Benchmark: qwewtrqw1231 - MeasureThat.net

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.5/lodash.min.js"></script>

Script Preparation code:

var firstEqual = [];
var secondEqual = [];

for (var i=0; i<=100; i++) {
  firstEqual.push(i);
  secondEqual.push(i);
}

var arrayToDedup = [...firstEqual, ...secondEqual];

Tests:

tets1
_.uniq(arrayToDedup);
test2
[...new Set(arrayToDedup)]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
tets1
test2

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its various components.

**Benchmark Definition JSON**

The benchmark definition is a JSON object that represents a JavaScript microbenchmark. It contains the following properties:

* `Name`: A unique identifier for the benchmark.
* `Description`: A brief description of the benchmark.
* `Script Preparation Code`: The code used to prepare the input data for the benchmark. In this case, it creates two arrays (`firstEqual` and `secondEqual`) and adds 101 numbers (0-100) to each array, which is then concatenated using the spread operator (`[...firstEqual, ...secondEqual]`). This creates a large array of duplicates.
* `Html Preparation Code`: The HTML code used to include a library in the benchmark. In this case, it includes the Lodash library version 4.17.5.

**Individual Test Cases**

The benchmark definition contains two individual test cases:

1. `test1`: Uses the `_uniq` function from the Lodash library to remove duplicates from the `arrayToDedup` array.
2. `test2`: Uses the spread operator (`[...new Set(arrayToDedup)]`) to create a new set from the `arrayToDedup` array and then converts it back to an array.

**Options Compared**

The two test cases compare the performance of two approaches to remove duplicates from an array:

1. **Lodash `_uniq` function**: This approach uses a library function specifically designed for removing duplicates.
2. **Spread operator with `Set`**: This approach uses the spread operator to create a new set, which automatically removes duplicates.

**Pros and Cons**

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

* **Lodash `_uniq` function**:
	+ Pros: Efficient and optimized for performance.
	+ Cons: Requires including an external library (Lodash).
* **Spread operator with `Set`**:
	+ Pros: Fast and efficient, without requiring any additional libraries.
	+ Cons: May be slower than the Lodash approach for very large datasets.

**Library - Lodash**

The Lodash library is a popular JavaScript utility library that provides various functions for tasks such as array manipulation, object manipulation, and more. The `_uniq` function is part of this library and is specifically designed to remove duplicates from an array.

**Special JS Feature/Syntax - None**

There are no special JavaScript features or syntax used in this benchmark.

**Other Alternatives**

If you don't want to use the Lodash library or the spread operator with `Set`, you could also consider other approaches, such as:

* Using a custom implementation of the `_uniq` function.
* Using the `filter` method to remove duplicates from the array.
* Using a different library or framework that provides similar functionality.

However, these alternatives may not be as efficient or optimized as the Lodash approach or the spread operator with `Set`.

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36

Browser/OS: Chrome 78 on Mac OS X 10.15.1

View result in a separate tab

Test name	Executions per second
tets1	166385.8 Ops/sec
test2	204625.0 Ops/sec

Related benchmarks:

qwewtrqw1231 (version: 0)

1qerqwe123

Comparing performance of: tets1 vs test2

Created: 5 years ago by: Guest

Jump to the latest result

tets1

test2

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):