Benchmark: twoja stara4 - MeasureThat.net

Script Preparation code:

var arr = [];

var createItem = function(group, value) {
  return {
    group: group,
    value: value
  }
}
const I = 12345
for (var i = 0; i < I; i++) {
  arr[i] = createItem('nosections', i);
}
arr[0] = createItem('sections', 0);

Tests:

reduce
arr.reduce((acc, filter) => { if (filter.group === 'sections') acc.push(filter.value); return acc; }, []);
filter + map
arr.filter(_ => _.group === 'sections') .map(_ => _.value);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
filter + map

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 what's being tested in this benchmark and the options being compared.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark, specifically designed to measure the performance of two different algorithms: `reduce` and `filter + map`. The benchmark uses a custom script with an array of items to process, where each item has a "group" property and a value.

**Options Being Compared**

In this benchmark, we have two main options being compared:

1. **Array.prototype.reduce()**: This method applies a function to each element in the array, accumulating a result.
2. **Array.prototype.filter() + Array.prototype.map()**: This approach filters the elements of an array using the `filter()` method and then maps the results to a new array using the `map()` method.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **Array.prototype.reduce()**:
	* Pros: More concise, can be more efficient for large arrays since it avoids creating an intermediate array.
	* Cons: May have higher overhead due to the need to accumulate a result across all elements.
2. **Array.prototype.filter() + Array.prototype.map()**:
	* Pros: Easier to understand and maintain, allows for parallel processing of filters and mappings.
	* Cons: Creates an intermediate array, which can increase memory usage and potentially slow down performance.

**Library Usage**

There is no explicit library usage in this benchmark. However, it's worth noting that the `reduce()` method is a built-in JavaScript method, while `filter()` and `map()` are also native methods.

**Special JS Features or Syntax**

This benchmark does not use any special JavaScript features or syntax beyond standard ES5-2015 features. If you're interested in exploring more advanced options, you might consider modifying the script to include features like:

* Arrow functions (e.g., using `=>` instead of `function()`)
* Spread operators (e.g., using `...arr` to create a new array)
* Promises or async/await

**Alternatives**

If you're interested in exploring alternative approaches, here are some options:

1. **Closures**: Consider using closures to encapsulate data and behavior, which can lead to more efficient code.
2. **Loop unrolling**: Use loop unrolling techniques (e.g., `for (var i = 0; i < arr.length; i++)`) to reduce overhead.
3. **Cache-friendly data structures**: Optimize the array's layout to make it cache-friendly, which can improve performance.
4. **Multi-threading or parallel processing**: Consider using libraries like Web Workers or Pizzicato to execute tasks concurrently.

Keep in mind that these alternatives might require more complex code and may not always lead to significant performance gains.

**Next Steps**

To modify the benchmark and explore alternative approaches, you can:

1. Clone the existing JSON data and modify it to include your preferred changes.
2. Run the modified benchmark using MeasureThat.net or a similar tool.
3. Analyze the results and refine your approach as needed.

Related benchmarks:

twoja stara4 (version: 0)

Comparing performance of: reduce vs filter + map

Created: 6 years ago by: Guest

Jump to the latest result

reduce

filter + map

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):