Benchmark: Lodash Uniq vs Javascript Set 21

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<=1000000; i++) {
  firstEqual.push(i);
  secondEqual.push(i);
}

var arrayToDedup = [...firstEqual, ...secondEqual];

Tests:

Lodash Uniq
_.uniq(arrayToDedup);
Javascript Set
[...new Set(arrayToDedup)]

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash Uniq
Javascript Set

Fastest: N/A

Slowest: N/A

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

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Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark compares two approaches to remove duplicates from an array:

1. **Lodash Uniq**: Uses the Lodash library to remove duplicates from an array.
2. **JavaScript Set**: Uses a native JavaScript `Set` object to remove duplicates from an array.

**Script Preparation Code**

The script preparation code creates two identical arrays, `firstEqual` and `secondEqual`, with 1 million elements each. The `arrayToDedup` variable is then created by concatenating the two arrays using the spread operator (`[...firstEqual, ...secondEqual]`). This array will be used as input for both benchmark tests.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the Lodash library version 4.17.5 from a CDN.

**Test Cases**

There are two test cases:

1. **Lodash Uniq**: The first test case measures the performance of using the `_.uniq` function from Lodash to remove duplicates from `arrayToDedup`.
2. **JavaScript Set**: The second test case measures the performance of using a native JavaScript `Set` object to remove duplicates from `arrayToDedup`.

**Pros and Cons of Each Approach**

1. **Lodash Uniq**:
	* Pros:
		+ Provides a more comprehensive set of features for array manipulation.
		+ Can be used with other Lodash functions to create complex data processing pipelines.
	* Cons:
		+ Adds overhead due to the library's functionality and potential memory allocation.
		+ May not be suitable for very large datasets due to performance considerations.
2. **JavaScript Set**:
	* Pros:
		+ Native JavaScript implementation, which means lower overhead and faster execution.
		+ More efficient memory usage compared to using Lodash Uniq.
	* Cons:
		+ Requires manual handling of duplicate values (e.g., adding elements to the set).
		+ Limited functionality for more complex data processing.

**Other Considerations**

When choosing between these approaches, consider the trade-offs between:

* Performance: JavaScript Set is likely to be faster than Lodash Uniq due to its native implementation.
* Complexity: Lodash Uniq provides a more comprehensive set of features, but may introduce additional overhead.

**Native JavaScript Features**

The benchmark uses a feature that is widely supported in modern browsers: the `Set` object. The `Set` object is implemented in native JavaScript and provides an efficient way to remove duplicates from arrays.

In summary, this benchmark allows users to compare the performance of two approaches to remove duplicates from arrays: using Lodash Uniq or native JavaScript Set. By understanding the pros and cons of each approach, developers can choose the best solution for their specific use case.

**Alternative Approaches**

Other alternatives for removing duplicates from arrays include:

* Using `Array.prototype.filter()` and checking for duplicate values.
* Implementing a custom algorithm to remove duplicates (e.g., using a hash table or sorting the array).
* Using a library like Moment.js, which includes an implementation of `_.uniq`.

Related benchmarks:

Lodash Uniq vs Javascript Set 21 (version: 0)

Comparing performance of: Lodash Uniq vs Javascript Set

Created: 7 years ago by: Guest

Jump to the latest result

Lodash Uniq

Javascript Set

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):