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System Architectures
Let’s Dive in
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Monolithic Architecture
Monolithic Architecture Single Application User Interface Layer (Web UI, Mobile UI) Business Logic Layer • User Management • Order Processing • Inventory Data Access Layer (Database Connections, ORM) Shared Libraries (Utilities, Common Functions) Database (Single Database) 👤 User 👤 User Characteristics: ✓ Single Deployable Unit ✓ Shared Database ✓ Tightly Coupled ✓ Simple Development ✗ Scaling Challenges ✗ Technology Lock-in Single Deployment WAR/JAR File or Container
Microservices Architecture
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Microservices Architecture API Gateway (Routing, Auth, Rate Limiting) User Service • Authentication • User Profiles • Registration Port: 8001 Order Service • Order Creation • Order Status • Order History Port: 8002 Product Service • Product Catalog • Search • Recommendations Port: 8003 Payment Service • Payment Processing • Billing • Refunds Port: 8004 User DB PostgreSQL Order DB MongoDB Product DB Elasticsearch Payment DB MySQL 👤 👤 Users Message Queue (RabbitMQ/Kafka) Microservices Characteristics: ✓ Independent Deployment ✓ Technology Diversity ✓ Scalable Components ✓ Fault Isolation ✓ Team Independence ✓ Database Per Service ✗ Network Complexity ✗ Data Consistency ✗ Distributed Debugging ✗ Operational Overhead ⚠ Service Discovery Needed ⚠ Monitoring Critical Each service runs in its own container/process and communicates via HTTP/REST APIs or messaging
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Event-Driven Architecture
Event-Driven Architecture Event Bus (Message Broker) Apache Kafka / RabbitMQ / Azure Service Bus User Service (Event Producer) Publishes: UserCreated, UserUpdated Order Service (Event Producer) Publishes: OrderPlaced, OrderCancelled Payment Service (Event Producer) Publishes: PaymentProcessed, PaymentFailed Email Service (Event Consumer) Subscribes: UserCreated, OrderPlaced Inventory Service (Event Consumer) Subscribes: OrderPlaced, OrderCancelled Analytics Service (Event Consumer) Subscribes: All Events for Reporting Event: UserCreated { userId: "123", email: "user@example.com", timestamp: "2025-06-06T10:30:00Z" } Event: OrderPlaced { orderId: "456", userId: "123", amount: 99.99, items: [...], timestamp: "2025-06-06T10:35:00Z" } Event Store (Event Sourcing) Persistent Event Log Order Saga Orchestrates: Payment → Inventory → Shipping CQRS Command & Query Responsibility Segregation Event-Driven Architecture Characteristics: ✓ Loose Coupling ✓ Scalability ✓ Asynchronous Processing ✓ Real-time Reactions ✓ Event Replay ✓ Auditability ✗ Eventual Consistency ✗ Complex Debugging ✗ Event Ordering ✗ Message Delivery ⚠ Schema Evolution ⚠ Duplicate Events 📋 Common Patterns: Event Sourcing, CQRS, Saga Pattern, Event Streaming Components communicate through events published to a central event bus. Producers publish events, consumers subscribe to relevant events. Publish Events Subscribe to Events
CQRS + Event Sourcing
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CQRS + Event Sourcing Architecture Client App Web/Mobile Interface COMMAND SIDE (Write) Commands CreateUser PlaceOrder Command Handlers Business Logic Validation Domain Models User Aggregate Order Aggregate Event Store Source of Truth UserCreated (v1) → UserEmailUpdated (v2) OrderPlaced (v1) → OrderShipped (v2) Immutable Event Log QUERY SIDE (Read) Event Handlers Project Events Update Views Read Models User Views Order Views Query Handlers GetUser GetOrders User DB PostgreSQL Optimized Views Order DB MongoDB Denormalized Analytics Elasticsearch Search/Reports Event Store Examples UserCreated Event { eventId: "e1", aggregateId: "user-123", version: 1, data: { name: "John", email: "..." {, timestamp: "2025-06-06T10:00:00Z" } OrderPlaced Event { eventId: "e2", aggregateId: "order-456", version: 1, data: { userId: "123", total: 99.99 {, timestamp: "2025-06-06T10:05:00Z" } Event Replay Example UserCreated → UserEmailUpdated = Current State: { name: "John", email: "john.new@example.com" } Read Model Projection UserView: { id: "123", name: "John", email: "...", totalOrders: 5, lastOrderDate: "2025-06-05" } (Denormalized for fast queries) CQRS + Event Sourcing Benefits & Challenges ✓ Benefits: • Complete Audit Trail: Every change is recorded as an immutable event • Scalability: Read and write sides can be scaled independently • Flexible Queries: Multiple read models optimized for different use cases • Event Replay: Rebuild state from events, create new projections • Temporal Queries: Query state at any point in time • Business Insights: Events capture business intent, not just final state ✗ Challenges: • Complexity: More complex than traditional CRUD operations • Eventual Consistency: Read models may be temporarily out of sync • Event Versioning: Managing schema changes over time • Storage Requirements: Events accumulate over time • Debugging: More complex to trace issues across event streams • Learning Curve: Requires different thinking about data modeling 🎯 Best Use Cases: • Financial systems requiring audit trails • Complex business workflows • High-read, low-write scenarios • Systems needing temporal queries • Analytics and reporting requirements • Compliance-heavy domains 📋 Related Patterns: Domain-Driven Design (DDD) • Aggregate Pattern • Saga Pattern • Event Streaming • Snapshot Pattern Commands Queries Events
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Domain-Driven Design (DDD)
Domain-Driven Design Architecture User Interface Layer • Web Controllers • REST APIs • GraphQL • CLI • Views/Templates • DTOs • Serializers • Formatters Application Layer • Application Services • Command Handlers • Query Handlers • Event Handlers • Use Cases • Workflows • Orchestration • Validation Domain Layer (Core Business Logic) Entities • User • Order • Product • Customer Value Objects • Email • Money • Address • Phone Aggregates • Order Aggregate • Customer Aggregate • Product Catalog • Inventory Domain Services • Pricing Service • Inventory Service • Shipping Service • Tax Calculator Repositories • User Repository • Order Repository • Product Repository (Interfaces) Infrastructure Layer • Database • External APIs • Message Queues • File System • Email Service • ORM/SQL • HTTP Clients • Event Bus • Cloud Storage • Logging Bounded Context Key Principles: • Domain layer is independent • Infrastructure depends on domain • UI/App orchestrate business logic
Serverless Architecture
