Skip to content
OstrichDB

Architecture Overview

Architecture Overview

Section titled “Architecture Overview”

OstrichDB is designed as a hierarchical NoSQL database with a focus on security, performance, and ease of use. This document provides an overview of the system’s architecture and key design decisions.

Design Principles

Section titled “Design Principles”

Hierarchical Organization

Section titled “Hierarchical Organization”

OstrichDB organizes data in a natural hierarchy that mirrors how humans think about data organization:

Projects (Top-level containers)
  └── Collections (Groups of related data)
      └── Clusters (Logical groupings)
          └── Records (Individual data items)

This structure provides:

  • Intuitive data organization: Natural way to group related data
  • Efficient querying: Hierarchical queries are fast and predictable
  • Scalable storage: Data can be distributed across the hierarchy
  • User isolation: Projects provide natural tenant boundaries

Memory Management

Section titled “Memory Management”

Written in Odin, OstrichDB uses manual memory management with defer cleanup patterns, providing:

  • Predictable performance: No garbage collection pauses
  • Low memory overhead: Precise control over memory allocation
  • Resource safety: Automatic cleanup through defer statements

Security by Design

Section titled “Security by Design”

Security is integrated into every layer:

  • Encryption at rest: Collections can be encrypted with user-specific keys
  • Access control: Multi-tenant isolation with user-based project ownership
  • Secure operations: Automatic encrypt/decrypt cycles for all data operations
  • JWT authentication: Token-based API authentication

System Components

Section titled “System Components”

The OstrichDB system consists of several key components working together:

Database Engine

Section titled “Database Engine”

The core engine handles all data operations and provides the foundation for the hierarchical data model.

HTTP Server

Section titled “HTTP Server”

RESTful API server that provides external access to database functionality with comprehensive endpoint coverage.

Security Layer

Section titled “Security Layer”

Handles authentication, authorization, encryption, and key management across all operations.

Configuration System

Section titled “Configuration System”

Dynamic configuration management supporting different environments and runtime adjustments.

Data Flow

Section titled “Data Flow”

Write Operations

Section titled “Write Operations”
  1. Authentication: JWT token validation
  2. Authorization: User access verification for the target project
  3. Validation: Data type and structure validation
  4. Encryption: Automatic encryption if collection is secured
  5. Storage: Hierarchical storage in the appropriate location
  6. Response: Confirmation with metadata

Read Operations

Section titled “Read Operations”
  1. Authentication: JWT token validation
  2. Authorization: User access verification
  3. Query Processing: Filter and search parameter processing
  4. Data Retrieval: Hierarchical data access
  5. Decryption: Automatic decryption if collection is secured
  6. Response: Formatted data with metadata

Performance Characteristics

Section titled “Performance Characteristics”

Strengths

Section titled “Strengths”
  • Fast hierarchical queries: Optimized for tree-like data access patterns
  • Predictable performance: Manual memory management eliminates GC pauses
  • Efficient filtering: Built-in support for complex queries
  • Low latency: Minimal overhead in the request/response cycle

Considerations

Section titled “Considerations”
  • Memory usage: Manual memory management requires careful resource handling
  • Concurrent access: Designed for high-concurrency read operations
  • Storage efficiency: Hierarchical structure optimizes for typical access patterns

Scalability Model

Section titled “Scalability Model”

Horizontal Scaling

Section titled “Horizontal Scaling”
  • Project-level distribution: Projects can be distributed across multiple instances
  • Collection-level sharding: Collections can be partitioned for larger datasets
  • Read replicas: Support for read-only replicas for query scaling

Vertical Scaling

Section titled “Vertical Scaling”
  • Memory efficiency: Optimized memory usage patterns
  • CPU utilization: Efficient algorithms for common operations
  • Storage optimization: Hierarchical storage reduces disk I/O

Integration Points

Section titled “Integration Points”

REST API

Section titled “REST API”
  • Complete CRUD operations for all hierarchy levels
  • Advanced filtering and search capabilities
  • Standardized HTTP status codes and error handling
  • JSON request/response format

Authentication

Section titled “Authentication”
  • JWT token-based authentication
  • User isolation and multi-tenancy
  • Configurable token expiration and refresh

Configuration

Section titled “Configuration”
  • Environment-specific configuration files
  • Runtime configuration updates
  • Monitoring and logging configuration

Error Handling

Section titled “Error Handling”

OstrichDB implements comprehensive error handling:

Custom Error System

Section titled “Custom Error System”
  • Location tracking: Errors include source location information
  • Error categorization: Different error types for different scenarios
  • Graceful degradation: System continues operating when possible

Logging and Monitoring

Section titled “Logging and Monitoring”
  • Request logging: Comprehensive audit trails
  • Error logging: Detailed error information for debugging
  • Performance monitoring: Built-in metrics and health checks

Next Steps

Section titled “Next Steps”

To dive deeper into specific aspects of the architecture: