How do you implement versioning in a document database?

Implementing versioning in a document database involves tracking changes to documents over time, ensuring historical data is preserved and accessible. The most common approach is to store each version as a separate document with metadata like version numbers, timestamps, or status flags. For example, in a database like MongoDB, you might add a version field to each document and increment it on updates. When a document is modified, instead of overwriting it, you insert a new document with an updated version number and a timestamp. To efficiently retrieve the latest version, you could use a secondary index on the version field or maintain a separate collection that points to the current version of each document.

Another strategy is to embed version history within the document itself. This involves storing previous versions as nested arrays or sub-documents. For instance, a document might include a versions array containing historical states, each with its own metadata. This simplifies querying all versions of a document in a single read operation. However, this approach can lead to larger document sizes, which may impact performance if versions accumulate rapidly. To mitigate this, some systems limit the number of stored versions or offload older entries to archival storage. For example, a user profile document might retain only the last five versions in the main document and archive older ones to a low-cost storage system.

Considerations like performance, data retention policies, and access patterns heavily influence the choice of method. If low-latency reads for the latest version are critical, maintaining separate collections for current and historical data can improve performance. Alternatively, databases like CouchDB offer built-in versioning through document revisions, which automatically handle conflict resolution. Security is another factor—ensuring older versions are subject to the same access controls as current data. Testing with realistic workloads is essential to evaluate trade-offs, such as storage costs for retaining versions versus the overhead of querying historical data. For example, an e-commerce system might version product listings to track price changes, requiring a balance between quick access to current prices and efficient storage of historical data for audits.