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Cloud Provider (AWS, Azure, GCP) Serverless Architecture Client Applications • Web Apps (React, Vue) • Mobile Apps (iOS, Android) • Desktop Applications API Gateway • Request Routing • Authentication • Rate Limiting Serverless Functions (FaaS) User Service • Register User • Login • Update Profile • Delete User AWS Lambda Order Service • Create Order • Process Payment • Update Status • Cancel Order Azure Functions Notification • Send Email • Send SMS • Push Notifications • Slack Alerts Google Cloud Analytics • Track Events • Generate Reports • Data Processing • ML Inference Serverless File Processing • Image Resize • PDF Generation • Video Transcode • Data Transform Event-Driven Managed Services (BaaS) Authentication • AWS Cognito • Auth0 • Firebase Auth Message Queue • AWS SQS • Azure Service Bus • Google Pub/Sub Event Streaming • AWS EventBridge • Azure Event Grid • CloudEvents Monitoring • CloudWatch • DataDog • New Relic API Management • AWS API Gateway • Azure API Mgmt • Kong Storage & Databases • NoSQL (DynamoDB, CosmosDB) • Object Storage (S3, Blob) • Managed SQL (RDS, Aurora) • CDN (CloudFront) Serverless Benefits ✓ No Server Management ✓ Auto-scaling ✓ Pay-per-execution ✓ Built-in High Availability ✓ Faster Time-to-Market ✓ Reduced Operational Overhead HTTP Timer Event Queue
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Self-Healing & Adaptive Architecture
Self-Healing & Adaptive Architecture Continuous Self-Healing Loop 1. Monitoring & Observability Metrics • CPU/Memory • Response Time • Error Rates Traces • Request Flow • Dependencies • Bottlenecks 2. Anomaly Detection & Analysis AI/ML Detection • Pattern Analysis • Baseline Deviation • Predictive Alerts • Root Cause Thresholds • Static Rules • Dynamic Limits • SLA Violations • Health Checks 3. Automated Response Immediate • Circuit Break • Failover • Restart Scaling • Auto-scale Up/Down • Load Balancing • Resource Alloc 4. Recovery & Adaptation Self-Repair • Config Updates • Code Rollback • Data Recovery • Service Reset Learn & Adapt • Update Models • Tune Params • Pattern Memory • Policy Updates 5. Adaptive Optimization Performance • Cache Tuning • Query Optimization • Resource Tuning • Algorithm Select Architecture • Service Topology • Data Partitioning • Network Routes • Deploy Strategy AI/ML Control Engine Key Technologies & Patterns Circuit Breakers • Hystrix • Resilience4j • Istio • Envoy Proxy • Netflix OSS Auto-Scaling • Kubernetes HPA • AWS Auto Scaling • KEDA • Azure VMSS • GCP Instance Groups Chaos Engineering • Chaos Monkey • Litmus • Gremlin • Chaos Toolkit • PowerfulSeal Observability • Prometheus • Grafana • Jaeger/Zipkin ML/AI Platforms • TensorFlow • Kubeflow • MLflow Service Mesh • Istio • Linkerd • Consul Connect OK ! ✓ Continuous Feedback Loop
Clean Architecture
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Clean Architecture Frameworks & Drivers Web, DB, External APIs Interface Adapters Controllers, Presenters, Gateways Use Cases Business Rules Entities Core Business DB UI API Web Ctrl Pres Gate Repo Dependencies Point Inward ✓ Independent T Testable Benefits: Independent • Testable • Framework Independent • UI Independent • Database Independent
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Hexagonal Architecture (Ports & Adapters)
Hexagonal Architecture (Ports & Adapters) Business Logic Domain Model Primary Ports User API Interface Secondary Ports Repository Interface REST API Adapter Web UI Adapter CLI Adapter Database Adapter Email Adapter File System Adapter Driving Side Primary Adapters (Initiate interactions) Driven Side Secondary Adapters (Respond to requests) P Ports = Interfaces A Adapters = Implementations ⚡ Isolation & Testability Key Benefits • Business logic isolated from external concerns • Easy to test with mock adapters • Technology-agnostic core • Flexible adapter swapping T Easy Testing
Micro Frontends
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Micro Frontends Architecture https://myapp.com Shell Application (Container) Navigation • Routing • Shared Components • Authentication Orchestrates micro frontends Home Products Orders Profile User: John Doe Product Catalog React + TypeScript 🛍️ 📦 💰 Team Alpha Shopping Cart Vue.js + JavaScript 🛒 ➕ ✅ Team Beta User Profile Angular + TypeScript 👤 ⚙️ 🔒 Team Gamma Analytics Svelte + JavaScript 📊 📈 🎯 Team Delta Inter-App Communication & Event Bus Deployment Strategies Build-time NPM Packages Mono-repo Single Bundle Run-time Module Federation iframe Web Components Server-side SSI (Server Side Includes) Edge-side Includes CDN/Edge Independent Deployment Global CDN ✓ Independent Development ⚡ Technology Diversity 🚀 Independent Deployment 👥 Team Autonomy
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Multi-Tenant SaaS Architecture
Multi-Tenant SaaS Architecture Tenant A Enterprise Corp 👤 👤 👤 Tenant B Startup Inc 👤 👤 👤 Tenant C SMB Solutions 👤 👤 👤 Load Balancer / API Gateway Tenant Identification • Routing • Rate Limiting • Authentication Application Tier App Instance 1 Tenant A Context Custom Features App Instance 2 Tenant B Context Standard Features App Instance 3 Tenant C Context Basic Features Data Layer - Tenancy Models Shared Database Row-Level Isolation Users Orders tenant_id = A tenant_id = B Database per Tenant Schema Isolation DB-A Tenant A DB-B Tenant B DB-C Tenant C Hybrid Model Mixed Tenancy Shared Core Data Isolated Custom Data Shared Services Authentication Authorization Billing Service Audit Logging Monitoring Notifications File Storage Analytics Configuration Caching Layer Isolation Strategies Physical Separate Infrastructure Highest Security Highest Cost Logical Shared Infrastructure Data Partitioning Cost Effective Virtual Containers/VMs Resource Isolation Balanced Approach Multi-Tenancy Benefits ✓ Cost Efficiency ⚡ Resource Sharing 🚀 Faster Deployment 🔧 Easier Maintenance 📊 Better Analytics 🔒 Secure Isolation
AI-Native Architecture
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AI-Native Architecture AI Processing Core 🧠 ⚡ Neural Networks Machine Learning 🔒 Secure Isolation Data Processing Zone Encrypted Storage 🔒 Secure Isolation Model Training Zone Federated Learning 🌐 API Gateway Authentication • Rate Limiting • Load Balancing 📱 Edge Node 💻 Edge Node 📊 Data Sources Real-time Streams 🏛️ Model Repo Version Control 📈 Monitoring & Observability Performance • Security • Compliance 🔒 Secure Isolation 🔒 Secure Isolation Architecture Components AI Processing & Edge Nodes Secure Isolation Zones API Gateway & Services Monitoring & Observability Data Sources & Streams Model Repository & Training Security Boundary
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Zero Trust Architecture
