Document databases, while useful for flexible data models, have several key limitations. First, they often lack strong transactional guarantees compared to relational databases. While some document databases like MongoDB now support multi-document ACID transactions, these can be slower or more complex to implement than the single-record atomic operations they’re optimized for. For example, updating multiple related documents atomically might require explicit transaction handling, which adds overhead. This makes them less suited for use cases requiring strict consistency, such as financial systems where balance updates and transaction logs must stay perfectly synchronized.
A second limitation is the difficulty in managing complex relationships between data. Document databases store data in nested structures (e.g., JSON), which works well for hierarchical data but struggles with many-to-many relationships. For instance, in a social media app, if user profiles and posts are stored as separate documents, querying “all posts liked by a user’s friends” would require multiple lookups or denormalization, increasing complexity. Unlike relational databases, there’s no built-in JOIN operation, so developers must handle relationships in application code or duplicate data, risking inconsistency. Over time, this can lead to bloated documents or fragmented data.
Finally, schema flexibility can become a double-edged sword. While schema-less design allows rapid iteration, it shifts the burden of data validation to the application layer. For example, if a field like “email” is added to user documents, there’s no database-level enforcement to ensure all documents include it or follow a valid format. This can lead to inconsistent data quality, especially in large teams or long-lived projects. Additionally, query performance may suffer if indexes aren’t carefully designed for evolving access patterns—a query filtering on a new field might require a full collection scan until an index is added. These factors make document databases less ideal for scenarios requiring strict data governance or predictable query performance.