Document databases, while popular for their flexibility and ease of use, do come with several limitations that developers should consider. First, one significant limitation is the lack of strong consistency guarantees. Unlike traditional relational databases that enforce strict ACID (Atomicity, Consistency, Isolation, Durability) properties, many document databases operate on eventual consistency. This means that data changes are propagated asynchronously, which can lead to temporary inconsistencies. For example, if multiple users are updating the same document, there is a risk that some updates may not be immediately visible to others, potentially causing confusion or data conflicts.
Another limitation relates to querying capabilities. Document databases often rely on key-value pairs and may not support complex queries as efficiently as relational databases. While they allow for powerful querying on document attributes, joining data across multiple documents can be cumbersome and less efficient. For instance, if a developer needs to join user data stored in one document with order data stored in another, this can require additional coding and potentially impact performance. In contrast, SQL databases are designed specifically for complex relationships, making them a better fit for applications with intricate querying needs.
Lastly, document databases may not offer robust support for transactions affecting multiple documents. While some document databases provide transaction support, often, it is limited to documents within a single collection. This can be a hindrance for applications that require multi-document transactions, such as banking applications where updating user accounts and transaction records must be done atomically. This limitation can lead to increased complexity in application logic and potential data integrity issues. Therefore, developers must carefully evaluate whether document databases align with their project requirements and data modeling needs.