Zero Trust Architecture "Never Trust, Always Verify" UNTRUSTED ZONE ⚠️ 🏴‍☠️ Hackers 🦠 Malware 🎭 Insider Threats 💻 APT NETWORK SECURITY LAYER 🛡️ IDENTITY VERIFICATION LAYER 🔐 PROTECTED RESOURCES ✅ Policy Decision Point ⚖️ Real-time Risk Assessment Continuous Verification 🔑 Multi-Factor Authentication MFA • SSO • PKI 📱 Device Trust Verification Certificate • Health App A Database API Files 🚫 VERIFY 🚫 VERIFY 🔍 CONTINUOUS 📊 SIEM / Analytics Behavioral Analysis Threat Detection 🔐 End-to-End Encryption Data in Transit/Rest 👤 Verified User 📱 Unknown Device 🏠 Remote Worker ✓ ✗ ? Zero Trust Principles 1 Never Trust, Always Verify 2 Least Privilege Access 3 Assume Breach Mentality 4 Continuous Monitoring 5 Verify Every Transaction HIGH THREAT SECURE
Hybrid Cloud & Multi-Cloud Architecture
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Hybrid Cloud & Multi-Cloud Architecture HIGH THREAT MONITOR SECURE On-Premises Data Center Database Public Cloud A Computing Services VM Storage API K8s Apps Public Cloud B Analytics Platform AI/ML Data Auth ETL BI Private Cloud Dedicated Resources Secure Backup Vault Comply Audit Edge Computing IoT CDN 5G Cloud Management Monitor Deploy Scale Security Backup Policy ✓ Architecture Components • Hybrid Cloud: Seamless integration between on-premises and cloud resources • Multi-Cloud: Distribution across multiple cloud providers for redundancy and optimization • Edge Computing: Processing closer to data sources for reduced latency • Centralized Management: Unified control plane for monitoring, security, and governance VPN API Direct
Design Patterns
Let’s Dive in
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Singleton, Factory, Builder
Design Patterns: Singleton, Factory, Builder Creational Design Patterns for Object Creation SINGLETON PATTERN Ensures only one instance exists Singleton -instance: Singleton -constructor() +getInstance() +doSomething() 1 Only One Instance FACTORY PATTERN Creates objects without specifying exact class Factory +createProduct() +getProduct(type) Product A method() Product B method() BUILDER PATTERN Constructs complex objects step by step Director construct() Builder +buildPart1() +buildPart2() +getResult() Complex Product part1 part2 Singleton Implementation class Singleton { private static instance; private constructor () public static getInstance () { if (!this.instance) { this.instance = new Singleton (); } return this.instance; } } Factory Implementation class ProductFactory { static createProduct (type) { switch (type) { case 'A' : return new ProductA (); case 'B' : return new ProductB (); default : throw new Error (); } } } Builder Implementation class CarBuilder { setEngine (engine) { this.engine = engine; return this; } setWheels (wheels) { this.wheels = wheels; return this; } } Singleton Benefits • Controlled access to sole instance • Reduced memory footprint • Global point of access • Lazy initialization Use: Database connections, loggers, caches Factory Benefits • Loose coupling between creator and products • Easy to extend with new product types • Centralized object creation logic • Runtime object type determination Use: UI components, database drivers, parsers Builder Benefits • Step-by-step object construction • Different representations of same object • Fine control over construction process • Immutable object creation Use: Complex configurations, SQL queries, documents ONE INSTANCE MULTIPLE PRODUCTS STEP BY STEP Creational Design Patterns - Object Creation Strategies Real-World Examples: Singleton: Application settings, thread pools, device drivers Factory: Payment processors, notification services, file handlers Builder: HTTP requests, email composers, game characters All patterns promote flexible, maintainable code architecture
Adapter, Composite, Decorator
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Structural Design Patterns ADAPTER PATTERN Client Adapter Adaptee Allows incompatible interfaces to work together Like a power adapter for different plug types Structural COMPOSITE PATTERN Component Leaf Composite Composes objects into tree structures Treats individual and composite objects uniformly Structural DECORATOR PATTERN Component ConcreteComponent Decorator Concrete Decorator Adds behavior to objects dynamically Wraps objects with additional functionality Structural Key Differences: • Adapter: Converts one interface to another (interface compatibility) • Composite: Treats individual and composite objects uniformly (part-whole hierarchy) • Decorator: Adds new functionality without altering structure (dynamic enhancement) All three patterns help create flexible, maintainable object structures Code Structure Examples: Adapter class LegacyPrinter { oldPrint(text) { ... { } class PrinterAdapter { print(text) { legacy.oldPrint(text) } } Composite class File { draw() { ... } } class Folder { add(item) { this.items.push(item) } draw() { items.forEach(i => i.draw()) } } Decorator class Coffee } cost() } return 5 } } class MilkDecorator { cost() } return coffee.cost() + 2 } } Real-World Examples: Adapter: • Database drivers (MySQL, PostgreSQL, MongoDB adapters) • Payment gateways (PayPal, Stripe, Square adapters) • Third-party API wrappers (REST to GraphQL adapters) Composite: • File systems (files and folders) • UI components (containers and widgets) • Organization charts (employees and departments) Decorator: • Text formatting (bold, italic, underline) • Middleware (authentication, logging, caching) • Stream processing (compression, encryption) All patterns promote flexible, maintainable code architecture and object composition
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Observer, Strategy, Command
Behavioral Design Patterns OBSERVER PATTERN Subject Observer A Observer B Observer C Notifies multiple objects about state changes One-to-many dependency relationship Behavioral STRATEGY PATTERN Context Strategy ConcreteA ConcreteB ConcreteC Behavioral COMMAND PATTERN Invoker Command Receiver SaveCommand OpenCommand Encapsulates requests as objects Enables undo, queuing, and logging Behavioral Key Differences: • Observer: Defines one-to-many dependency between objects (publish-subscribe) • Strategy: Defines family of algorithms and makes them interchangeable • Command: Encapsulates requests as objects, enabling parameterization and queuing All three patterns define how objects communicate and interact with each other Code Structure Examples: Observer class Subject { notify() { observers.forEach(o => o.update()) } } class Observer { update() { /* react to changes */ } } Strategy class Context { setStrategy(strategy) { this.strategy = strategy } execute() { return this.strategy.algorithm() } } class ConcreteStrategy { algorithm() {...} } Command class Command { execute() { receiver.action() } } class Invoker { setCommand(cmd) { this.command = cmd } invoke() { this.command.execute() } } Real-World Examples: Observer: • Event listeners (DOM events, user interactions) • Model-View architectures (MVC, MVVM) • News subscriptions, stock price notifications Strategy: • Sorting algorithms (quicksort, mergesort, bubblesort) • Payment methods (credit card, PayPal, crypto) • Compression algorithms (ZIP, RAR, 7z) Command: • Text editor operations (copy, paste, undo/redo) • GUI buttons and menu items • Macro recording, job queues, transaction logs All patterns enable loose coupling and flexible communication between objects
