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Architecture Overview

ageSchemaClient is designed with modularity, type safety, and performance in mind. This document explains the key architectural decisions and design patterns used throughout the library.

Core Principles​

1. Type Safety First​

  • Full TypeScript support with comprehensive type definitions
  • Compile-time validation of queries and schemas
  • IntelliSense support for better developer experience

2. Apache AGE Integration​

  • Direct integration with Apache AGE's unique requirements
  • Handles AGE-specific parameter limitations
  • Optimized for AGE's Cypher implementation

3. Modular Design​

  • Loosely coupled components
  • Plugin-based extension system
  • Clear separation of concerns

4. Performance Optimization​

  • Connection pooling with proper resource management
  • Batch operations for efficient data loading
  • Query optimization and caching

System Architecture​

┌─────────────────────────────────────────────────────────────┐
│                    Application Layer                        │
├─────────────────────────────────────────────────────────────┤
│  AgeSchemaClient  │  QueryBuilder  │  BatchLoader  │ Schema │
├─────────────────────────────────────────────────────────────┤
│                     Core Layer                              │
├─────────────────────────────────────────────────────────────┤
│   Connection Pool  │  Transaction  │  Extensions  │  Utils  │
├─────────────────────────────────────────────────────────────┤
│                   Database Layer                            │
├─────────────────────────────────────────────────────────────┤
│              PostgreSQL + Apache AGE                        │
└─────────────────────────────────────────────────────────────┘

Key Components​

AgeSchemaClient​

The main entry point that orchestrates all other components:

  • Manages database connections
  • Provides factory methods for builders
  • Handles configuration and lifecycle

QueryBuilder​

Fluent API for constructing type-safe Cypher queries:

  • Method chaining for readable query construction
  • Parameter management with AGE-specific handling
  • Query optimization and validation

BatchLoader​

Efficient data loading for large datasets:

  • Optimized batch processing
  • Progress reporting and error handling
  • Memory-efficient streaming operations

SchemaManager​

Schema definition and validation:

  • JSON Schema-based validation
  • Migration support
  • Comprehensive error reporting

Connection Pool​

Manages database connections efficiently:

  • Connection reuse and pooling
  • Resource cleanup and error recovery
  • Health monitoring and reconnection

Design Patterns​

1. Builder Pattern​

Used extensively for query construction:

const query = client.query()
  .match('(p:Person)')
  .where({ age: { $gte: 18 } })
  .return('p.name')
  .orderBy('p.name')
  .limit(10);

2. Factory Pattern​

For creating specialized instances:

// Client acts as factory for builders
const queryBuilder = client.query();
const batchLoader = client.batch();
const schemaManager = client.schema();

3. Strategy Pattern​

For different execution strategies:

// Different strategies for batch loading
const loader = client.batch()
  .strategy('memory-optimized') // or 'speed-optimized'
  .batchSize(1000);

4. Observer Pattern​

For event handling and progress reporting:

loader.on('progress', (progress) => {
  console.log(`Loaded ${progress.completed}/${progress.total}`);
});

Data Flow​

Query Execution Flow​

  1. Query Construction - Builder pattern creates query object
  2. Parameter Processing - AGE-specific parameter handling
  3. Query Optimization - Query analysis and optimization
  4. Execution - Connection pool provides database connection
  5. Result Processing - Type-safe result transformation
  6. Resource Cleanup - Connection returned to pool

Batch Loading Flow​

  1. Data Validation - Schema validation if enabled
  2. Batch Preparation - Data chunking and optimization
  3. Parallel Processing - Concurrent batch execution
  4. Progress Tracking - Real-time progress reporting
  5. Error Handling - Graceful error recovery
  6. Completion - Final validation and cleanup

Extension System​

Plugin Architecture​

interface Extension {
  name: string;
  version: string;
  initialize(client: AgeSchemaClient): void;
  destroy(): void;
}

// Register extension
client.use(new MyCustomExtension());

Built-in Extensions​

  • Logging Extension - Comprehensive query and performance logging
  • Metrics Extension - Performance metrics and monitoring
  • Cache Extension - Query result caching
  • Validation Extension - Enhanced schema validation

Error Handling Strategy​

Error Hierarchy​

AgeError (base)
├── ConnectionError
├── QueryError
│   ├── SyntaxError
│   ├── ValidationError
│   └── ExecutionError
├── SchemaError
│   ├── ValidationError
│   └── MigrationError
└── BatchError
    ├── DataError
    └── ProcessingError

Error Recovery​

  • Automatic Retry - Configurable retry logic for transient errors
  • Circuit Breaker - Prevents cascade failures
  • Graceful Degradation - Fallback strategies for non-critical operations

Performance Considerations​

Connection Management​

  • Connection pooling with configurable limits
  • Connection health monitoring
  • Automatic reconnection on failure

Query Optimization​

  • Parameter caching and reuse
  • Query plan analysis
  • Batch query execution

Memory Management​

  • Streaming for large result sets
  • Configurable memory limits
  • Garbage collection optimization

Security Features​

Input Validation​

  • SQL injection prevention
  • Parameter sanitization
  • Schema-based validation

Connection Security​

  • SSL/TLS support
  • Connection encryption
  • Credential management

Testing Strategy​

Unit Testing​

  • Comprehensive test coverage
  • Mock-based testing for isolation
  • Property-based testing for edge cases

Integration Testing​

  • Real database testing
  • Performance benchmarking
  • Compatibility testing across AGE versions

End-to-End Testing​

  • Full workflow testing
  • Error scenario validation
  • Performance regression testing

Next Steps​