Benchmark: lodash (v4.17.15) map vs Object.keys map

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.15/lodash.min.js"></script>

Script Preparation code:

var value = {a: 30310, b: 100303, c: 3040494};

Tests:

lodash.map
_.map(value, (v) => v)
native
Object.keys(value).map((v) => v)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash.map
native

Fastest: N/A

Slowest: N/A

Latest 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.

The benchmark being tested is comparing the performance of two approaches to map over an object in JavaScript:

1. **Lodash (`_.map`)**: Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks like array manipulation, string concatenation, and more.
2. **Native `Object.keys`** (also referred to as "native" or "built-in"): This approach uses the built-in `Object.keys()` method to iterate over the object's keys and then maps each key-value pair using the `map()` method.

Now, let's discuss the pros and cons of these approaches:

**Lodash (`_.map`)**

Pros:

* **Convenience**: Lodash provides a simple and concise way to map over an object without having to worry about iterating over its keys.
* **Flexibility**: Lodash's `map()` function can be easily customized using callback functions.

Cons:

* **Overhead**: Using Lodash introduces additional overhead due to the library itself, which may impact performance.
* **Dependence on external libraries**: The benchmark relies on Lodash being loaded and executed before running the test, which could lead to inconsistencies if not properly handled.

**Native `Object.keys`**

Pros:

* **Low overhead**: Using built-in methods like `Object.keys()` has minimal additional overhead compared to using an external library.
* **Independence from external libraries**: This approach doesn't rely on any external libraries, making it more consistent and predictable.

Cons:

* **Verbosity**: Writing code that manually iterates over object keys can be more verbose and error-prone than using a built-in function like `map()`.
* **Potential for performance issues with large objects**: If the object is very large, iterating over its keys might introduce performance overhead.

Other considerations:

* **Use of callback functions**: Both approaches use callback functions (the arrow function in Lodash's case) to transform each key-value pair. While this allows for flexibility, it also adds additional complexity.
* **Language features**: Neither approach explicitly uses special JavaScript language features like generators or async/await. However, the `map()` method is a native array method that leverages the language's built-in iteration functionality.

Now, let's take a look at some specific libraries and features mentioned in the benchmark:

* **Lodash**: As mentioned earlier, Lodash provides the `_map` function used in this benchmark.
* **No special JavaScript features or syntax** are explicitly mentioned. However, it's worth noting that modern JavaScript versions (ES6+) do offer more concise ways to map over arrays and objects using `map()` and rest parameters.

Alternatives to these approaches could include:

* Using a different utility library like Moment.js for date manipulation
* Implementing custom iteration logic using loops or other techniques
* Leveraging language features like async/await for asynchronous iteration

In general, when choosing between performance-critical code paths like this benchmark, it's essential to consider factors like overhead, consistency, and maintainability.

Related benchmarks:

lodash (v4.17.15) map vs Object.keys map (version: 0)

lodash map vs Object.keys map

Comparing performance of: lodash.map vs native

Created: 6 years ago by: Guest

Jump to the latest result

lodash.map

native

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):