Document Store

Data Storage and Sources
Updated on:
July 11, 2024

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.


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.

  • Document orientation - nested structures, variable/evolving schemas.
  • Indexing and querying based on document contents and metadata.
  • Specialized indexing for text search across documents.
  • Tradeoffs on scale, joins, transactions vs relational databases.

When should you use a document store?

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

  • Applications dealing with JSON, XML, text documents.
  • Rapid prototyping and agile development.
  • Content management and blogging platforms.
  • User profiles, catalogs, directories.
  • IoT and time series data.

What are key challenges with document stores?

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

  • No standard query language unlike SQL for relational data.
  • Joining data across documents can be challenging.
  • Replicating and sharing documents across clusters.
  • Scaling to large datasets efficiently.
  • Managing indexes for performance at scale.

What are some examples of popular document store technologies?


Related Entries

Vector Database

A vector database is designed to efficiently store and query vector representations of data for applications like search, recommendations, and AI.

Graph Database

A graph database stores data in a graph structure with nodes, edges and properties to represent and query relationships between connected data entities.

Search Engine (Database)

A search engine database is designed to store, index, and query full text content to enable fast text search and retrieval.


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