CQRS, Event Sourcing, Microservices
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Architectural Patterns CQRS PATTERN (Command Query Responsibility Segregation) Client Application COMMAND SIDE Write Operations • Command Handlers • Write Database QUERY SIDE Read Operations • Query Handlers • Read Database Sync Architectural EVENT SOURCING Application EVENT STORE Immutable sequence of events Event 1 Event 2 Event 3 Event N Current State Architectural MICROSERVICES API Gateway User Service • User DB • Auth Logic Order Service • Order DB • Business Logic Payment Service • Payment DB • Payment Logic Message Bus / Event Bus Architectural Key Characteristics: • CQRS: Separates read and write operations for better scalability and performance • Event Sourcing: Stores state changes as events, enabling audit trails and time travel • Microservices: Decomposes applications into independent, deployable services These patterns often work together to create scalable, maintainable distributed systems Benefits & Challenges: CQRS Benefits: • Optimized read/write models • Better scalability • Performance optimization Challenges: • Data consistency complexity • Increased infrastructure Event Sourcing Benefits: • Complete audit trail • Time travel debugging • Event replay capability Challenges: • Event schema evolution • Snapshot complexity Microservices Benefits: • Independent deployment • Technology diversity • Team autonomy Challenges: • Distributed system complexity • Network latency & failures Real-World Examples & Use Cases: CQRS: • E-commerce platforms (separate product catalog reads from inventory writes) • Banking systems (account balance queries vs transaction processing) • Content management systems (article publishing vs content retrieval) Event Sourcing: • Financial systems (transaction history, audit compliance) • Version control systems (Git commits as events) • Collaborative editing (document change tracking) Microservices: • Netflix (video streaming, recommendations, user management) • Amazon (shopping cart, payment, inventory, shipping) • Uber (ride matching, payment, driver tracking, notifications) Combined Usage: Many modern systems use all three patterns together for maximum scalability and reliability Example: Event-driven microservices with CQRS for read/write separation and event sourcing for audit trails Technologies: Apache Kafka, EventStore, Docker, Kubernetes, API Gateway, Service Mesh
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Domain-Driven Design (DDD)
Domain-Driven Design (DDD) Architecture User Management Context Domain Layer User, Role, Permission Application Layer UserService, AuthService Infrastructure Layer UserRepository, Database Order Management Context Domain Layer Order, OrderItem, Customer Application Layer OrderService, PaymentService Infrastructure Layer OrderRepository, EventStore Inventory Context Domain Layer Product, Stock, Warehouse Application Layer InventoryService, StockService Infrastructure Layer ProductRepository, Cache Event Bus / Message Infrastructure Domain Events, Integration Events, Command/Query Bus Core DDD Building Blocks Entities Objects with identity and lifecycle User, Order, Product Value Objects Immutable objects defined by attributes Money, Address, Email Aggregates Consistency boundaries OrderAggregate Repositories Data access abstraction IUserRepository Domain Services Business logic coordination PricingService Factories Complex object creation OrderFactory DDD Architecture Patterns CQRS Command Query Responsibility Segregation Event Sourcing State from Event History Hexagonal Ports & Adapters Architecture Saga Pattern Distributed Transaction Management Technologies & Tools Apache Kafka, EventStore, Docker, Kubernetes, API Gateway, Service Mesh MediatR, Entity Framework, MongoDB, Redis, RabbitMQ, gRPC
Self-Healing, Load Balancer, Circuit Breaker
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Resilient Architecture: Self-Healing, Load Balancer & Circuit Breaker Client Applications Mobile App Web Client API Client Load Balancer { Algorithm: Round Robin } Health Checks, SSL Termination Session Affinity, Failover Circuit Breaker Pattern CLOSED Normal Operation Requests Pass Through Monitor Failures OPEN Failure Detected Requests Rejected Fast Fail Response HALF-OPEN Testing Recovery Limited Requests Evaluating Health Circuit Breaker Configuration Failure Threshold: 5 failures in 60s | Timeout: 30s | Recovery Time: 60s Fallback: Cached Response, Default Values, Graceful Degradation Metrics: Success Rate, Response Time, Error Rate Microservice Instances Service A Instance 1 ✓ Healthy CPU: 45% Memory: 60% Service A Instance 2 ✓ Healthy CPU: 52% Memory: 58% Service A Instance 3 ✗ Failing CPU: 95% Memory: 98% Service A Instance 4 ⚡ Recovering CPU: 78% Memory: 82% Service A Instance 5 ↗ Auto-Scaled CPU: 25% Memory: 35% Self-Healing System Components Health Monitoring Heartbeat Checks Performance Metrics Error Rate Tracking Resource Utilization Auto-Recovery Service Restart Container Recreation Configuration Reset Rollback Deployment Auto-Scaling Horizontal Scaling Vertical Scaling Load-based Triggers Predictive Scaling Traffic Rerouting Failover Routing Geographic Redirect Load Redistribution Graceful Degradation Alerting & Logging Real-time Alerts Event Correlation Audit Trail Root Cause Analysis Self-Healing Process Flow 1. Detect → 2. Isolate → 3. Recover → 4. Validate → 5. Re-integrate → 6. Learn Technologies & Implementation Load Balancing NGINX, HAProxy, AWS ALB Envoy Proxy, Istio Circuit Breakers Hystrix, Resilience4j Polly, Sentinel Orchestration Kubernetes, Docker Swarm Service Mesh, Consul Monitoring Prometheus, Grafana ELK Stack, Jaeger
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Role-Based Access Control (RBAC)
Role-Based Access Control (RBAC) Users Admin Manager Employee Guest Roles System Administrator Department Manager Regular User Read-Only User Permissions Create Read Update Delete Protected Resources Database Records File System Network Services Applications API Endpoints Admin Panel assigned to have control access to RBAC Flow: Users → Roles → Permissions → Resources
Abstract Factory
