade-offs between the two test cases being compared (Test Case 1 vs. Test Case 2).

Related benchmarks:

hb-2365-test (tes1t) (version: 1)

Comparing performance of: 1 vs 2

Created: 7 years ago by: Registered User

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):