Benchmark: Lodash.js vs Native 123 1

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.21/lodash.min.js"></script>

Script Preparation code:

var data = new Array(1).fill({'firstName':'First', 'lastName': 'Last'});


var printFullName = (firstName, lastName) => console.log(firstName+' '+lastName);

Tests:

Native
data.map(item => printFullName(item.firstName, item.lastName));
Lodash
_.map(data, item => printFullName(item.firstName, item.lastName))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
Lodash

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):

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Let's break down the provided benchmark definition and test cases to understand what is being tested.

**Benchmark Definition**

The benchmark uses two different approaches: native JavaScript (using arrow functions) and Lodash, a popular JavaScript utility library.

1. **Native JavaScript**: The first test case uses native JavaScript to map over an array of objects, printing the full name of each item using an anonymous arrow function.
2. **Lodash**: The second test case uses Lodash's `map` function to achieve the same result as the native JavaScript implementation.

**Options Compared**

The benchmark compares the performance of two different approaches:

1. Native JavaScript (using arrow functions)
2. Lodash (`_.map` function)

**Pros and Cons**

**Native JavaScript:**

Pros:

* Faster execution since it doesn't require an external library to be loaded.
* More control over the implementation, which can lead to optimized code.

Cons:

* Requires manual iteration over the array elements, which might be slower for large datasets.
* The arrow function syntax is newer and may not be supported in older browsers.

**Lodash:**

Pros:

* Provides a simple and concise way to perform common data manipulation tasks.
* Optimized for performance by using a V8 (the same engine used by Google Chrome) internal iterator.
* Faster execution since it's optimized for the specific use case.

Cons:

* Requires loading an external library, which can increase overhead.
* May not be as flexible or customizable as native JavaScript implementations.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a comprehensive set of functions for common data manipulation tasks. In this benchmark, it's used to compare the performance of its `map` function with native JavaScript. The `_` prefix refers to the Lodash namespace.

The `_.map` function takes an array and a callback function as arguments and returns a new array with the results of applying the callback function to each element in the original array.

**Special JS Feature or Syntax**

None mentioned in this benchmark definition.

**Other Considerations**

When choosing between native JavaScript and Lodash for data manipulation tasks, consider the following:

* For small datasets or simple operations, native JavaScript might be a better choice due to its simplicity and lack of overhead.
* For larger datasets or complex operations, Lodash's optimized functions can provide significant performance benefits.

**Alternatives**

If you prefer not to use an external library like Lodash, you can also consider:

1. Other utility libraries: e.g., Ramda, Underscore.js (the predecessor to Lodash), or the new JavaScript standard library (`Array.prototype.map` and `Array.prototype.forEach`)
2. In-house solutions: create your own utility functions using native JavaScript for specific data manipulation tasks.

Keep in mind that each alternative has its pros and cons, and some might be more suitable for your use case than others.

Related benchmarks:

Lodash.js vs Native 123 1 (version: 0)

Comparing performance of: Native vs Lodash

Created: 3 years ago by: Guest

Jump to the latest result

Native

Lodash

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):