Add comprehensive database testing suite with CI/CD integration

[brockp949]

Summary

This PR introduces a comprehensive database testing framework for the NerdLearn platform, including automated CI/CD workflows, test fixtures, and a command-line test runner. The implementation ensures database schema integrity, data consistency, CRUD operations, relationships, performance, and concurrent access patterns are thoroughly validated.

Key Changes

GitHub Actions Workflow (database-tests.yml)

• Schema Integrity Tests: Validates database schema structure and constraints
• Data Integrity Tests: Ensures data consistency and constraint enforcement
• CRUD Operations Tests: Verifies Create, Read, Update, Delete operations
• Relationship & Cascade Tests: Tests foreign key relationships and cascade behaviors
• Performance Benchmarks: Measures query performance and identifies regressions (optional)
• Concurrency Tests: Validates behavior under concurrent access (optional)
• Summary Report Generation: Aggregates all test results with pass/fail metrics
• Failure Notifications: Automatically creates GitHub issues when scheduled tests fail

Test Infrastructure

• conftest.py: Comprehensive pytest fixtures including:

  • Async and sync database session management
  • Test data factories (User, Concept, Course, SpacedRepetitionCard)
  • Mock fixtures for Redis and Neo4j
  • Query performance timing utilities
  • Test report collection and summarization

• pytest.ini: Configuration for:

  • Async test support
  • Custom markers (unit, integration, benchmark, slow, concurrency)
  • Logging and timeout settings
  • Coverage reporting setup

Command-Line Test Runner (run_db_tests.sh)

• Flexible test execution with options for:
  • Docker PostgreSQL container management (-d/--docker)
  • Category-specific test runs (-c/--category)
  • Coverage reporting (--coverage)
  • Benchmark inclusion (--benchmarks)
  • Verbose output (-v/--verbose)
• Automatic report generation (HTML and JSON)
• Clean container cleanup

Implementation Details

Workflow Triggers

• Push: Runs on main/develop branches when database-related files change
• Pull Requests: Validates changes before merge
• Schedule: Daily 2 AM UTC run for regression detection
• Manual: Workflow dispatch with optional benchmark/stress test flags

Test Organization

Tests are organized by category with clear dependencies:

1. Schema integrity (foundational)
2. Data integrity & CRUD (depend on schema)
3. Relationships (depend on CRUD)
4. Performance & Concurrency (optional, depend on relationships)

Reporting

• Individual HTML and JSON reports per test category
• Combined summary report with pass rates and metrics
• GitHub Step Summary integration for PR visibility
• 30-90 day artifact retention based on test type

Test Data Management

Factory classes provide consistent, reusable test data with:

• Auto-incrementing counters for unique values
• Sensible defaults matching domain requirements
• Easy customization via kwargs
• Automatic reset between tests

Benefits

• Regression Detection: Scheduled runs catch issues early
• CI/CD Integration: Blocks merges with failing database tests
• Performance Tracking: Benchmarks identify performance regressions
• Comprehensive Coverage: Tests schema, data, operations, relationships, and concurrency
• Developer Experience: Local test runner with Docker support for easy development
• Visibility: Automated issue creation and PR comments for test results

https://claude.ai/code/session_01PxbeeXrCHt2y3wZD6oHXcF