MongoDB provides several options for storing nested categories efficiently. The choice depends on your specific requirements and the nature of your data. Here are three common approaches:

1 - Embedded Documents:

You can store nested categories as embedded documents within a parent document. This approach is suitable when the depth of nesting is relatively shallow and the categories are not frequently updated independently. The structure would look something like this:

{
  _id: ObjectId("parent_id"),
  name: "Parent Category",
  subcategories: [
    {
      name: "Subcategory 1",
      subcategories: [
        { name: "Sub-subcategory 1" },
        { name: "Sub-subcategory 2" }
      ]
    },
    {
      name: "Subcategory 2",
      subcategories: [
        { name: "Sub-subcategory 3" },
        { name: "Sub-subcategory 4" }
      ]
    }
  ]
}

The advantage of this approach is that it allows you to retrieve the entire category hierarchy in a single query. However, keep in mind that updating or removing a subcategory can be more complex and may require modifying the entire parent document.

2 - Parent Reference:

Instead of embedding the subcategories within the parent document, you can store references to the subcategories using their IDs. Each category document would have a reference to its parent category. This approach is suitable when the depth of nesting is variable and the categories are frequently updated independently. The structure would look something like this:

{
  _id: ObjectId("category_id"),
  name: "Category",
  parent: ObjectId("parent_category_id")
}

With this approach, you can retrieve the entire category hierarchy by performing multiple queries to fetch each level. Updating or removing a subcategory is easier since it only requires modifying the relevant document.

3 - Materialized Paths:

Materialized paths involve storing the full path of each category as a string in a dedicated field. The path can include the category names or IDs separated by a delimiter (e.g., slashes). This approach is suitable when you need to perform hierarchical queries efficiently. The structure would look something like this:

{
  _id: ObjectId("category_id"),
  name: "Category",
  path: "/parent/subcategory"
}

With materialized paths, you can retrieve categories based on their paths using simple string-matching queries. However, updating or rearranging categories can be more complex and may require updating multiple documents.

Each of these approaches has its trade-offs, and the most efficient one depends on your specific use case. Consider factors like the depth of nesting, frequency of updates, querying requirements, and the size of your dataset when deciding which approach to use.