MongoDB Schema Design and Data Modeling Flashcards

What is the primary trade-off when using the Subset Pattern in MongoDB schema design?

Answer: Reduced document size at the cost of requiring more queries for complete data

The Subset Pattern splits a document into a 'hot' subset (frequently accessed fields in the main document) and a 'cold' subset (rarely accessed fields in a separate document). This reduces working set size but requires an additional query to access full data.

In MongoDB, what does schema validation using $jsonSchema allow you to enforce?

Answer: Required fields, field types, value ranges, and custom validation rules at the database level

MongoDB's $jsonSchema validator enforces document structure at the database level. You can specify required fields, BSON types for fields, minimum/maximum values, string patterns, and more. Validation can be set to 'error' (reject invalid docs) or 'warn' mode.

When modeling a many-to-many relationship in MongoDB, which approach avoids duplication while keeping queries efficient?

Answer: Use an intermediate linking collection or store arrays of references on one or both sides

For many-to-many relationships, a common approach is to store arrays of ObjectId references in documents on one or both sides. For complex cases, an intermediate collection (junction collection) explicitly models the relationship and can carry relationship-specific attributes.

What is a key indicator that you should move from embedding to referencing in an existing schema?

Answer: Embedded arrays are growing unboundedly, risking the 16 MB document limit

When embedded arrays grow without bound — e.g., an activity log embedded in a user document — the document can eventually exceed the 16 MB BSON limit. This is a clear signal to switch to referencing, storing related records in a separate collection.

Which field is automatically created by MongoDB for every document if not explicitly provided?

Answer: _id

MongoDB automatically creates an _id field for every document if one is not provided. By default, this is an ObjectId — a 12-byte BSON type that encodes a timestamp, machine identifier, process ID, and a random increment, ensuring global uniqueness.

What does the Anti-Pattern of using MongoDB as a key-value store with large arbitrary JSON blobs typically cause?

Answer: Inability to use indexes, poor query performance, and difficulty filtering on nested data

Storing arbitrary JSON blobs without a defined schema means MongoDB cannot create effective indexes on nested fields. Queries require scanning entire documents, indexes cannot be leveraged, and the application must parse large payloads client-side — a common anti-pattern.