Design Patterns MCP Server

{ "patterns": [ { "id": "dao-pattern", "name": "Data Access Object (DAO)", "category": "Data Access", "description": "Abstract interface to database or persistence mechanism", "when_to_use": ["Database abstraction", "Legacy systems", "Multiple data sources"], "benefits": ["Database independence", "Centralized data access", "Code reusability"], "drawbacks": ["Additional layer", "Complexity", "Performance overhead"], "use_cases": [ "Enterprise applications", "Legacy database integration", "Multi-database systems" ], "complexity": "Medium", "tags": ["data-access", "abstraction", "database"] }, { "id": "orm-pattern", "name": "Object-Relational Mapping (ORM)", "category": "Data Access", "description": "Technique for converting data between incompatible type systems in OOP languages and relational databases", "when_to_use": [ "Object-oriented applications", "Complex domain models", "Database abstraction" ], "benefits": ["Object-oriented data access", "Database independence", "Automatic mapping"], "drawbacks": ["Performance overhead", "Complex queries", "Learning curve"], "use_cases": ["Enterprise applications", "Domain-driven design", "Rapid development"], "complexity": "High", "tags": ["data-access", "mapping", "orm"] }, { "id": "connection-pool", "name": "Connection Pool", "category": "Data Access", "description": "Maintains cache of database connections to improve performance and resource utilization", "when_to_use": [ "High-traffic applications", "Performance optimization", "Resource management" ], "benefits": ["Performance improvement", "Resource efficiency", "Connection reuse"], "drawbacks": ["Configuration complexity", "Memory overhead", "Connection leaks"], "use_cases": ["Web applications", "Enterprise systems", "High-concurrency applications"], "complexity": "Medium", "tags": ["data-access", "performance", "pooling"] }, { "id": "database-transaction", "name": "Database Transaction Pattern", "category": "Data Access", "description": "Ensures data consistency through ACID properties in database operations", "when_to_use": ["Data integrity", "Multi-step operations", "Consistency requirements"], "benefits": ["Data consistency", "Error recovery", "Atomicity", "Isolation"], "drawbacks": ["Performance overhead", "Deadlock risk", "Complex error handling"], "use_cases": ["Financial systems", "E-commerce", "Multi-table operations"], "complexity": "Medium", "tags": ["data-access", "transaction", "consistency"] }, { "id": "read-through-cache", "name": "Read-Through Cache", "category": "Data Access", "description": "Cache that automatically loads data from data source on cache miss", "when_to_use": ["Frequent data access", "Performance optimization", "Reduced database load"], "benefits": ["Automatic loading", "Simplified client code", "Performance improvement"], "drawbacks": ["Cache complexity", "Potential inconsistency", "Memory usage"], "use_cases": [ "High-read applications", "Database performance optimization", "Content delivery" ], "complexity": "Medium", "tags": ["data-access", "caching", "performance"] }, { "id": "write-through-cache", "name": "Write-Through Cache", "category": "Data Access", "description": "Cache that writes data to storage immediately when cache is updated", "when_to_use": ["Data consistency", "Write-heavy applications", "Durability requirements"], "benefits": ["Data consistency", "Durability", "Simplified recovery"], "drawbacks": ["Performance overhead", "Write latency", "Complex implementation"], "use_cases": ["Critical data systems", "Financial applications", "Audit trails"], "complexity": "High", "tags": ["data-access", "caching", "consistency"] }, { "id": "write-behind-cache", "name": "Write-Behind Cache", "category": "Data Access", "description": "Cache that delays writing to storage to improve write performance", "when_to_use": ["High write volume", "Performance optimization", "Batch processing"], "benefits": ["Write performance", "Reduced database load", "Batch operations"], "drawbacks": ["Data loss risk", "Complex recovery", "Eventual consistency"], "use_cases": ["High-throughput systems", "Logging systems", "Analytics"], "complexity": "High", "tags": ["data-access", "caching", "performance"] }, { "id": "optimistic-locking", "name": "Optimistic Locking", "category": "Data Access", "description": "Assumes conflicts are rare and checks for conflicts at commit time", "when_to_use": ["Low conflict scenarios", "Performance requirements", "Scalable systems"], "benefits": ["Better performance", "No deadlocks", "High concurrency"], "drawbacks": ["Conflict handling", "Retry logic", "Version management"], "use_cases": ["Web applications", "Distributed systems", "High-concurrency scenarios"], "complexity": "Medium", "tags": ["data-access", "concurrency", "locking"] }, { "id": "pessimistic-locking", "name": "Pessimistic Locking", "category": "Data Access", "description": "Locks resources immediately when accessed to prevent conflicts", "when_to_use": ["High conflict scenarios", "Critical data", "Strong consistency"], "benefits": ["Prevents conflicts", "Data consistency", "Simple conflict resolution"], "drawbacks": ["Reduced performance", "Deadlock risk", "Limited scalability"], "use_cases": ["Financial systems", "Inventory management", "Critical updates"], "complexity": "Medium", "tags": ["data-access", "concurrency", "locking"] }, { "id": "database-sharding", "name": "Database Sharding", "category": "Data Access", "description": "Horizontal partitioning of data across multiple databases", "when_to_use": ["Large datasets", "Scalability requirements", "Performance optimization"], "benefits": ["Horizontal scaling", "Performance improvement", "Load distribution"], "drawbacks": ["Complex queries", "Data consistency", "Operational complexity"], "use_cases": ["Large-scale applications", "Big data systems", "Global applications"], "complexity": "High", "tags": ["data-access", "scaling", "partitioning"] }, { "id": "database-replication", "name": "Database Replication", "category": "Data Access", "description": "Maintains copies of data across multiple database instances", "when_to_use": ["High availability", "Read scaling", "Disaster recovery"], "benefits": ["High availability", "Read performance", "Fault tolerance"], "drawbacks": ["Consistency challenges", "Replication lag", "Storage overhead"], "use_cases": [ "High-availability systems", "Read-heavy applications", "Geographic distribution" ], "complexity": "High", "tags": ["data-access", "replication", "availability"] }, { "id": "lazy-loading", "name": "Lazy Loading", "category": "Data Access", "description": "Defers loading of data until it is actually needed", "when_to_use": ["Large object graphs", "Performance optimization", "Memory constraints"], "benefits": ["Performance optimization", "Memory efficiency", "On-demand loading"], "drawbacks": ["N+1 query problem", "Complex implementation", "Delayed loading"], "use_cases": ["ORM frameworks", "Large datasets", "Performance-critical applications"], "complexity": "Medium", "tags": ["data-access", "optimization", "loading"] }, { "id": "eager-loading", "name": "Eager Loading", "category": "Data Access", "description": "Loads related data upfront to avoid multiple database queries", "when_to_use": ["Known data requirements", "Query optimization", "Reducing database calls"], "benefits": ["Fewer database calls", "Predictable performance", "Simple implementation"], "drawbacks": ["Memory overhead", "Potential over-fetching", "Complex queries"], "use_cases": ["Known access patterns", "Performance optimization", "Reporting systems"], "complexity": "Medium", "tags": ["data-access", "optimization", "loading"] } ] }