Benchmark: R vs Native - MeasureThat.net

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/ramda/0.22.1/ramda.min.js"></script>
<script src="https://cdn.jsdelivr.net/lodash/4/lodash.min.js"></script>

Script Preparation code:

let state = {
    channel: {
        '102': {
            data: {
                id: 102,
                isFavorite: false,
                name: '102',
            },
        },
    },
};

let payload = [
    {
        id: 100,
        isFavorite: false,
        name: '100',
    },
    {
        id: 101,
        isFavorite: true,
        name: '101',
    }
];

const omitStoreEntities = (entity) => _.omit(entity, ['isFavorite', 'position']);

function nativeJS() {
    const mapped = payload.reduce(
        (acc, channel) => ({
            ...acc,
            [channel.id]: {
                data: omitStoreEntities(channel),
                status: 'loaded',
                error: null,
            },
        }),
        {}
    );

    return {
        ...state,
        ...mapped,
    };
}

const setChannel = R.compose(
    R.assoc,
    R.toString
);

function ramdaF() {
    return R.compose(
        R.merge(state),
        R.reduce(
            (acc, channel) =>
                setChannel(channel.id)(
                    {
                        data: omitStoreEntities(channel),
                        status: 'loaded',
                        error: null,
                    },
                    acc
                ),
            {}
        )
    )(payload);
};

Tests:

Native
nativeJS()
R
ramdaF()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Native
R

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark test case.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark that compares the execution performance of two different approaches: Native (using native JavaScript) and R (using Ramda, a functional programming library).

**Options Compared**

Two options are compared:

1. **Native**: The native JavaScript implementation, which is written in pure JavaScript. This approach uses vanilla JavaScript features to process the data.
2. **R**: The Ramda implementation, which uses the Ramda library for functional programming. This approach leverages Ramda's higher-order functions and immutable data structures.

**Pros and Cons**

Here are some pros and cons of each approach:

**Native**

Pros:

* Faster execution: Native code can be optimized for specific hardware and execute more efficiently.
* Low overhead: No additional dependencies or library overhead.

Cons:

* More complex implementation: Requires manual iteration over the data using loops.
* Limited expressiveness: May not be as concise or readable as functional programming approaches.

**R**

Pros:

* Concise implementation: Ramda's higher-order functions make it easier to write expressive and concise code.
* Immutability: Immutable data structures reduce the risk of side effects and make the code easier to reason about.

Cons:

* Higher overhead: Requires loading an additional library (Ramda) and its dependencies.
* Steeper learning curve: Familiarity with functional programming concepts and Ramda's API is required.

**Library**

The `ramda` library provides a set of higher-order functions for functional programming, including `R.compose`, `R.assoc`, and others. It allows developers to write more concise and composable code. The `_` symbol refers to the Lodash library, which extends Ramda with additional utility functions.

**Special JS Feature**

There is no special JavaScript feature or syntax used in this benchmark.

**Alternatives**

If you're interested in exploring alternative approaches, consider:

* **Functional programming libraries**: Other options like Lodash, Underscore.js, or the native ES6 `Array.prototype.reduce()` method can be used to implement functional programming styles.
* **Native module implementation**: If performance is critical, using a native module (e.g., WebAssembly) could provide additional optimization opportunities.

Keep in mind that these alternatives may introduce additional complexity, overhead, or dependencies, so it's essential to weigh the trade-offs before choosing an approach.

Related benchmarks:

R vs Native (version: 0)

Comparing performance of: Native vs R

Created: 7 years ago by: Guest

Jump to the latest result

Native

R

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):