CouchDB

Last Updated : 2 Jul, 2026

CouchDB is an open-source, document-oriented NoSQL database developed by the Apache Software Foundation. CouchDB stores data as JSON documents containing key-value pairs, lists, and maps.

  • Flexible Document Model: Stores data in schema-less JSON documents.
  • Powerful Querying: Supports data filtering, mapping, and querying.
  • RESTful API: Uses an HTTP-based REST API for easy database communication.
  • Simple HTTP Methods: Supports standard methods like GET, PUT, and DELETE for CRUD operations.

CouchDB Data Model

  • In CouchDB each database is a collection of independent documents which makes the outermost data structure/container of it.
  • Each document maintains its own data and self-contained schema.
  • To merge the differences occurred while the databases were disconnected, Document metadata contains revision information.
  • To avoid the need to lock the database field during writes, it implements multi version concurrency control.

Simple CouchDB Document Structure

{
  "_id": "101",
  "name": "John Doe",
  "age": 25,
  "email": "john@example.com",
  "city": "Mumbai"
}
  • _id: Unique identifier for the document.
  • Fields: Store data as key-value pairs (e.g., name, age, email, city).
  • JSON Format: Documents are stored in JSON format.
  • Flexible Schema: Different documents can have different fields.

Advantages

  • Schema-less Database: Stores JSON documents without requiring a fixed schema.
  • Easy Replication: Supports master-master replication and data synchronization across multiple servers.
  • RESTful API: Uses HTTP/HTTPS, making it easy to integrate with web applications.
  • Offline-First Support: Allows applications to work offline and synchronize data when connectivity is restored.

Limitations

  • No Native Joins: Does not support SQL-style joins between documents.
  • Limited Querying: Complex queries are harder compared to relational databases.
  • Eventual Consistency: Replicated data may not be immediately consistent across all nodes.
  • Not Suitable for Highly Relational Data: Performs poorly for applications with many interconnected relationships
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