Benchmark: twoja stara5 - 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(i< I/2? 'sections':'nosections', 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.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided JSON represents a benchmark definition, which includes the script preparation code, HTML preparation code (empty in this case), and individual test cases.

**Script Preparation Code Analysis**

The script preparation code creates an array `arr` with a specified number of elements, where each element is an object with two properties: `group` and `value`. The value is determined by the function `createItem`, which takes two arguments: `group` and `value`. The loop iterates from 0 to `I-1`, where `I` is a constant set to 12345.

**Options Compared**

The benchmark comparison involves two options:

1. **reduce**: This method iterates over the array and accumulates values that match a specific condition (in this case, `filter.group === 'sections'`). The order of elements in the original array is preserved.
2. **filter + map**: This approach filters out elements that don't match the condition (`_ => _.group !== 'sections'`) and then maps each remaining element to its value.

**Pros and Cons**

* **reduce**:
	+ Pros: Preserves the order of elements in the original array, can be more efficient for large datasets.
	+ Cons: May have higher overhead due to the accumulation process.
* **filter + map**:
	+ Pros: Typically faster and more memory-efficient, as it avoids accumulating values.
	+ Cons: Can lose data if the filtering condition is not accurate or has side effects.

**Library Use**

The `Array.prototype.reduce()` method is used in the "reduce" test case. This method applies a function to each element of an array (in this case, `arr`), reducing it to a single value. The purpose of this library is to provide a concise and expressive way to perform cumulative operations on arrays.

**Special JavaScript Feature**

The `let` keyword with arrow functions (`_ => _.group === 'sections'`) is used in the filtering condition. This syntax is called "arrow function expression" or "Fat Arrow Function". It provides a concise way to define small, one-time use functions without declaring a named function. While not strictly necessary for this benchmark, it's a modern JavaScript feature that can improve code readability.

**Other Alternatives**

If you wanted to write the same benchmark using `forEach` and manual iteration:

```javascript
arr.forEach(function(item) {
  if (item.group === 'sections') {
    result.push(item.value);
  }
});
```

Or, using `every()` with a callback function:

```javascript
const result = arr.reduce((acc, filter) => {
  if (filter.group !== 'sections') return false;
  acc.push(filter.value);
  return true;
}, []);
```

Note that these alternatives have different performance characteristics and may not be suitable for all use cases.

I hope this explanation helps! Let me know if you have any further questions.

Related benchmarks:

twoja stara5 (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):