Benchmark: test native vs lodash 1

HTML Preparation code:

<script src="lodash.js"></script>

Script Preparation code:

var array = [...Array(100000).keys()];

Tests:

Native
array.map((a) => a + 1).filter((a) => a % 2).map((a) => a - 1)
Lodash
_.chain(array).map((a) => a + 1).filter((a) => a % 2).map((a) => a - 1).value()

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):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition**

The provided JSON represents a benchmark test between native JavaScript and Lodash for an array processing task. The test is designed to compare the performance of these two approaches:

1. **Native**: The first test case uses only native JavaScript functions (e.g., `map`, `filter`, `reduce`) without any external libraries.
2. **Lodash**: The second test case utilizes the Lodash library, which provides a set of functional programming helpers and utilities.

**Options Compared**

The two approaches are compared in terms of their performance on the specified task:

* Native JavaScript:
	+ Pros: No external dependencies, potentially faster execution due to optimized browser engines.
	+ Cons: May require more manual iteration or optimization for large datasets.
* Lodash:
	+ Pros: Simplifies complex operations with high-level functions (e.g., `chain`, `map`), reduces boilerplate code.
	+ Cons: Introduces an external dependency, may incur overhead due to the library itself.

**Other Considerations**

When choosing between native JavaScript and Lodash for array processing tasks:

* **Data size**: For small datasets, the difference in performance might be negligible. However, for larger datasets, native JavaScript might perform better.
* **Code complexity**: If your code requires complex operations with many iterations, using a library like Lodash can simplify the process but may also introduce overhead.
* **Performance-critical sections**: In performance-critical areas of your application, it's often best to stick with native JavaScript to minimize external dependencies.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides over 120 functions for tasks such as:

* Array manipulation (e.g., `map`, `filter`, `reduce`)
* String processing (e.g., `trim`, `split`)
* Object manipulation (e.g., `pick`, `omit`)
* Functionality extension (e.g., `bind`, `curry`)

Lodash's `chain` function is particularly useful for creating a series of functions to be applied to an object or array in sequence.

**Special JS Feature: ES6+ Syntax**

MeasureThat.net tests the performance of JavaScript code using modern syntax features, such as:

* Arrow functions (`(a) => a + 1`)
* Template literals (`<script src="lodash.js"></script>`)

These features are often used in modern JavaScript applications and can provide better readability and conciseness. However, they may not be supported by older browsers or environments.

**Alternatives**

If you're looking for alternative libraries or approaches to array processing:

1. **Underscore.js**: Another popular utility library that provides a similar set of functions as Lodash.
2. **Ramda**: A functional programming library that offers a different set of curried functions for manipulating arrays and objects.
3. **Native JavaScript alternatives**: Consider using `forEach`, `for...of` loops, or `Promise.all()` instead of Lodash's high-level functions.

Keep in mind that each alternative has its strengths and weaknesses, and the choice ultimately depends on your specific requirements and performance constraints.

Related benchmarks:

test native vs lodash 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):