Comprehensive List of Patterns Covered
This document provides an overview of various design and microservice patterns, categorized to address challenges in system design, distributed systems, and microservices. Each pattern includes steps to implement, real-world use cases, Spring Boot integrations, advantages, and disadvantages.
Design Patterns
1. Creational Patterns
- Singleton: Ensures a class has only one instance and provides global access.
- Factory Method: Defines an interface for creating objects but lets subclasses alter the object type.
- Abstract Factory: Provides an interface to create families of related objects without specifying concrete classes.
- Builder: Separates object construction from its representation.
- Prototype: Creates objects by cloning an existing instance.
- Object Pool: Reuses objects that are expensive to create.
- Multiton: Similar to Singleton but allows multiple instances.
- Static Factory Method: A static method encapsulates object creation logic.
2. Structural Patterns
- Adapter: Acts as a bridge between incompatible interfaces.
- Bridge: Decouples abstraction from implementation.
- Composite: Combines objects into tree structures to represent hierarchies.
- Decorator: Adds responsibilities to an object dynamically.
- Facade: Provides a simplified interface to a complex subsystem.
- Flyweight: Reduces memory usage by sharing common object data.
- Proxy: Provides a surrogate to control access to an object.
3. Behavioral Patterns
- Chain of Responsibility: Passes requests along a chain of handlers.
- Command: Encapsulates a request as an object.
- Interpreter: Defines a grammar and an interpreter for processing it.
- Iterator: Provides a way to access elements sequentially.
- Mediator: Reduces dependencies by centralizing communication.
- Memento: Captures an object’s state for undo functionality.
- Observer: Sets up a one-to-many dependency for automatic updates.
- State: Alters object behavior when its state changes.
- Strategy: Encapsulates interchangeable algorithms.
- Template Method: Defines the skeleton of an algorithm.
- Visitor: Encapsulates operations on elements.
Microservice Patterns
1. Integration Patterns
- API Gateway: Central entry point for routing requests.
- Aggregator: Combines results from multiple services.
- Chained Microservices: Sequential calls between services.
- Proxy: Mediates access to backend services.
- Event-Driven Architecture: Uses publish/subscribe for asynchronous communication.
- Service Mesh: Handles inter-service communication, security, and observability.
- Fan-Out: Sends requests to multiple services and aggregates responses.
2. Decomposition Patterns
- Service Decomposition: Divides a large system into smaller, self-contained services.
- Layered Decomposition: Organizes the system into layers, each responsible for a specific concern.
- Data Decomposition: Partitions data based on access patterns or entities.
- Functional Decomposition: Breaks a problem into smaller functions, each responsible for a specific task.
- Workflow Decomposition: Breaks workflows into smaller, reusable steps.
- Domain Decomposition: Divides the system based on the business domain or problem space.
- Event-Driven Decomposition: Breaks the system into components that react to specific events.
3. Database Patterns
- Database per Service: Each service manages its own database.
- Shared Database: Multiple services share a single database.
- Saga: Manages distributed transactions using compensating actions.
- CQRS: Separates read and write models.
- Event Sourcing: Stores changes as events in an immutable log.
- Command Sourcing: Logs all commands for reproducibility.
- Index Table: Maintains precomputed indexes to speed up queries.
4. Resiliency Patterns
- Circuit Breaker: Stops calls to failing services to prevent cascading failures.
- Retry: Automatically retries failed operations.
- Timeout: Sets a limit for request completion.
- Bulkhead: Isolates failures by partitioning resources.
- Fail-Fast: Quickly detects and fails on errors.
- Backpressure: Controls request flow to prevent overload.
- Throttling: Limits the number of requests handled.
5. Security Patterns
- Authentication and Authorization: Verifies user identity and permissions.
- Data Encryption: Protects sensitive data by converting it into a secure format.
- Input Validation: Prevents malicious input from causing harm.
- Secure Session Management: Maintains secure user sessions.
- Audit Logging: Tracks user activities and system events.
- Tokenization: Replaces sensitive data with tokens.
- Security Headers: Enhances security by configuring HTTP headers.
6. Observability Patterns
- Centralized Logging: Aggregates logs from all services into a single location.
- Distributed Tracing: Tracks requests across microservices.
- Metrics Collection: Monitors system performance with metrics.
- Health Checks: Periodically checks service health to ensure availability.
7. Communication Patterns
- Request-Response: Synchronous communication between services.
- Event-Based Communication: Asynchronous communication using events.
- Message Queues: Decouples services using queues for communication.
8. Deployment Patterns
- Blue-Green Deployment: Reduces downtime during deployments.
- Canary Deployment: Gradually releases changes to a subset of users.
- Rolling Deployment: Incrementally updates services while keeping them available.
- Immutable Infrastructure: Deploys immutable instances instead of updating existing ones.
9. Scalability Patterns
- Horizontal Scaling: Adds more instances to handle increased load.
- Vertical Scaling: Upgrades existing resources to improve capacity.
- Auto-Scaling: Dynamically adjusts resources based on demand.
- Sharding: Distributes data across multiple databases to improve performance.
This document now provides a comprehensive summary of all design and microservice patterns, categorized and structured for clarity.