What is Document Store

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.

What does it do/how does it work?

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.

Why is it important? Where is it used?

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.

FAQ

How are document stores different from other NoSQL databases?

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.

When should you use a document store?

Document stores work well for document-oriented, schema-less data, and are ideal for:

What are key challenges with document stores?

However, document stores also come with tradeoffs around scale, querying, and operations:

What are some examples of popular document store technologies?

References