What is DocumentDB?

DocumentDB provides a NoSQL datastore implemented using PostgreSQL, giving developers complete visibility into the architecture and implementation of the engine. It's designed to offer:

• Full compatibility with MongoDB wire protocol through the pg_documentdb_api layer
• Native BSON document support via the pg_documentdb_core PostgreSQL extension
• Advanced indexing capabilities including single field, multi-key, compound, text, and geospatial indexes
• Vector search functionality powered by the pg_vector PostgreSQL extension
• Enterprise-grade security with SCRAM authentication
• Full PostgreSQL compatibility for advanced SQL operations

Key Features

• PostgreSQL Foundation: Built on the powerful PostgreSQL engine, allowing you to leverage both document and relational capabilities
• Open Source: Released under the MIT license with no restrictions on usage, modification, or distribution
• Document Database Standard: First implementation towards creating an open standard for document databases, similar to ANSI SQL for relational databases
• Cloud Ready: Supports multi-cloud deployments across major cloud providers with native integration in Azure Cosmos DB
• Developer Friendly: Rich ecosystem of tools and extensions, including VS Code integration and MongoDB compatibility

Architecture Components

DocumentDB consists of two primary components:

1. pg_documentdb_core: A custom PostgreSQL extension that provides:

   • Native BSON data type support in PostgreSQL
   • Field-level indexing at any nesting depth
   • Multi-key, compound, text, and geospatial indexing
   • Vector search capabilities for AI/ML applications
   • SCRAM authentication mechanism

2. pg_documentdb_api: The data plane implementing:

   • CRUD operations with MongoDB compatibility
   • Advanced query functionality
   • Index management and optimization
   • Protocol translation layer for MongoDB wire protocol

Common Use Cases

• Modern Web Applications: Store and query JSON documents with MongoDB compatibility
• AI/ML Applications: Leverage vector search for similarity matching and embeddings
• Hybrid Data Models: Combine document and relational data in the same database
• Migration Path: Easy transition from existing MongoDB workloads
• Local Development: Full-featured local instance for development and testing

Getting Started Options

Choose the getting started guide that best fits your needs:

Quick Start Guides

• VS Code Extension Quick Start - Recommended for developers new to DocumentDB
• MongoDB Shell Quick Start - Ideal for MongoDB users
• VS Code Extension Guide - Comprehensive guide to the VS Code extension

Migration Guides

• MongoDB to DocumentDB Migration - Migrate from MongoDB to DocumentDB

Language-Specific Guides

• Python Setup Guide - Using DocumentDB with Python applications
• Node.js Setup Guide - Using DocumentDB with Node.js applications

Deployment Options

• Pre-built Packages - Download and install ready-to-use packages
• Multi-cloud with Azure - Deploy on Azure with Cosmos DB integration
• Multi-cloud with GCP - Deploy on Google Cloud Platform
• Multi-cloud with AWS DocumentDB - Deploy alongside AWS services
• Using with YugabyteDB - Combine with distributed SQL capabilities

Community and Support

• Discord Community: Join our Discord server for:
  • Community syncs and office hours
  • Real-time technical support
  • Migration assistance
  • Best practices sharing
• GitHub Repository: documentdb/documentdb
• Documentation: Comprehensive guides and API references
• Issue Tracking: Report bugs and request features on GitHub

Next Steps

After choosing your preferred getting started path:

• Explore our API Reference for detailed documentation
• Learn about the Architecture for deep technical insights
• Join our community to contribute and get support