Benchmark: GroupBy - MeasureThat.net

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/3.10.1/lodash.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>

Script Preparation code:

function Rand(x) {
  return Math.floor(Math.random() * x);
}

const arr = R.map(x => { return {room: {name: Rand(x)}, structureType: Rand(x)}; }, R.repeat(30, 10000));

function GetArray() { return arr; }

function SortMyStructs()
{
    Game = {};
  	Game.structures = GetArray();

    Game.mStructs = { };
    Game.mStructs.mRooms = { };
    Game.mObservers = [ ];

    for (let structIter in Game.structures)
    {
        const structure = Game.structures[structIter];
        const roomName = structure.room.name;
        const type = structure.structureType;

        if (undefined === Game.mStructs.mRooms[roomName])
        {
            Game.mStructs.mRooms[roomName] = { };
            Game.mStructs.mRooms[roomName].mAll = [ ];
            Game.mStructs.mRooms[roomName].mType = { };
        }

        Game.mStructs.mRooms[roomName].mAll.push(structure);

        if (undefined === Game.mStructs.mRooms[roomName].mType[type])
        {
           Game.mStructs.mRooms[roomName].mType[type] = [ ];
        }

        Game.mStructs.mRooms[roomName].mType[type].push(structure);
    }
};

Tests:

Lodash groupBy
const tmp = _.groupBy(GetArray(), x => x.room.name); const tmp2 = _.forEach(tmp, (val, key) => tmp[key] = _.groupBy(tmp[key], x => x.structureType)); tmp2;
Ramda groupBy
R.pipe( R.groupBy(x => x.room.name), R.map(R.groupBy(x => x.structureType)) )(GetArray());
Hand written
SortMyStructs();
Using Reduce
_.reduce(GetArray(), (acc, x) => { if (!acc.mRooms) acc.mRooms = {}; if (!acc.mRooms[x.room.name]) acc.mRooms[x.room.name] = {mAll: [], mType: {}}; acc.mRooms[x.room.name].mAll.push(x); if (!acc.mRooms[x.room.name].mType[x.structureType]) acc.mRooms[x.room.name].mType[x.structureType] = []; acc.mRooms[x.room.name].mType[x.structureType].push(x); return acc; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash groupBy
Ramda groupBy
Hand written
Using Reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 11 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36

Browser/OS: Chrome 136 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Lodash groupBy	4511.0 Ops/sec
Ramda groupBy	2792.8 Ops/sec
Hand written	4413.2 Ops/sec
Using Reduce	6401.6 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The benchmark is designed to compare the performance of three different approaches for grouping data by two criteria: room name and structure type. The benchmark uses a JavaScript array of 30,000 objects, each containing a room and a structure type.

**Library Used: Ramda**

Ramda is a functional programming library that provides various utility functions for working with arrays and other data structures. In this benchmark, the `R.groupBy` function from Ramda is used to group the data by room name and structure type. The `R.map` function is also used to apply another grouping operation.

**Library Used: Lodash**

Lodash is a JavaScript library that provides various utility functions for working with arrays, objects, and other data structures. In this benchmark, the `_groupBy` function from Lodash is used to group the data by room name and structure type.

**Hand Written Approach**

The hand written approach uses a simple iterative loop to group the data by room name and structure type.

**Comparison of Approaches**

Here's a brief overview of each approach:

1. **Lodash `groupBy`**: This function groups an array by a specified property and returns an object with the grouped values.
2. **Ramda `groupBy`**: This function is similar to Lodash's `_groupBy`, but it uses a more functional programming style to group the data.
3. **Hand Written Approach**: This approach uses a simple iterative loop to group the data by room name and structure type.

**Pros and Cons of Each Approach**

Here are some pros and cons of each approach:

1. **Lodash `groupBy`**:
	* Pros: Easy to use, well-documented.
	* Cons: May not be as efficient as other approaches for large datasets.
2. **Ramda `groupBy`**:
	* Pros: More functional programming style, which can make the code more concise and readable.
	* Cons: May require more effort to understand and use, especially for those without experience with functional programming.
3. **Hand Written Approach**:
	* Pros: Can be more efficient than other approaches for large datasets, as it avoids the overhead of a library function call.
	* Cons: Requires more code and may be less readable than other approaches.

**Other Considerations**

When choosing an approach, consider the following factors:

1. **Performance**: If performance is critical, the hand written approach or Ramda's `groupBy` may be faster for large datasets.
2. **Readability**: If readability is important, Lodash's `_groupBy` or Ramda's `groupBy` may be a better choice.
3. **Maintainability**: If maintainability is crucial, Lodash's `_groupBy` or Ramda's `groupBy` may be easier to update and modify in the future.

**Alternatives**

Other approaches for grouping data by two criteria include:

1. **D3.js's `groupBy`**: A popular data visualization library that also provides a `groupBy` function.
2. **Moment.js's `groupBy`**: A date and time manipulation library that includes a `groupBy` function.
3. ** vanilla JavaScript approaches**: There are many examples of vanilla JavaScript approaches for grouping data, including using `Array.prototype.reduce()` or `Array.prototype.forEach()`.

Related benchmarks:

GroupBy (version: 15)

Lodash vs Ramda

Comparing performance of: Lodash groupBy vs Ramda groupBy vs Hand written vs Using Reduce

Created: 8 years ago by: Registered User

Jump to the latest result

Lodash groupBy

Ramda groupBy

Hand written

Using Reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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