How do document databases handle distributed systems?

Document databases handle distributed systems through three main mechanisms: sharding, replication, and consistency models. Sharding splits data into partitions (shards) distributed across multiple nodes, allowing horizontal scaling. Each shard holds a subset of the database, determined by a shard key (e.g., a document field like user_id). Replication creates copies of shards on different nodes to ensure fault tolerance and high availability. Consistency models define how data updates propagate across nodes, balancing availability and accuracy. For example, some systems prioritize immediate consistency, ensuring all nodes reflect the latest write, while others allow eventual consistency for faster performance.

A concrete example is MongoDB, which uses sharding to distribute collections across clusters. Developers choose a shard key, and MongoDB automatically routes queries to the correct shard. Replica sets in MongoDB maintain multiple copies of each shard, with one primary node handling writes and secondary nodes replicating data. If the primary fails, a secondary takes over. Couchbase employs a similar approach with automatic sharding (called “vBuckets”) and cross-data-center replication. Both databases allow developers to tune consistency: MongoDB offers adjustable write concerns (e.g., requiring acknowledgment from a majority of nodes), while Couchbase uses “Read Your Own Writes” semantics to ensure clients see their own updates immediately.

Trade-offs exist. Sharding requires careful key selection to avoid uneven data distribution (e.g., a poorly chosen key might overload a single node). Replication introduces latency if synchronous consistency is enforced, but relaxed consistency risks stale reads. Document databases often provide tools to mitigate these issues. For example, MongoDB’s balancer redistributes shards when nodes are added or removed, and Couchbase’s N1QL query engine optimizes distributed queries. Developers must balance scalability needs with consistency requirements, choosing configurations that align with their application’s priorities (e.g., favoring availability for global apps vs. strong consistency for financial systems).