How do document databases handle multi-tenancy?

Document databases handle multi-tenancy by using strategies that isolate tenant data while balancing resource efficiency and scalability. The most common approaches include using separate databases per tenant, partitioning data within a shared database using tenant identifiers, or employing a hybrid model. These methods ensure data separation, simplify access control, and maintain performance across tenants. Document databases are well-suited for multi-tenancy due to their flexible schema, which allows tenant-specific data structures to coexist without rigid table definitions.

One approach is assigning each tenant a dedicated database or collection. For example, MongoDB supports creating separate databases for each tenant, ensuring complete isolation. This method provides strong security and avoids accidental data leaks but can become resource-intensive as tenant numbers grow. A middle-ground strategy uses a shared database with tenant-specific collections or partitions. In Azure Cosmos DB, developers can use a tenant ID as the partition key, grouping all documents for a tenant within a logical container. This balances isolation with efficient resource usage. Queries filter by the tenant ID, ensuring operations only access authorized data. A third option stores all tenant data in a single collection, with a mandatory tenant ID field in every document. This maximizes storage efficiency but requires strict enforcement of tenant-based query filters to prevent data exposure.

Developers must consider trade-offs between isolation, performance, and complexity. Indexing the tenant ID field is critical for query efficiency in shared collections. Access control mechanisms like role-based permissions should bind tenant IDs to user sessions, ensuring tenants only access their data. Some document databases, like Couchbase, offer row-level security features to automate this filtering. Operational considerations include backup strategies (per-tenant or global) and scalability patterns, such as sharding based on tenant ID. While document databases provide tools for multi-tenancy, consistent implementation of tenant isolation in application code—such as middleware that injects tenant IDs into queries—remains essential to prevent errors.