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Abstract Factory Design Pattern Client Uses abstract interfaces only <<interface>> AbstractFactory + createProductA() + createProductB() + createProductC() <<interface>> AbstractProductA + operation() <<interface>> AbstractProductB + operation() ConcreteFactory1 (Windows Theme) + createProductA() + createProductB() + createProductC() ConcreteFactory2 (Mac Theme) + createProductA() + createProductB() + createProductC() ProductA1 (Windows Button) + operation() ProductA2 (Mac Button) + operation() ProductB1 (Windows Menu) + operation() ProductB2 (Mac Menu) + operation() uses creates creates implements implements implements implements implements creates creates Abstract Factory creates families of related products without specifying concrete classes Pattern Benefits • Ensures product compatibility • Easy to switch product families • Isolates concrete classes • Promotes consistency • Supports Open/Closed principle • Makes adding new products families straightforward
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Mediator, Chain of Responsibility
Design Patterns: Mediator & Chain of Responsibility Mediator Pattern Mediator {hub} Colleague A {component} Colleague B {component} Colleague C {component} Components communicate through central mediator {loose coupling, centralized control} Chain of Responsibility Handler 1 {first} Handler 2 {second} Handler 3 {third} R Request passes through handler chain {sequential processing} Key Differences Mediator Pattern • Components know about mediator • Centralized communication hub • All interactions go through mediator • {promotes loose coupling} Chain of Responsibility • Handlers linked in sequence • Request flows through chain • Handler decides to process or pass • {dynamic request handling} Common Use Cases Mediator: UI Components, Chat Systems {dialog boxes, event coordination} Chain: Filters, Middleware, Approvals {request processing pipelines}
Producer-Consumer, Thread Pool
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Concurrency Patterns: Producer-Consumer & Thread Pool Producer-Consumer Pattern Producer 1 {creates} Producer 2 {generates} Shared Buffer {synchronized queue} Items: {data packets} Consumer 1 {processes} Consumer 2 {handles} put() put() get() get() Producers create items, consumers process them {decoupled, asynchronous processing} Thread Pool Pattern Task Queue {pending work} Thread Pool {worker threads} T1 T2 T3 T4 T5 T6 assign {busy: T5, idle: others} Client {requests} submit Fixed pool of threads handle queued tasks {resource management, controlled concurrency} Pattern Characteristics Producer-Consumer Benefits • Decouples data production from consumption • Handles different processing speeds {buffering} • Enables asynchronous processing workflows • Thread-safe synchronization via shared buffer • {scalable: multiple producers/consumers} Thread Pool Benefits • Limits thread creation overhead {reuse} • Controls system resource consumption • Manages concurrent task execution efficiently • Queue handles task overflow gracefully • {configurable pool size and policies} Common Implementation Patterns Producer-Consumer: BlockingQueue, Channel {Java: ArrayBlockingQueue, Go: channels} Semaphores, Condition Variables, Event Loops Thread Pool: ExecutorService, WorkerPool {Java: ThreadPoolExecutor, .NET: ThreadPool} Fixed, Cached, Scheduled Thread Pools Real-World Applications Producer-Consumer Applications • Log processing, Message queues, Data streaming • {Kafka, RabbitMQ, Apache Pulsar, ETL pipelines} Thread Pool Applications • Web servers, Database connections, Batch processing • {Tomcat, Nginx workers, Connection pooling}
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Service Locator, DAO, Repository
Data Access Patterns: Service Locator, DAO & Repository Service Locator Pattern Client {requests} Service Locator {registry & factory} Service Registry UserService {business logic} OrderService {operations} EmailService {notifications} locate Central registry for service discovery and instantiation {decouples client from service implementations} DAO Pattern Business Logic {service layer} UserDAO Interface {abstract data access} MySQLUserDAO {SQL impl} MongoUserDAO {NoSQL impl} MySQL DB MongoDB Encapsulates data access logic with abstract interface {database-agnostic business logic} Repository Pattern Domain/Business Layer {domain entities & logic} IUserRepository {collection-like interface} UserRepository {concrete implementation} Data Mapper / ORM {Entity Framework, Hibernate} Database User Order Product Domain-driven collection-like interface for aggregates {encapsulates query logic, domain-focused} Key Differences & Relationships Service Locator • Service discovery and dependency resolution • Alternative to Dependency Injection • {centralized service registry} • Can manage DAO/Repository instances DAO Pattern • Abstract data access operations • Database/technology agnostic • {CRUD operations focus} • Table-centric, simpler approach Repository Pattern • Domain-driven design approach • Collection-like interface for aggregates • {complex query encapsulation} • Higher-level abstraction than DAO When to Use Each Pattern Service Locator: Legacy systems, Plugin architectures {when DI container not available} DAO: Simple CRUD, Multiple data sources {straightforward data access needs} Repository: DDD, Complex domains {rich domain models with aggregates} {patterns often used together}
Saga Pattern
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Saga Pattern Architecture Saga Orchestrator Coordinates workflow Payment Service processPayment() compensatePayment() {status: "success"} Inventory Service reserveItems() releaseItems() {items: "reserved"} Shipping Service createShipment() cancelShipment() {shipment: "created"} 1. Execute 2. Execute 3. Execute Compensate Compensate Compensate Transaction States Success: All Complete Failure: Compensating Compensated: Rolled Back Success Response: {"status": "completed", "transactionId": "tx-123"} Failure Response: {"status": "compensated", "error": "payment failed"} Key Benefits: • Maintains data consistency across distributed services • Handles failures gracefully with compensation logic • No distributed locks - improves system performance • Supports complex business workflows with rollback capability
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Reactive Streams, Backpressure
