How does Couchbase handle document storage?

Couchbase stores data as flexible JSON documents, organizing them into logical containers called buckets. Each document is identified by a unique key, which developers assign during insertion. These documents can include nested structures, arrays, and varying fields, making them suitable for semi-structured data. For example, a user profile document might contain fields like user_id, name, email, and a nested address object. Couchbase automatically stores metadata like document expiration (TTL), versioning (CAS value), and flags alongside each document, enabling features like automatic expiration and conflict detection during updates.

Documents are distributed across nodes using a sharding mechanism called vBuckets. When a cluster is created, Couchbase splits each bucket into 1024 vBuckets, which are dynamically assigned to nodes. This allows horizontal scaling: adding nodes triggers automatic redistribution of vBuckets to balance storage and query load. For instance, in a 3-node cluster, vBuckets for a “users” bucket might initially be evenly split. Adding a fourth node redistributes 25% of the vBuckets to the new node without downtime. Data is accessed via a smart client SDK that knows which node hosts a specific document’s vBucket, reducing network hops for read/write operations.

Couchbase optimizes performance by combining memory and disk storage. Documents are first written to an in-memory cache (the write-back cache) and asynchronously persisted to disk. This allows low-latency writes while ensuring durability. For example, an e-commerce application might rapidly log user activity to memory, with Couchbase flushing changes to disk every second. The database also supports cross-data-center replication (XDCR), synchronizing buckets across clusters for disaster recovery. Developers can tune persistence settings—like adjusting the number of disk copies per document—to balance speed and reliability based on their use case.