Benchmark: werwrewrewer - MeasureThat.net

Script Preparation code:

var test = [];
for(let i = 0; i < 100000; i ++) {
 	 test.push({id: i, value: 300 + i});
}

Tests:

weewwewe
test.filter((data) => data.value > 30000).map((data) => { return {...data, checkValue: true}});
23243432
test.reduce((acc, data) => { if(data.value > 30000) { acc.push({...data, checkValue: true}); } return acc; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
weewwewe
23243432

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark test cases and analyze what's being tested.

**Benchmark Definition**

The Benchmark Definition JSON defines a set of test cases, each with its own JavaScript code snippet. The preparation code is used to initialize an array `test` with 100,000 objects, where each object has two properties: `id` and `value`. This prepared data will be used in the subsequent test cases.

**Individual Test Cases**

There are two individual test cases:

1. **Test Name:** "weewwewe"
2. **Benchmark Definition**: `test.filter((data) => data.value > 30000).map((data) => { return {...data, checkValue: true}});`

In this test case, we're using the `filter()` method to select all objects from the `test` array where the `value` property is greater than 30,000. Then, the `map()` method is applied to transform each selected object by adding a new property called `checkValue` with value `true`. This test case measures how long it takes to perform this filtering and mapping operation on the prepared data.

2. **Test Name:** "23243432"
3. **Benchmark Definition**: `test.reduce((acc, data) => {\r\n\tif(data.value > 30000) {\r\n    \tacc.push({...data, checkValue: true});\r\n    }\r\n  \treturn acc;\r\n}, []);`

In this test case, we're using the `reduce()` method to iterate over the `test` array and accumulate a new array (`acc`) with selected objects. The condition is similar to the previous test case: only objects with `value` greater than 30,000 are included in the accumulated array, along with an additional property called `checkValue` set to `true`. This test case measures how long it takes to perform this reduction operation on the prepared data.

**Comparison and Pros/Cons**

The two test cases compare the performance of filtering (`filter()`) vs. accumulation using reduction (`reduce()`). Both methods can be used to select and transform objects in an array, but they have different characteristics:

* `filter()` creates a new array with the filtered elements, whereas `reduce()` accumulates the selected elements into a single array.
* `filter()` is generally faster for small to medium-sized arrays because it doesn't require building up an accumulation like `reduce()`.
* However, as the input size grows, `reduce()` can be more efficient because it avoids creating intermediate arrays.

**Other Considerations**

The test cases don't use any external libraries or special JavaScript features. The only notable aspect is the use of template literals (e.g., `${...data, checkValue: true}`) in the `map()` and `reduce()` callbacks, which allows for concise expression of object creation.

**Alternative Approaches**

Other alternatives to achieve similar filtering and mapping operations include:

* Using a plain `for` loop or `forEach()` method to iterate over the array and manually create the new array.
* Utilizing libraries like Lodash or Underscore.js that provide optimized methods for common data processing tasks.
* Employing functional programming techniques, such as using functions like `map()` and `filter()`, to express data transformations.

Keep in mind that these alternatives might introduce additional overhead or require different syntax, so it's essential to consider the specific use case and performance requirements when choosing an approach.

Related benchmarks:

werwrewrewer (version: 0)

Comparing performance of: weewwewe vs 23243432

Created: 3 years ago by: Guest

Jump to the latest result

weewwewe

23243432

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.1:latest, generated one year ago):