Benchmark: lodash uniq vs Array.from(new Set()) vs spread new Set() vs array.filter

HTML Preparation code:


<script src='https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.min.js'></script>

Tests:

Spread
return [...new Set([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7])]
use lodash
return _.uniq([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]);
Array.from
return Array.from( new Set([1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]) );
filter
const arr = [1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7] return arr.filter((num,index)=> index =arr.indexOf(num) )

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Spread
use lodash
Array.from
filter

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

The provided JSON represents a benchmark test that compares four different approaches to remove duplicate elements from an array:

1. `lodash uniq`: Uses the Lodash library's `uniq` function, which removes duplicates from an array while preserving the original order.
2. `Array.from(new Set())`: Utilizes the `Set` data structure in JavaScript, which automatically removes duplicates. The `Array.from()` method is used to convert the `Set` object back into an array.
3. `spread new Set()`: Spreads the elements of a set (which removes duplicates) and converts it back into an array using the spread operator (`...`).
4. `array.filter`: Uses the `filter()` method to create a new array with only unique elements, based on the presence of each element in the original array.

**Pros and Cons of Each Approach:**

*   **lodash uniq**: 
    *   Pros:
        *   Efficient for large datasets
        *   Maintains the original order of elements
    *   Cons:
        *   Requires an external library (Lodash)
        *   May have slower startup times compared to other approaches
*   **Array.from(new Set())**:
    *   Pros:
        *   Fast and efficient for large datasets
        *   No external libraries required
    *   Cons:
        *   Does not maintain the original order of elements
        *   Requires converting back into an array
*   **spread new Set()**:
    *   Pros:
        *   Maintains the original order of elements
        *   Does not require any external libraries
    *   Cons:
        *   May have slower performance due to set creation and spreading operations
        *   Less efficient than `Array.from(new Set())` for large datasets
*   **array.filter**:
    *   Pros:
        *   Fast and efficient for small to medium-sized datasets
        *   Does not require any external libraries
    *   Cons:
        *   May have slower performance for very large datasets due to the overhead of creating a new array
        *   Does not maintain the original order of elements

**Library Used:**

The Lodash library is used in the `use lodash` test case. Lodash provides a wide range of utility functions, including ones for working with arrays and objects.

**Special JS Feature/Syntax:**

There are no special JavaScript features or syntax mentioned in the provided benchmarking code.

**Alternatives to Benchmarking Libraries:**

If you're interested in exploring alternatives to MeasureThat.net, there are several other benchmarking libraries available for JavaScript:

1.  **Benchmark.js**: A popular open-source benchmarking library for Node.js and browser environments.
2.  **JSBench**: A simple and lightweight benchmarking library specifically designed for Node.js and browser environments.
3.  **benchmark.js**: Another well-established benchmarking library for Node.js and browser environments.

These libraries provide similar functionality to MeasureThat.net, allowing you to create and run JavaScript microbenchmarks for various use cases.

**Additional Considerations:**

When choosing a benchmarking library or creating your own benchmarks, consider the following factors:

1.  **Hardware**: Ensure that your test environment accurately represents the hardware configuration you're targeting.
2.  **Software Environment**: Account for differences in browser versions, Node.js environments, and other software configurations.
3.  **Test Scope**: Define a clear scope for your benchmarking tests to ensure they focus on specific aspects of performance or usability.
4.  **Repeatability**: Use techniques like warm-up runs, flush data, and accurate instrumentation to ensure consistent results across multiple executions.

By carefully considering these factors and choosing the right tools for your use case, you can create reliable and informative benchmarking tests that provide valuable insights into the performance of your JavaScript applications.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/119.0

Browser/OS: Firefox 119 on Ubuntu

View result in a separate tab

Test name	Executions per second
Spread	757970.0 Ops/sec
use lodash	3667681.8 Ops/sec
Array.from	754783.2 Ops/sec
filter	1710970.6 Ops/sec

Related benchmarks:

lodash uniq vs Array.from(new Set()) vs spread new Set() vs array.filter (version: 0)

Comparing performance of: Spread vs use lodash vs Array.from vs filter

Created: 3 years ago by: Guest

Jump to the latest result

Spread

use lodash

Array.from

filter

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):