documentoriented

Document-oriented databases, also called document stores, are a class of NoSQL databases designed to store, retrieve, and manage document-like data. Data is stored as documents, typically encoded in JSON, BSON, or XML, and grouped into collections. Each document contains a set of key-value pairs and can include nested arrays and objects. Unlike relational models, document schemas are flexible: fields can vary between documents in the same collection, and documents can evolve without requiring a formal schema.

Documents are identified by unique keys; applications query by field values, ranges, text search, and can use indexes to accelerate queries. Some databases offer rich query languages and support for updates, aggregation, and indexing. Relationships are usually represented by embedding related data within a document or by storing references to other documents. This denormalized approach can improve read performance but may introduce data duplication.

Document stores typically support horizontal scaling through sharding and replication; consistency models range from eventual to strong, and some platforms provide ACID transactions across multiple documents or collections. They are well suited for content management, catalog and product data, user profiles, session data, and other semi-structured or evolving data workloads where a flexible schema is advantageous.

Common examples include MongoDB, CouchDB, RavenDB, Amazon DocumentDB, and Azure Cosmos DB (in document API mode). The choice to use a document-oriented approach depends on workload characteristics, data access patterns, and the need for flexible schemas versus the complexity of handling relations and joins.