Benchmark: lodash omit vs native

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

  var allHeaders = {
    'Accept-Encoding': 'gzip,deflate',
    'Accept-Language': 'en-US',
    'Content-Type': 'application/json',
    'User-Agent': `(compatible; Mozilla/5.0; MSIE 9.0; Trident/5.0;})`,
    'Proxy-Connection': 'Keep-Alive',
    Connection: 'Keep-Alive',
    Accept: 'application/hal+json, application/json',
  };

Tests:

lodash omit
_.omit(allHeaders, 'Accept-Encoding');
native delete
delete allHeaders['Accept-Encoding'];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash omit
native delete

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested in the microbenchmark.

**What is being tested?**

The test compares two approaches:

1. **Lodash `_.omit()` method**: This is a JavaScript library function that removes specified properties from an object.
2. **Native `delete` operator**: This is a built-in JavaScript operator used to delete a property from an object.

**Options compared**

In this benchmark, we have two test cases:

1. `lodash omit`: Uses the Lodash library's `_omit()` method to remove the `'Accept-Encoding'` property from the `allHeaders` object.
2. `native delete`: Directly uses the native `delete` operator to delete the `'Accept-Encoding'` property from the `allHeaders` object.

**Pros and cons of each approach**

**Lodash `_omit()` method:**

Pros:

* Provides a more elegant and readable way to remove properties from an object.
* Can be used with other Lodash functions for more complex operations.

Cons:

* Requires including the Lodash library in your code, which may add unnecessary size to your bundle.
* May have slightly slower performance due to the overhead of calling a function.

**Native `delete` operator:**

Pros:

* Faster execution since it's a built-in operator that doesn't require a function call.
* No additional libraries or dependencies are required.

Cons:

* Can be less readable and more verbose for complex property removals.

**Other considerations**

Both approaches have their use cases. If you need to perform more complex object manipulation, the Lodash library may provide a convenient and efficient solution. However, if you're looking for a lightweight and performance-critical solution, using native `delete` operators might be the better choice.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks such as array manipulation, object transformation, and more. The `_omit()` method is part of this library and helps remove specified properties from an object.

In this benchmark, using Lodash simplifies the code and reduces the amount of boilerplate required to achieve the desired result. However, it does come with a slight performance penalty due to the function call overhead.

**Special JS feature: None**

There are no special JavaScript features or syntax used in this benchmark beyond the standard language features (e.g., objects, arrays, `delete` operator).

**Alternatives**

Other alternatives for removing properties from an object include:

* Using the `Object.keys()` and `splice()` methods to remove a property by its key.
* Using a recursive approach with `Object.assign()` and array manipulation.
* Utilizing other JavaScript libraries or frameworks that provide similar functionality, such as Underscore.js.

However, these alternatives may not offer the same level of conciseness and readability as Lodash's `_omit()` method.

Related benchmarks:

lodash omit vs native (version: 0)

Comparing performance of: lodash omit vs native delete

Created: 6 years ago by: Guest

Jump to the latest result

lodash omit

native delete

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):