Benchmark: Ramda values vs Object.values

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Tests:

Ramda values
const data = { key: "abc" } R.values(data);
Object.values()
const data = { key: "abc" } Object.values(data);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda values
Object.values()

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Windows

View result in a separate tab

Test name	Executions per second
Ramda values	4537895.5 Ops/sec
Object.values()	5482040.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain what's being tested in the provided benchmark.

The benchmark is comparing two approaches: using Ramda's `R.values()` function and using JavaScript's built-in `Object.values()` method.

**Ramda's R.values()**

Ramda is a functional programming library for JavaScript. It provides various functions for data manipulation, filtering, mapping, reducing, and more. In this case, `R.values()` is used to extract all the values from an object. The purpose of using Ramda in this benchmark is likely to test its performance against the built-in JavaScript method.

**JavaScript's Object.values()**

This approach uses the built-in `Object.values()` method to extract all the property values from an object. This is a relatively modern feature introduced in ECMAScript 2015 (ES6).

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

**Ramda's R.values():**

Pros:

1. **Consistency**: Ramda's API is consistent across its various functions, making it easier to use and understand.
2. **Type Safety**: Ramda provides strong type safety, which can help prevent errors at runtime.

Cons:

1. **Performance Overhead**: Using a library like Ramda may introduce additional overhead due to the need for function calls, object lookups, and garbage collection.
2. **Memory Usage**: Depending on the size of the input data, using Ramda might consume more memory than the built-in JavaScript method.

**JavaScript's Object.values():**

Pros:

1. **Native Performance**: The built-in `Object.values()` method is implemented in native code, making it faster and more efficient than a library like Ramda.
2. **Low Memory Usage**: Using the built-in method typically consumes less memory, especially for large datasets.

Cons:

1. **Less Consistent API**: JavaScript's APIs can be less consistent across different browsers and versions, which might make it harder to use and understand.
2. **Potential Errors**: Without proper type checking or null checks, using `Object.values()` without caution can lead to errors or unexpected behavior.

The benchmark results show that the built-in `Object.values()` method outperforms Ramda's `R.values()` by a significant margin (approximately 20-25% better execution rate). This suggests that for simple cases like extracting values from objects, the native JavaScript method is a good choice. However, if you need more advanced data manipulation or filtering capabilities, Ramda might still be a viable option.

As for other alternatives, you could consider using:

1. **Array.prototype.reduce()` and `Object.keys()`: A combination of these two methods can achieve similar results to `Object.values()`, but with less concise syntax.
2. **Lodash**: Another popular JavaScript library that provides various utility functions, including those for working with objects.

Keep in mind that the choice ultimately depends on your specific use case, performance requirements, and personal preference.

Related benchmarks:

Ramda values vs Object.values (version: 0)

Comparing performance of: Ramda values vs Object.values()

Created: 4 years ago by: Guest

Jump to the latest result

Ramda values

Object.values()

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):