Reactive Streams & Backpressure Publisher Data Source {rate: "1000/sec"} Subscription Demand Control request(n) Subscriber Data Consumer {capacity: "100/sec"} Data Stream Backpressure Signal Backpressure Strategies Buffer Item 1 Item 2 Item 3 Store excess data Drop ✓ ✗ Discard overflow {dropped: 500} Latest Latest Old Older Keep most recent Block Pause publisher Wait for capacity Reactive Streams API Publisher<T> subscribe(Subscriber s) {method: "reactive"} Subscriber<T> onSubscribe(Subscription s) onNext(T item) onError(Throwable t) onComplete() Subscription request(long n) cancel() Processor<T,R> extends Publisher<R> extends Subscriber<T> Flow Control Example 1. Subscriber requests: request(10) 2. Publisher sends: {"data": [item1, item2, ..., item10]} 3. Subscriber processes and requests more: request(5) Benefits: Memory efficient, prevents overwhelm No blocking, self-regulating system Handles fast producers, slow consumers
Secure Pipeline Pattern
14
Secure Pipeline Pattern (DevSecOps) Source Code Git Repository {branch: "main"} Commit hooks Security Scan SAST/SCA {vulnerabilities: 0} Code analysis Secure Build Container Build {signed: true} Image signing Security Test DAST/IAST {passed: true} Penetration test Secure Deploy Production {encrypted: true} Zero downtime Security Gates & Quality Checks G1 Secret scan {secrets: "none"} G2 CVE check {score: "< 7.0"} G3 Image scan {malware: "clean"} G4 Runtime test {exploits: "blocked"} G5 Compliance {SOC2: "passed"} Integrated Security Tools SAST (Static Analysis) • SonarQube • Checkmarx • Veracode {coverage: "95%"} DAST (Dynamic Analysis) • OWASP ZAP • Burp Suite • Nessus {runtime: "secure"} SCA (Composition Analysis) • Snyk • WhiteSource • Black Duck {dependencies: "clean"} Container Security • Twistlock • Aqua • Falco {images: "hardened"} Continuous Monitoring & Incident Response 🔍 Real-time threat detection 📊 Security metrics dashboard 🚨 Automated incident response 📋 Compliance reporting 🔄 Feedback loop integration ⚡ Auto-remediation triggers Security Principles 🛡️ Shift Left: Early security integration 🔒 Zero Trust: Never trust, always verify 🔐 Defense in Depth: Multiple security layers 📝 Immutable Infrastructure: {"policy": "read-only"} ⚡ Fail Fast: Quick detection and response 🔍 Observability: Full visibility pipeline
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Pipeline Pattern (for ML), Adapter
ML Pipeline Pattern with Adapter Integration Data Ingestion Raw Data Sources {format: "mixed"} CSV, JSON, Parquet Preprocessing Clean & Transform {cleaned: true} Normalize, Scale Feature Engineering Extract Features {features: 128} PCA, Encoding Model Training Train & Validate {accuracy: 0.95} Cross-validation Model Evaluation Test & Metrics {f1_score: 0.93} ROC, Precision Deployment Serve Model {status: "live"} REST API Adapter Pattern Integration Data Source Adapters CSV Adapter JSON Adapter DB Adapter API Adapter Stream Adapter File Adapter Pipeline Interface Standardized API process(data) validate(schema) transform(rules) {interface: "unified"} Model Framework Adapters TensorFlow PyTorch Scikit-learn XGBoost LightGBM Custom Model Deployment Adapters REST API gRPC Batch Stream Cloud Edge Pipeline Configuration YAML Configuration: pipeline: stages: - name: "data_ingestion" adapter: {"type": "csv", "config": "..."} - name: "preprocessing" Benefits of ML Pipeline + Adapter Pattern 🔄 Modular & Reusable Components 🔧 Easy Integration of New Data Sources ⚡ Framework Agnostic Model Training 📊 Consistent Data Processing Interface 🚀 Flexible Deployment Options Implementation Example class DataAdapter: def load(self, source): pass def validate(self, data): pass class CSVAdapter(DataAdapter): def load(self, file_path): return pd.read_csv(file_path) class MLPipeline: def __init__(self, adapters): self.adapters = adapters def run(self, config): # Process through pipeline # Usage Example pipeline = MLPipeline({"csv": CSVAdapter(), "json": JSONAdapter()}) result = pipeline.run(config={"source": "data.csv", "model": "xgboost"})
Best Practices & Industry Techniques
Let’s Dive in
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Scaffolding
Software Development Scaffolding Process 1. Project Initialization npm init, create-react-app, rails new, django-admin startproject 2. Code Generation rails generate, yeoman, angular-cli, plop.js 3. Boilerplate Setup Folder structure, config files, basic components 4. Development Tools Hot reload, dev server, debugging tools 5. Testing Framework Jest, Mocha, RSpec, test templates 6. Build Pipeline Webpack, Gulp, Grunt, build scripts 7. Development Phase Iterative coding with scaffolding support 8. Refactoring Remove scaffolding, optimize code 9. Production Ready Clean, optimized, deployment ready Popular Scaffolding Tools Frontend: • Create React App • Angular CLI • Vue CLI • Vite Backend: • Ruby on Rails • Django • Express Generator • Spring Boot Generators: • Yeoman • Plop.js • Hygen • Cookiecutter Full-Stack: • Next.js • Nuxt.js • T3 Stack Benefits of Software Scaffolding Rapid Prototyping: Quick project setup and initial structure Consistency: Standardized project structure and conventions Best Practices: Built-in testing, linting, and build processes Developer Experience: Hot reload, debugging tools, and automation Scaffolding provides temporary structure to accelerate development, later refined or removed
Dependency Injection
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Dependency Injection (DI) - Concepts & Patterns Without Dependency Injection (Tight Coupling) Class A new ClassB() new ClassC() creates Class B Database Access creates Class C Email Service Problems: Hard to test, tightly coupled, difficult to maintain With Dependency Injection (Loose Coupling) DI Container manages dependencies injects Class A IDatabase db IEmail email IDatabase Interface IEmail Interface Benefits: Easy to test, loosely coupled, flexible, maintainable Types of Dependency Injection 1. Constructor Injection Dependencies injected via constructor public class OrderService { public OrderService(IRepository repo) {...} 2. Property Injection Dependencies set via properties public class OrderService { public IRepository Repository {get; set;} 3. Method Injection Dependencies passed to methods public void ProcessOrder( IRepository repo, IEmail email) {...} DI Container Lifecycle Management Singleton One instance Shared across app Transient New instance Every request Scoped Per scope (e.g., HTTP request) Popular DI Frameworks .NET: Built-in DI, Autofac, Unity, Castle Windsor Java: Spring Framework, Google Guice, Dagger JavaScript: InversifyJS, Awilix, TSyringe Python: Dependency Injector, Inject, Pinject PHP: Symfony DI, Laravel Container, PHP-DI Key Benefits of Dependency Injection Testability: Easy to mock dependencies for unit testing Flexibility: Easy to swap implementations without code changes Maintainability: Loose coupling makes code easier to maintain Single Responsibility: Classes focus on their core logic Configuration: Centralized dependency management Lifecycle Management: Automatic object creation and disposal
