Benchmark: collectExpandedIdsFromDataSource

Script Preparation code:

var dataSource = [...Array(100)].map(() => ({
    _id: crypto.randomUUID(),
    children: [...Array(10)].map(() => ({
        _id: crypto.randomUUID(),
        children: [...Array(10)].map(() => ({
            _id: crypto.randomUUID(),
            children: [],
        }))
    }))
}));

var collectExpandedIdsFromDataSource1 = (
    dataSource,
    searchPhrase,
) => {
    const ids = [];

    if (!searchPhrase) return ids;

    const getRowIds = (row) => {
        if (row._id && row.children?.length) ids.push(row._id);

        for (const child of row.children ?? []) getRowIds(child);
    };

    for (const row of dataSource) getRowIds(row);

    return ids;
};

var collectExpandedIdsFromDataSource2 = (dataSource, searchPhrase) => {
    if (!searchPhrase) return [];

    return dataSource.flatMap(row => {
        return row.children?.length ?
            (row._id ? [row._id, ...collectExpandedIdsFromDataSource2(row.children, searchPhrase)] :
                collectExpandedIdsFromDataSource2(row.children, searchPhrase)) : []
    })
};

function* collectExpandedIdsFromDataSource3(dataSource, searchPhrase) {
    if (!searchPhrase) return;

    for (const row of dataSource) {
        if (row.children?.length) {
            if (row._id) yield row._id;

            yield* collectExpandedIdsFromDataSource3(row.children, searchPhrase);
        }

    };
};

Tests:

collectExpandedIdsFromDataSource loop
collectExpandedIdsFromDataSource1(dataSource, 'aaa')
collectExpandedIdsFromDataSource flatMap
collectExpandedIdsFromDataSource2(dataSource, 'aaa')
collectExpandedIdsFromDataSource generator
[...collectExpandedIdsFromDataSource3(dataSource, 'aaa')]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
collectExpandedIdsFromDataSource loop
collectExpandedIdsFromDataSource flatMap
collectExpandedIdsFromDataSource generator

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

I'll break down what's being tested in the provided JSON benchmark definition and explain the different approaches, their pros and cons, and other considerations.

**Benchmark Definition**

The benchmark measures the performance of three functions that extract expanded IDs from a nested data source using different methods:

1. `collectExpandedIdsFromDataSource1`: A recursive function that uses a loop to traverse the nested data source.
2. `collectExpandedIdsFromDataSource2`: A functional programming approach that uses `flatMap` to flatten the nested data source and then extracts IDs.
3. `collectExpandedIdsFromDataSource3`: A generator function that yields IDs from the nested data source.

**Script Preparation Code**

The script preparation code defines a nested data source with 100 items, each containing an `_id` and multiple children. The children themselves are also arrays of objects with `_id` and `children` properties.

**Options Compared**

1. **Loop vs FlatMap**: `collectExpandedIdsFromDataSource1` uses a loop to traverse the nested data source, while `collectExpandedIdsFromDataSource2` uses `flatMap` to flatten the data before extracting IDs.
	* Pros of Loop:
		+ Can be more intuitive for developers familiar with loops.
		+ May perform better in certain cases due to cache locality.
	* Cons of Loop:
		+ Can lead to performance issues if not implemented carefully (e.g., using `for` loop instead of `forEach`).
	* Pros of FlatMap:
		+ More concise and expressive code.
		+ Easier to reason about and debug.
	* Cons of FlatMap:
		+ May incur additional overhead due to function call and array creation.
2. **Generator vs Recursive Function**: `collectExpandedIdsFromDataSource3` uses a generator function, while `collectExpandedIdsFromDataSource1` is a regular recursive function.
	* Pros of Generator:
		+ More memory-efficient, as it yields IDs one at a time instead of creating an entire array.
		+ Can be more suitable for large datasets.
	* Cons of Generator:
		+ May require more complex code to work with.
	* Pros of Recursive Function:
		+ Easier to implement and understand for developers familiar with recursion.
		+ Can be more straightforward to reason about.

**Library Used**

None explicitly mentioned in the benchmark definition. However, `crypto` is used for generating random UUIDs, which is a built-in JavaScript library.

**Special JS Feature or Syntax**

The benchmark uses `flatMap`, which is a relatively modern feature introduced in ECMAScript 2019 (ES2020). It's a concise way to flatten arrays and perform other transformations. While most browsers support this feature, it might not be supported in older versions of Internet Explorer.

**Other Considerations**

1. **Cache locality**: The loop-based approach (`collectExpandedIdsFromDataSource1`) can benefit from cache locality, where data is stored in contiguous memory locations. This can lead to better performance on modern CPUs.
2. **Array creation overhead**: The `flatMap` approach (`collectExpandedIdsFromDataSource2`) creates a new array by concatenating elements. While this might be more efficient for some use cases, it can incur additional overhead due to array creation and copying.

**Alternatives**

Other approaches that could be used to implement the same benchmark include:

1. Using `reduce()` instead of loops.
2. Employing iterative algorithms with techniques like memoization or caching.
3. Leveraging specialized libraries or frameworks designed for performance-critical tasks, such as parsing or data processing.
4. Utilizing parallel processing or concurrent programming techniques to take advantage of multiple CPU cores.

Keep in mind that the best approach depends on the specific requirements and constraints of your use case.

Latest run results:

Run details: (Test run date: 4 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36

Browser/OS: Chrome 126 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
collectExpandedIdsFromDataSource loop	8424.2 Ops/sec
collectExpandedIdsFromDataSource flatMap	4569.1 Ops/sec
collectExpandedIdsFromDataSource generator	4240.6 Ops/sec

Related benchmarks:

collectExpandedIdsFromDataSource (version: 0)

Comparing performance of: collectExpandedIdsFromDataSource loop vs collectExpandedIdsFromDataSource flatMap vs collectExpandedIdsFromDataSource generator

Created: 4 months ago by: Registered User

Jump to the latest result

collectExpandedIdsFromDataSource loop

collectExpandedIdsFromDataSource flatMap

collectExpandedIdsFromDataSource generator

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):