Benchmark: fdqwefd - MeasureThat.net

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Tests:

lodash
const a = {aa: 'aa', bb: 'bb', cc: 'cc', dd: 'dd', ee: 'ee'} const b = _.omit(a, ['aa', 'bb', 'ee'])
regular
const a = {aa: 'aa', bb: 'bb', cc: 'cc', dd: 'dd', ee: 'ee'} const b = ['aa', 'bb', 'ee'].reduce((acc, key) => { const { [key]: _omit, ...rest } = acc return rest })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
regular

Fastest: N/A

Slowest: N/A

Latest run results:

No previous 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 benchmark definition and test cases to understand what is being tested.

**Benchmark Definition JSON**

The benchmark definition consists of four main parts:

1. **Name**: A unique name for the benchmark, which in this case is "fdqwefd".
2. **Description**: A brief description of the benchmark, which is "qwefqwef". This information is not used by MeasureThat.net.
3. **Script Preparation Code**: An optional code that can be executed before running the benchmark. In this case, it's null, meaning no script preparation code is required.
4. **Html Preparation Code**: A HTML code that includes a reference to an external JavaScript library, which in this case is Lodash (version 4.17.5).

**Individual Test Cases**

The test cases are defined within the "Benchmark Definition" JSON as an array of objects. Each object contains:

1. **Benchmark Definition**: The actual benchmark code, which defines two functions: `const a = {aa: 'aa', bb: 'bb', cc: 'cc', dd: 'dd', ee: 'ee'}` and `const b = _.omit(a, ['aa', 'bb', 'ee'])`. The second function uses the Lodash library to remove specific properties from object `a`.
2. **Test Name**: A name for each test case, which in this case is "lodash" and "regular".

**Options Compared**

The two options being compared are:

1. **Lodash (_.omit)**: Using the `_.omit` function from Lodash to remove specific properties from object `a`.
2. **Regular Reduction**: Implementing a custom reduction function to achieve the same result as `_.omit`. This involves using the `reduce` method and destructuring assignment.

**Pros and Cons**

1. **Lodash (.omit)**:
	* Pros:
		+ Well-tested and widely used library.
		+ Provides a concise and expressive way to remove properties from objects.
	* Cons:
		+ Introduces an additional dependency (Lodash).
		+ May not be optimized for specific use cases.
2. **Regular Reduction**:
	* Pros:
		+ Customizable and flexible.
		+ No additional dependencies required.
	* Cons:
		+ More verbose and complex to implement correctly.

**Library: Lodash**

Lodash is a popular JavaScript library that provides various utility functions for tasks such as array manipulation, object transformation, and more. In this benchmark, `_.omit` is used to remove specific properties from objects.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax being tested in this benchmark. The code is standard JavaScript and uses modern ES6+ features.

**Other Alternatives**

If you wanted to implement a custom solution without using Lodash, you could use other libraries like `omit` from the `ramda` library (similar to Lodash but with different API) or implement your own reduction function using recursion or iteration. However, these alternatives would require additional dependencies and may not be as concise or efficient as using `_.omit`.

For regular expressions, you can use a simple regex like `/aa|bb|ee/g` in combination with the `replace()` method to achieve similar results.

In summary, MeasureThat.net is testing the performance of two approaches: using Lodash's `_.omit` function and implementing a custom reduction function. The benchmark highlights the trade-offs between using established libraries (Lodash) versus custom solutions, including dependencies, conciseness, and performance.

Related benchmarks:

fdqwefd (version: 0)

qwefqwef

Comparing performance of: lodash vs regular

Created: 4 years ago by: Guest

Jump to the latest result

lodash

regular

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):