Managing Relationships
While denormalization simplifies data access, it also raises concerns about data consistency and redundancy. In scenarios where data updates are frequent or where the embedded data is shared across multiple documents, maintaining consistency becomes paramount. Let’s illustrate this with an example
Suppose you have a social media platform where users can follow each other. In a document-based database, you might represent the follower-followee relationship as follows:
{
"_id": "user1",
"name": "Alice",
"followers": ["user2", "user3"]
}
{
"_id": "user2",
"name": "Bob",
"followers": ["user1"]
}
Here, each user document maintains an array of follower IDs. While this design facilitates quick retrieval of a user’s followers, it introduces redundancy and complexity when updating follower lists. To address this, you might consider employing a reference model, where user IDs are stored instead of the entire user document
{
"_id": "user1",
"name": "Alice",
"followers": ["user2", "user3"]
}
{
"_id": "user2",
"name": "Bob"
}
In this revised design, each user document stores only the IDs of their followers, reducing redundancy and simplifying updates. However, retrieving follower details now requires additional queries.
Schema Design and Relationship in NoSQL Document-Base Databases
NoSQL databases are powerful alternatives to traditional relational databases, offering flexibility, scalability, and performance. Among the various types of NoSQL databases, document-based databases stand out for their ability to store and retrieve data in flexible, schema-less documents.
In this article, we will explore the intricacies of schema design and relationships in NoSQL document-based databases through real-world examples.