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SOLID Principles
SOLID Principles of Object-Oriented Design S Single Responsibility O Open/Closed L Liskov Substitution I Interface Segregation D Dependency Inversion Single Responsibility Principle (SRP) Definition: A class should have only one reason to change. ❌ Bad Example class User { save() // Database logic sendEmail() // Email logic ✅ Good Example class User { /* user data */ } class UserRepository { save(user) { ... } } class EmailService { send(user) { ... } Open/Closed Principle (OCP) Definition: Classes should be open for extension, closed for modification. ❌ Bad Example class AreaCalculator { calculate(shape) { if (shape.type === 'circle') return π * r² ✅ Good Example interface Shape { calculateArea() } class Circle implements Shape class Rectangle implements Shape Liskov Substitution Principle (LSP) Definition: Objects of a superclass should be replaceable with subclasses. ❌ Bad Example class Rectangle { setWidth(), setHeight() } class Square extends Rectangle ✅ Good Example interface Shape { calculateArea() } class Rectangle implements Shape class Square implements Shape Interface Segregation Principle (ISP) Definition: No client should depend on methods it doesn't use. ❌ Bad Example interface Worker { work(), eat(), sleep() } class Robot implements Worker ✅ Good Example interface Workable {work()} interface Eatable {eat()} interface Sleepable {sleep()} class Human: Workable, Eatable class Robot: Workable Dependency Inversion Principle (DIP) Definition: Depend on abstractions, not concretions. ❌ Bad Example class OrderService { MySQLDatabase db = new MySQLDatabase() } ✅ Good Example class OrderService { IDatabase database OrderService(IDatabase db) } // Injected via DI container Benefits of Following SOLID Principles Maintainability: Code is easier to maintain and modify Testability: Classes are easier to unit test in isolation Flexibility: Easy to extend and modify behavior Reusability: Components can be reused across projects Loose Coupling: Reduced dependencies between classes Scalability: Architecture can grow with requirements Readability: Code is cleaner and easier to understand Debugging: Easier to locate and fix issues Team Collaboration: Consistent code structure Quality: Overall improvement in code quality Key Takeaway SOLID principles work together to create a foundation for writing clean, maintainable, and extensible object-oriented code that stands the test of time. SOLID principles were introduced by Robert C. Martin (Uncle Bob) for better software design
KISS/YAGNI/DRY
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Programming Principles KISS Keep It Simple, Stupid Process: 1. Identify complexity 2. Simplify solution 3. Test & iterate YAGNI You Aren't Gonna Need It Process: 1. Question each feature 2. Build only what's needed 3. Add features when required DRY Don't Repeat Yourself Process: 1. Identify duplication 2. Extract common code 3. Create reusable functions Development Process 1. Start simple (KISS) 2. Build only what's needed (YAGNI) 3. Refactor duplicates (DRY) 4. Maintain & iterate 5. Review & optimize
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TDD & BDD
TDD & BDD Development Processes TDD - Test-Driven Development RED Write Test GREEN Make Pass REFACTOR Improve TDD Process: 1. Write failing test (Red) 2. Write minimal code to pass (Green) 3. Refactor and improve (Blue) BDD - Behavior-Driven Development GIVEN Context WHEN Action THEN Outcome Example Scenario: Given: User is on login page When: User enters valid credentials Then: User should be redirected to dashboard BDD Process: 1. Define behavior in plain language 2. Write scenarios (Given-When-Then) 3. Implement step definitions TDD + BDD Integration Workflow 1 BDD Define Behavior 2 TDD Write Unit Tests 3 TDD Implement Code 4 Both Verify Behavior TDD Benefits • Better code quality • Faster debugging • Confidence in refactoring BDD Benefits • Clear requirements • Stakeholder collaboration • Living documentation
Code Smells & Refactoring
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Code Smells & Refactoring Techniques Common Code Smells Long Method Too many lines Large Class Too many responsibilities Duplicate Code Copy-paste programming Feature Envy Using other class data Data Clumps Grouped parameters God Object Knows/does everything Refactoring Process 1 Identify Smell 2 Apply Technique 3 Verify Tests Pass Refactoring Techniques Extract Method Fixes: Long Method • Break large methods into smaller ones • Give meaningful names • Single responsibility per method Extract Class Fixes: Large/God Class • Split responsibilities • Create focused classes • Improve cohesion Move Method Fixes: Feature Envy • Move method to appropriate class • Follow data usage patterns • Reduce coupling Replace Magic Numbers Fixes: Magic Numbers • Use named constants • Improve readability • Centralize configuration Parameter Object Fixes: Data Clumps • Group related parameters • Create meaningful objects • Reduce parameter lists Pull Up Method Fixes: Duplicate Code • Move common code to parent • Eliminate duplication • Use inheritance properly Replace Conditional with Polymorphism Fixes: Long Switch/If chains • Use inheritance/interfaces • Eliminate type checking Rename Method/Variable Fixes: Poor naming • Use descriptive names • Reveal intention • Improve self-documentation Refactoring Benefits • Improved code readability and maintainability • Reduced technical debt and bug likelihood • Better design patterns and architecture
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Git Flow & Trunk-Based Dev
Git Flow vs Trunk-Based Development Git Flow master v1.0 v2.0 v2.1 develop feature/login feature/dashboard release/2.0 hotfix/critical-bug Git Flow Process 1. Create feature branch from develop 2. Work on feature, commit changes 3. Merge feature back to develop 4. Create release branch from develop 5. Test, fix bugs, merge to master & develop 6. Create hotfix from master if needed Trunk-Based Development main v1.0 v2.0 feature (1-2 days) feature (1-2 days) Feature Flags: Toggle incomplete features Trunk-Based Process 1. Create short-lived feature branch (1-2 days max) 2. Commit frequently to main branch 3. Use feature flags for incomplete features 4. Continuous integration & deployment 5. All developers work on same branch 6. Fast feedback & quick releases Automated CI/CD Pipeline: Build → Test → Deploy Comparison: Git Flow vs Trunk-Based Development Aspect Git Flow Trunk-Based Development Branch Strategy Multiple long-lived branches Single main branch + short feature branches Complexity High - Multiple branch types & rules Low - Simple, single branch focus Release Frequency Planned releases (weeks/months) Continuous deployment (daily/hourly) Best for Team Size Large teams, distributed development Small to medium teams CI/CD Integration Moderate - Batch testing & deployment Excellent - Continuous integration essential Merge Conflicts Higher risk - Long-lived branches Lower risk - Frequent integration When to Use Each? Use Git Flow when: • Large, distributed teams • Need formal release process • Multiple versions to maintain Use Trunk-Based when: • Small to medium teams • Continuous deployment • Fast iteration cycles
