Benchmark: Ramda vs Native (after TS)

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:

var __assign = (this && this.__assign) || function () {
    __assign = Object.assign || function(t) {
        for (var s, i = 1, n = arguments.length; i < n; i++) {
            s = arguments[i];
            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
                t[p] = s[p];
        }
        return t;
    };
    return __assign.apply(this, arguments);
};
var state = {
    channel: {
        '102': {
            data: {
                id: 102,
                isFavorite: false,
                name: '102',
            },
        },
    },
};
var payload = [
    {
        id: 100,
        isFavorite: false,
        name: '100',
    },
    {
        id: 101,
        isFavorite: true,
        name: '101',
    }
];
var omitStoreEntities = function (entity) { return _.omit(entity, ['isFavorite', 'position']); };
function nativeJS() {
    var mapped = payload.reduce(function (acc, channel) {
        var _a;
        return (__assign({}, acc, (_a = {}, _a[channel.id] = {
            data: omitStoreEntities(channel),
            status: 'loaded',
            error: null,
        }, _a)));
    }, {});
    return __assign({}, state, mapped);
}
var setChannel = R.compose(R.assoc, R.toString);
function ramdaF() {
    return R.compose(R.merge(state), R.reduce(function (acc, channel) {
        return 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:

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested.

**Benchmark Definition**

The benchmark is comparing two approaches: native JavaScript (using `nativeJS()`) and Ramda (a functional programming library) (using `ramdaF()`).

**Native Approach (`nativeJS()`)**

In this approach, we have a `nativeJS` function that:

1. Uses the `payload` array to create a new object with transformed data using the `reduce()` method.
2. Iterates over each item in the `payload` array and creates a new object with `data` set to the result of `omitStoreEntities(channel)`, where `channel` is an object from the `state` object.
3. Merges the resulting objects into the original `state` object using the `__assign` function.

**Ramda Approach (`ramdaF()`)**

In this approach, we have a `ramdaF` function that:

1. Uses Ramda's functional programming library to transform the data in the `payload` array.
2. Composes several functions:
	* `R.merge(state)` merges an object into the original `state` object.
	* `R.reduce()` applies a reduction function to each item in the `payload` array.
	* `setChannel(id)()` creates a new object with the channel ID as a key and an initial value of `{ status: 'loaded', error: null }`.
3. The final result is merged into the original `state` object using `R.merge(state)`.

**Library**

Ramda is a functional programming library that provides a set of higher-order functions for working with data. In this benchmark, we're using Ramda's `reduce()`, `merge()`, and `assoc()` (an alias for `setChannel()`) functions to transform the data.

**Special JavaScript Feature/Syntax**

There are no special JavaScript features or syntax used in these functions that would require additional explanation beyond the functional programming concepts illustrated by Ramda.

**Other Considerations**

When comparing these two approaches, we need to consider factors such as:

* Performance: How does each approach compare in terms of execution speed?
* Code Readability and Maintainability: Are the functions easy to understand and modify?

**Alternatives**

If you wanted to write a native JavaScript implementation similar to `nativeJS()`, you could use other libraries or techniques, such as:

* Lodash (a utility library with a subset of Ramda's functionality)
* The Array methods (`forEach()`, `map()`, `reduce()`) and the `Object.assign()` method for merging objects.
* A simple loop-based implementation without using any external libraries.

Keep in mind that each approach has its own trade-offs, and the choice ultimately depends on your specific requirements and preferences.

Related benchmarks:

Ramda vs Native (after TS) (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):