Benchmark: twoja stara2 - 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('nosection', i);
}

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.

Measuring JavaScript performance is a complex task, and MeasurThat.net's approach helps to identify which specific parts of code are the bottlenecks.

The provided JSON benchmark definition describes two test cases: "reduce" and "filter + map". These two approaches are commonly used in JavaScript for data processing and manipulation. Here's what's being tested:

**Options Compared**

1. `arr.reduce()`: This method iterates over the array and applies a callback function to each element, reducing it to a single value (in this case, an array of values). The key differences between "reduce" and "filter + map" lie in how they handle the array elements.

**Pros and Cons**

* **arr.reduce()**: This approach can be more memory-efficient if you only need to process one element at a time. However, it requires iterating over the entire array in a single pass, which can lead to slower performance for large datasets.
* **filter + map**: This approach is often considered faster because it separates filtering and mapping into two separate operations. Filtering creates a new array with only the elements that match the condition, while mapping applies a transformation function to each element in that filtered array. However, this approach requires creating an intermediate array, which can lead to higher memory usage.

**Other Considerations**

* **Array iteration**: Both approaches iterate over the `arr` array. If the array is large, this could be a bottleneck.
* **Callback functions**: The use of callback functions in both approaches means that performance may also depend on the complexity and number of these functions.

**Library Usage**

In the provided benchmark definition, there is no explicit library usage mentioned. However, it's worth noting that some JavaScript engines (like V8) have built-in optimizations for array methods like `reduce()` and `filter()`, which might affect performance.

**Special JS Features or Syntax**

There are a few notable features used in this benchmark:

* **Arrow functions**: The callback function in the "reduce" definition uses an arrow function, which is a concise way to define small anonymous functions.
* **Template literals**: The script preparation code defines variables `arr` and `createItem` using template literals (the `\r\n\r\n` separator).

**Alternatives**

If you're interested in exploring other approaches or alternatives for performance testing JavaScript microbenchmarks, consider the following:

1. **Benchmarking frameworks**: Libraries like Benchmark.js or js-perf provide more structured benchmarking and can help with setup, execution, and reporting.
2. **Custom instrumentation**: Writing custom code to instrument your application with performance counters can provide more detailed insights into specific parts of your codebase.
3. **Profiling tools**: Using built-in profiling tools (like Chrome DevTools' Profiler) or third-party libraries like WebPageTest can help identify performance bottlenecks in your application.

In summary, the provided benchmark definition compares two common JavaScript approaches for data processing: `arr.reduce()` and "filter + map". By understanding the trade-offs between these approaches, developers can make informed decisions about which method to use for their specific use cases.

Related benchmarks:

twoja stara2 (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):