Benchmark: Filter-Map: Lodash FP vs Native

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/g/lodash@4(lodash.min.js+lodash.fp.min.js)"></script>

Script Preparation code:

var data = Array(1000000).fill({ filtering: true, mapping: 42 });

Tests:

Native filter-map
data .filter(({ filtering }) => filtering) .map(({ mapping }) => mapping)
Lodash FP filter-map
_.pipe( _.filter(({ filtering }) => filtering), _.map(({ mapping }) => mapping) )(data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native filter-map
Lodash FP filter-map

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):

LLMs can make mistakes. Check important info.

Measuring the performance of two approaches to implement the `filter` and `map` functions in JavaScript is an interesting benchmark.

**Overview**

The provided JSON represents a benchmark that compares two approaches to achieve the same result: using native JavaScript (`filter` and `map` methods) versus utilizing the Lodash library with its functional programming (FP) capabilities. The goal of this benchmark is to determine which approach is more efficient in terms of performance.

**Options Compared**

The benchmark tests two options:

1. **Native JavaScript**: This option uses the built-in `filter` and `map` functions provided by JavaScript, which are part of the ECMAScript standard.
2. **Lodash FP**: This option utilizes Lodash's functional programming utilities, specifically the `_.filter` and `_.map` methods.

**Pros and Cons**

### Native JavaScript

Pros:

* No external dependency (no need to load Lodash)
* Performance might be better due to native implementation
* Easy to understand and use for developers familiar with native JavaScript

Cons:

* May require more boilerplate code compared to the FP approach
* Might not be as concise or readable for complex operations

### Lodash FP

Pros:

* More concise and readable syntax, especially for complex operations
* Utilizes optimized C++ implementations under the hood
* Can lead to better performance due to lazy evaluation and caching

Cons:

* Requires loading an external dependency (Lodash)
* Might have higher overhead compared to native JavaScript

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a comprehensive set of functional programming utilities, including `_.filter` and `_.map`. The FP version of these methods utilize lazy evaluation and caching to optimize performance.

**Special JS Feature/ Syntax**

There are no special JavaScript features or syntax used in this benchmark. However, the FP approach relies on understanding functional programming concepts, such as higher-order functions and closures.

**Other Alternatives**

If you're looking for alternative approaches to implement `filter` and `map`, you might consider:

1. **React Query**: A popular library for managing data fetching and caching.
2. **RxJS**: A reactive library that provides observables and operators, similar to FP in Lodash.
3. **ES6+ Iterators**: You can also use native JavaScript iterators and the `for...of` loop to achieve similar results.

In summary, the benchmark compares two approaches: using native JavaScript (`filter` and `map`) versus utilizing Lodash's functional programming utilities (FP). The choice of approach depends on your specific requirements, personal preference, and familiarity with the libraries.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Linux

View result in a separate tab

Test name	Executions per second
Native filter-map	26.9 Ops/sec
Lodash FP filter-map	35.4 Ops/sec

Related benchmarks:

Filter-Map: Lodash FP vs Native (version: 0)

Comparing performance of: Native filter-map vs Lodash FP filter-map

Created: 5 years ago by: Guest

Jump to the latest result

Native filter-map

Lodash FP filter-map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 29 days ago):