Couchbase handles document storage using a NoSQL approach, predominantly focusing on JSON documents. Each document is stored as a self-contained unit that includes both data and structure within a single entity. This means that developers can easily model complex data structures without the need for rigid tables and schemas, commonly found in traditional relational databases. Each document is uniquely identified by a key, enabling efficient retrieval and manipulation of data. For example, if you were to store user profiles, each profile would be a JSON document identified by a unique user ID, making access straightforward and quick.
Couchbase employs a distributed architecture, allowing documents to be stored across multiple nodes in a cluster. This ensures scalability and fault tolerance. When a new document is added, it can be stored on any available node in the cluster, which helps balance the load. The system uses an underlying storage engine that manages data persistence, so documents are safely stored on disk while maintaining memory for fast access. For instance, with Couchbase, if a cluster has three nodes and a user adds a new document, the system can automatically distribute that data to optimize performance and ensure redundancy.
Additionally, Couchbase provides built-in indexing options to enhance the speed of queries on documents. By creating indexes on specific fields within documents, developers can execute fast queries without needing to scan all documents. For example, if you're looking to retrieve all documents representing users from a specific city, an index on the "city" field would significantly speed up that query. Coupled with a flexible querying mechanism (N1QL), similar to SQL but designed for JSON data, Couchbase gives developers versatile tools to manage and interact with their document storage effectively.