Benchmark: reduce native vs lodash

HTML Preparation code:

<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.11/lodash.core.js"></script>

Script Preparation code:

var array = [];
for (var i = 0; i < 100000; ++i) array[i] = i;

Tests:

native
array.reduce(function(prev, item) { return prev + item; }, 0);
lodash
_.reduce(array, function(prev, item) { return prev + item; }, 0);

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 dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition:**
The benchmark is designed to compare two approaches for summing up an array of numbers using the `reduce` method:

1. **Native Approach:** Using only standard JavaScript without any external libraries.
2. **Lodash Approach:** Utilizing the popular Lodash library, a utility belt for functional programming in JavaScript.

**Script Preparation Code:**
The script preparation code generates an array of 100,000 numbers from 0 to 99,999, which will be used as input for the benchmark.

**HTML Preparation Code:**
The HTML preparation code includes a link to load the Lodash library (version 4.17.11) asynchronously in the background.

**Individual Test Cases:**

1. **Native Approach:** The first test case uses only standard JavaScript and the `reduce` method without any external libraries.
```javascript
array.reduce(function(prev, item) { return prev + item; }, 0);
```
2. **Lodash Approach:** The second test case utilizes Lodash's `reduce` function from its core library, which provides a more efficient implementation of the reduction algorithm.

Now, let's discuss the pros and cons of each approach:

**Native Approach:**

Pros:

* No external dependencies or overhead.
* Can be optimized for performance using various techniques (e.g., loop unrolling, caching).

Cons:

* May require additional setup and configuration to achieve optimal performance.
* Might not be as readable or maintainable due to the complexity of the implementation.

**Lodash Approach:**

Pros:

* Provides a well-tested and optimized implementation of the `reduce` method.
* Can simplify code by allowing for more functional programming styles.

Cons:

* Introduces an external dependency, which may lead to slower performance due to overhead.
* May not be as performant as the native approach, especially for large inputs.

**Library: Lodash**

Lodash is a popular utility belt for functional programming in JavaScript. Its `reduce` function provides several benefits:

* Optimized implementation of the reduction algorithm.
* Supports various options and edge cases (e.g., initial value, callback type).
* Allows for more concise and readable code.

However, using an external library also introduces additional overhead due to:

* Importing the library's bundle size.
* Potential conflicts with existing libraries or frameworks.
* Dependence on the library's maintainers and versioning.

**Special JS Features/Syntax:**
There are no special JavaScript features or syntax used in this benchmark. The code focuses solely on demonstrating the performance difference between two approaches using standard JavaScript and Lodash.

**Other Alternatives:**

If you're interested in exploring other alternatives, consider the following options:

* **Built-in Array.prototype.reduce() method:** Most modern browsers support this method natively, which might be a good middle ground between the native approach and the Lodash approach.
* **Custom implementation with caching and optimization:** You can implement your own reduction algorithm using caching and optimization techniques to achieve better performance without relying on an external library.
* **Other libraries or frameworks:** Depending on your specific use case, other libraries like Ramda or MooTools might provide alternative implementations of the `reduce` method.

Keep in mind that each approach has its trade-offs, and the best choice ultimately depends on your project's requirements, performance constraints, and personal preferences.

Related benchmarks:

reduce native vs lodash (version: 0)

Comparing performance of: native vs lodash

Created: 6 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):