Optimal method for categorizing a product list by category | Utilizing Javascript

I'm currently exploring the most efficient method to categorize and analyze products in O(n) to extract valuable insights from different categories.

Here is a snippet of the data I am working with:

[
    {
        "code": 25754,
        "description": "BLUETOOTH USB AUDIO RECEIVER P2 ADAPTER",
        "price": 5.0,
        "stock": 10,
        "category": {
            "id": 1,
            "name": "Adapters"
        }
    },
    {
        "code": 20212,
        "description": "HDMI FEMALE TO FEMALE CONNECTOR ADAPTER",
        "price": 2.8,
        "stock": 20,
        "category": {
            "id": 2,
            "name": "Electronics"
        }
    },
]

To reverse the association, creating a list of categories with their corresponding products, I have devised the following solution:

function group_by_categories(products) {
    const categories = {}

    for (const product of products) {
        const { category, ...cleanedProduct } = product
        categories[category.id] = categories[category.id] || category
        categories[category.id].products = categories[category.id].products || []
        categories[category.id].products.push(cleanedProduct)
    }

    return Object.values(categories)
}

// The resulting structure:

[
  { id: 1, name: 'Adapters', products: [ [Object] ] },
  { id: 2, name: 'Electronics', products: [ [Object] ] }
]

However, I am facing challenges in two key areas.

  1. Is this approach optimal for reversing the relationship? How could I replicate this logic in a language like C without object-oriented features?

  2. Once we organize data this way, is it only possible to iterate through categories and products in O(n²)?

Any help or insight would be greatly appreciated, even if you can address just one of these questions. And please pardon any mistakes in my English as I strive to convey my thoughts clearly.

Answer №1

There are three main issues that need to be addressed in your code: 1) Instead of using all data as keys, focus on mapping connections, 2) Use sets rather than arrays for faster performance, and 3) Implement an efficient data structure to prevent duplicating work.

  1. It is crucial to map only the connections and not all the data. The unique product code should serve as the key and the category ID should also be considered unique. By structuring the mappings with minimal unique data, you can significantly improve performance. Using sets along with a hash object will ensure quick lookup, size check, inclusion test, and mapping back to original data.
  2. Emphasize mapping connections over storing all data. Utilizing sets and hashes will enhance efficiency by minimizing redundant information. Also, consider utilizing a database for more complex queries and optimal performance.

To optimize your system, follow these steps:

  • Create a simple code:product object to answer queries related to product records.
  • Use a category_id:set(codes) lookup to determine codes within a certain category. Ensure to use sets for fast operations.
  • Check the number of products within a specific category using data[category].size.

Implement dictionaries and sets for better performance. Building lookups should have a time complexity of O(P), where P is the total number of products.

To avoid inefficient performance, calculate the lookup once and update it when necessary. Prioritize updating the lookup itself instead of rebuilding it entirely each time. In scenarios where the number of products is manageable, building the lookup at each request using minimal data may be practical.

Ensure your category code resembles this structure after implementing the methods.

...
category_lookup = build_category_lookup() # O(P)
product_lookup = build_product_lookup() # O(P)
...
products_for_category = product_lookup[category_id] # O(1)
products_count = products_for_category.length # O(1) | C: sizeof(products_for_category)
...

Opt for snake_case naming conventions, especially when coding in Ruby.

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