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ADR-001: Database Technology Choice

Status​

Accepted

Context​

We need to choose a primary database technology for the GISE methodology platform that can:

  • Support high read/write throughput for documentation and code generation
  • Handle structured data for user management, projects, and templates
  • Provide strong consistency for critical business operations
  • Support complex queries for search and analytics
  • Scale horizontally as the platform grows
  • Integrate well with Node.js/TypeScript backend
  • Support JSON/document storage for flexible content types
  • Provide reliable backup and disaster recovery options

Current Requirements​

  • Expected users: 10,000+ registered users
  • Expected projects: 50,000+ managed projects
  • Data types: User profiles, project metadata, templates, generated code, documentation
  • Query patterns: Full-text search, complex joins, aggregations, real-time updates
  • Consistency needs: Strong consistency for user data, eventual consistency acceptable for analytics

Decision​

We will use PostgreSQL as our primary database technology.

Rationale​

PostgreSQL Advantages​

Technical Capabilities:

  • ACID Compliance: Strong consistency guarantees for critical operations
  • JSON Support: Native JSON and JSONB support for flexible document storage
  • Full-Text Search: Built-in full-text search capabilities with ranking
  • Extensibility: Rich ecosystem of extensions (PostGIS, pg_trgm, etc.)
  • Performance: Excellent query optimization and index support
  • Concurrent Access: MVCC for high concurrency without locking issues

Operational Benefits:

  • Maturity: 30+ years of development with proven stability
  • Community: Large, active open-source community
  • Tooling: Extensive monitoring, backup, and management tools
  • Expertise: Wide availability of PostgreSQL expertise in the market
  • Cloud Support: Excellent managed services (AWS RDS, Google Cloud SQL, etc.)

GISE-Specific Benefits:

  • Template Storage: JSON columns perfect for storing flexible template definitions
  • Code Search: Full-text search ideal for searching generated code and documentation
  • Audit Trail: Built-in transaction logging for compliance requirements
  • Schema Evolution: ALTER TABLE operations support iterative development

Implementation Plan​

Initial Setup:

-- Database configuration optimized for GISE workload
-- postgresql.conf settings
shared_buffers = 256MB              # 25% of available RAM
effective_cache_size = 1GB          # 75% of available RAM  
work_mem = 16MB                     # For sorting and joins
maintenance_work_mem = 256MB        # For maintenance operations
wal_buffers = 16MB                  # Write-ahead logging
checkpoint_completion_target = 0.9   # Smooth checkpoints
random_page_cost = 1.1              # SSD-optimized

Connection Pooling:

// Using pg-pool for connection management
const { Pool } = require('pg');

const pool = new Pool({
  host: process.env.DB_HOST,
  port: process.env.DB_PORT || 5432,
  database: process.env.DB_NAME,
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
  max: 20,                    // Maximum connections
  idleTimeoutMillis: 30000,   # Idle connection timeout
  connectionTimeoutMillis: 2000, # Connection timeout
});

Schema Design:

-- Core entity tables
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    profile JSONB NOT NULL DEFAULT '{}',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE projects (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    owner_id UUID REFERENCES users(id),
    metadata JSONB NOT NULL DEFAULT '{}',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE templates (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    category VARCHAR(100) NOT NULL,
    definition JSONB NOT NULL,
    searchable_content TEXT GENERATED ALWAYS AS 
        (definition->>'title' || ' ' || definition->>'description') STORED,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Full-text search indexes
CREATE INDEX idx_templates_search ON templates 
    USING gin(to_tsvector('english', searchable_content));

-- Performance indexes  
CREATE INDEX idx_projects_owner ON projects(owner_id);
CREATE INDEX idx_templates_category ON templates(category);

Consequences​

Positive Consequences​

Performance Benefits:

  • Query optimizer handles complex joins efficiently
  • JSONB indexing provides fast access to semi-structured data
  • Full-text search eliminates need for external search service initially
  • Connection pooling reduces connection overhead

Development Benefits:

  • Strong typing with TypeScript integration via libraries like Prisma
  • Comprehensive error messages aid debugging
  • Transaction support ensures data consistency
  • Schema migrations support iterative development

Operational Benefits:

  • Automated backup with point-in-time recovery
  • Streaming replication for high availability
  • Extensive monitoring and alerting capabilities
  • Well-understood operational procedures

Negative Consequences​

Operational Complexity:

  • Requires database administration expertise
  • Backup and recovery procedures more complex than managed NoSQL
  • Performance tuning requires PostgreSQL-specific knowledge
  • Vertical scaling limitations compared to distributed databases

Development Constraints:

  • Schema changes require migrations and careful planning
  • Complex queries may require PostgreSQL-specific SQL
  • JSON operations less intuitive than native document databases
  • ORM impedance mismatch for some object-oriented patterns

Scaling Considerations:

  • Horizontal scaling requires partitioning or read replicas
  • Write scaling limited to single master node
  • Large JSON documents can impact performance
  • Full database locks during some maintenance operations

Alternatives Considered​

MongoDB (Document Database)​

Pros:

  • Natural fit for JSON-heavy data structures
  • Built-in horizontal scaling (sharding)
  • Flexible schema evolution
  • Strong Node.js ecosystem

Cons:

  • Eventual consistency by default
  • Limited transaction support across documents
  • Query language less expressive than SQL
  • Operational complexity of sharded clusters

Decision: Rejected due to consistency requirements for user and billing data.

MySQL (Relational Database)​

Pros:

  • Wide adoption and community support
  • Good performance for read-heavy workloads
  • Extensive tooling and expertise available

Cons:

  • Limited JSON support compared to PostgreSQL
  • Less extensible architecture
  • Storage engine complexity (InnoDB vs MyISAM)
  • Less sophisticated query optimizer

Decision: Rejected due to inferior JSON support and extensibility.

Amazon DynamoDB (Managed NoSQL)​

Pros:

  • Fully managed with automatic scaling
  • High performance and availability
  • Strong consistency option available
  • Pay-per-use pricing model

Cons:

  • Vendor lock-in to AWS
  • Limited query capabilities (no joins)
  • Complex pricing model
  • No full-text search without additional services

Decision: Rejected due to vendor lock-in concerns and query limitations.

Redis (In-Memory Database)​

Pros:

  • Extremely high performance
  • Built-in data structures (sets, sorted sets, etc.)
  • Pub/sub messaging capabilities
  • Atomic operations support

Cons:

  • Memory-only storage (persistence options available but complex)
  • Limited query capabilities
  • No relational model support
  • Expensive for large datasets

Decision: Rejected as primary database due to persistence concerns. Will use as cache layer.

Implementation Timeline​

Phase 1: Foundation (Weeks 1-2)​

  • Set up PostgreSQL instance with appropriate configuration
  • Implement connection pooling and basic database utilities
  • Create core schema (users, projects, templates)
  • Implement basic CRUD operations with TypeScript types
  • Set up automated backups and monitoring

Phase 2: Search and Performance (Weeks 3-4)​

  • Implement full-text search for templates and documentation
  • Add performance indexes based on query patterns
  • Set up query performance monitoring
  • Implement connection pool monitoring and alerting
  • Load testing and performance tuning

Phase 3: Advanced Features (Weeks 5-6)​

  • Implement audit logging for compliance
  • Add read replicas for scaling read operations
  • Set up database metrics and dashboards
  • Implement automated schema migration system
  • Documentation and runbooks for operations

Phase 4: Production Hardening (Weeks 7-8)​

  • High availability setup with failover
  • Disaster recovery testing and procedures
  • Security hardening and access controls
  • Performance optimization based on production data
  • Monitoring and alerting fine-tuning

Monitoring and Success Metrics​

Performance Metrics​

  • Query response time (target: <100ms for 95% of queries)
  • Connection pool utilization (target: <80% peak usage)
  • Database CPU utilization (target: <70% average)
  • Cache hit ratio (target: >95%)

Reliability Metrics​

  • Database uptime (target: 99.9%)
  • Backup success rate (target: 100%)
  • Recovery time objective (target: <4 hours)
  • Recovery point objective (target: <1 hour)

Business Metrics​

  • User registration success rate (target: >99%)
  • Project creation success rate (target: >99%)
  • Template search response time (target: <200ms)
  • Data consistency errors (target: 0)

Review and Evolution​

Review Schedule: This decision will be reviewed in 6 months (July 2024) to assess:

  • Performance against target metrics
  • Operational complexity and costs
  • Team satisfaction with development experience
  • Scalability requirements evolution

Evolution Triggers: Consider revisiting this decision if:

  • Query performance degrades below acceptable levels
  • Operational costs exceed budget by >50%
  • Horizontal scaling requirements exceed PostgreSQL capabilities
  • Team expertise shifts significantly toward NoSQL technologies
  • Vendor-specific features become critical business requirements

Success Criteria for Continuation:

  • All performance metrics consistently met
  • Operational procedures well-established and documented
  • Development team productivity maintained or improved
  • Total cost of ownership within budget expectations

Decision Date: December 19, 2024
Participants: Architecture Team, Backend Developers, DevOps Team
Next Review: June 19, 2025