A document store is a NoSQL database optimized for storing, retrieving, and managing document-style data. Document stores allow developers to store and query unstructured or semi-structured document-oriented information, unlike traditional table-based relational databases.
Examples of popular document store databases include MongoDB, Amazon DocumentDB, Couchbase, and Elasticsearch. Document stores are commonly used with other NoSQL databases like vector databases and graph databases.
A document store ingests free-form document data like JSON, XML, text. Documents are retrieved using document identifiers or keys. It provides APIs or a query language to insert, update, delete and search documents by contents.
Document stores typically retain nested document structure with no imposed schema, and allow indexing parts of documents for efficient content-based search via keywords or other metadata.
Storing schemaless documents allows easy development and iteration for applications dealing with unstructured or ever-changing data. Document stores provide more flexibility than relational databases for such use cases.
Applications include content management, blogging platforms, e-commerce catalogs, user profiles, web applications. Document stores are commonly used across domains dealing with irregular or rapidly evolving data.
Unlike other NoSQL databases, document stores are optimized for storing and querying document-style data like JSON, XML, rather than simple key-values, tables or graphs.
Document stores work well for document-oriented, schema-less data, and are ideal for:
However, document stores also come with tradeoffs around scale, querying, and operations:
A vector database is designed to efficiently store and query vector representations of data for applications like search, recommendations, and AI.Read more ->
A graph database stores data in a graph structure with nodes, edges and properties to represent and query relationships between connected data entities.Read more ->
A search engine database is designed to store, index, and query full text content to enable fast text search and retrieval.Read more ->
Our CEO Ozan recently joined an episode of the Streaming Caffeine podcast — Streaming Caffeine E10: Ozan from Synnada, about Arrow Datafusion, Rust, Databases, SQL, AI — to discuss our perspective on DataFusion and the future of data infrastructure.