API First Design
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API First Design API Contract 1. DESIGN API Specification OpenAPI/Swagger 2. MOCK Prototype Early Testing 3. TEST Contract Testing Validation 4. BUILD Implementation Backend/Frontend Web App Frontend Mobile App iOS/Android Third Party Integration IoT Devices Sensors Key Benefits • Parallel Development • Consistent Interface • Early Feedback • Reduced Integration Issues • Better Documentation • Faster Time to Market API-First Development Process
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Feature Flags
Feature Flags Feature Flag Service Configuration Management Real-time Updates Web App React/Vue Frontend Mobile App iOS/Android Native API Server Node.js/Java Backend Microservice Payment/Auth Service ✓ NEW_CHECKOUT: ON ✗ BETA_FEATURES: OFF ✓ DARK_MODE: ON % A/B_TEST: 50% Common Use Cases Feature Rollouts Gradual deployment Risk mitigation A/B Testing Experiment tracking Performance metrics Kill Switches Emergency disable System protection User Targeting Segment-based Personalization Canary Releases Limited exposure Quality assurance 🎛️ Control Panel Real-time Configuration Centralized Feature Management Runtime Feature Control
Monorepo vs Polyrepo
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Monorepo vs Polyrepo VS MONOREPO Single Repository 📁 company-monorepo frontend React App backend API Server mobile Native App shared Common Utils docs Documentation tools Build Scripts POLYREPO Multiple Repositories 📁 frontend-app React Repository Team A 📁 api-service Backend Repository Team B 📁 mobile-app Native Repository Team C 📁 shared-lib Common Library Platform Team 📁 docs Documentation DevOps Team 📁 infra Infrastructure DevOps Team Comparison Aspect Monorepo Polyrepo Code Sharing Easy, direct imports Complex, requires packages Dependencies Unified, consistent versions Version conflicts possible Build & CI Complex, but unified pipeline Simple, independent builds Team Autonomy Shared ownership, coordination Full ownership, independence Refactoring Atomic, cross-project changes Requires coordination Examples: Google, Facebook, Microsoft Examples: Netflix, Amazon, GitHub
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Infrastructure as Code (IaC)
Infrastructure as Code (IaC) Define → Version → Deploy → Manage Infrastructure through Code 📝 Infrastructure Code resource "aws_instance" "web" { ami = "ami-0c02fb55956c7d316" instance_type = "t3.micro" tags = { Name = "WebServer" Environment = "Production" } } 🛠️ IaC Tools Terraform Multi-cloud CloudForm AWS Native Pulumi Modern IaC Ansible Config Mgmt ARM Azure Native CDK Code-first ☁️ Cloud Infrastructure Compute EC2, VMs Storage S3, Disks Network VPC, LB Database RDS, NoSQL Security IAM, SG Monitor Logs, Alerts IaC Workflow 1. Write Code 2. Version Control 3. Plan & Review 4. Apply Changes 5. Monitor State ✅ Benefits • Consistency & Repeatability Identical environments every time • Version Control & Audit Trail Track all infrastructure changes • Automation & Speed Deploy in minutes, not hours • Disaster Recovery Rebuild infrastructure quickly • Cost Optimization Tear down unused resources 📋 Best Practices • Modular & Reusable Code Create reusable modules and templates • State Management Use remote state storage with locking • Environment Separation Separate dev, staging, production • Security & Compliance Implement security scanning and policies • Documentation Document architecture and dependencies
Feature Toggles & Rollbacks
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Feature Toggles & Rollbacks Feature Toggles New Dashboard Beta Features A/B Testing Rollback System v2.1 v2.0 v1.9 Failed Warning Stable ROLLBACK Deploy Benefits 🛡️ Risk Mitigation Reduce deployment risks ⚡ Instant Recovery Quick rollback capability 📊 Gradual Rollout Control feature exposure 🧪 Safe Testing Test in production safely
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Observability by Design
Observability by Design 📊 METRICS CPU Usage Memory Response Time 📝 LOGS INFO: Request processed WARN: High latency ERROR: Connection failed 🔗 TRACES API Gateway Auth Service Database Cache Application Architecture with Built-in Observability Frontend React App API Gateway Kong/Nginx Services Microservices Database PostgreSQL Observability Layer (OpenTelemetry, Jaeger, Prometheus) Design Principles Instrumentation First Build monitoring into every component 🔧 Context Propagation Trace requests across service boundaries 🔗 Structured Data Consistent schemas and formats 📋 Automated Alerting Proactive issue detection 🚨
A/B Testing
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A/B Testing Split Testing for Data-Driven Decisions Traffic Split 👥 1000 Users 50/50 Variant A (Control) Buy Now 500 Users Variant B (Test) Get Started 500 Users Test Results Variant A Variant B 8.0% 12.0% Conversions: 40 / 500 Conversions: 60 / 500 Confidence: 95% Confidence: 95% ✓ Statistically Significant Key Metrics to Track % Conversion Rate Primary success metric 👆 Click-through Rate User engagement ↩️ Bounce Rate User retention $ Revenue per User Business impact ⏱️ Time on Page Content quality Best Practices: • Test one variable at a time • Run tests for sufficient duration • Ensure statistical significance • Use proper sample sizes • Consider external factors • Document all changes and results
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Tech Debt Tracking
Tech Debt Tracking Technical Debt Accumulation Over Time Q1 Q2 Q3 Q4 Now Code Debt Architecture Test Debt Doc Debt Debt Categories & Impact Code Debt • Duplicated code • Poor naming • Complex methods Impact: High Architectural • Tight coupling • Missing patterns • Scalability issues Impact: Critical Test Debt • Low coverage • Brittle tests • No integration tests Impact: Medium 📝 Documentation • Outdated docs • Missing API docs • No onboarding Impact: Low Priority Matrix High Impact Low Impact High Effort Low Effort A1 A2 B1 B2 C1 C2 DO LATER AVOID DO FIRST DO NEXT Tracking Dashboard Total Debt Score 847 +12% this month Items Tracked 23 5 resolved Resolution Rate 2.1 items/sprint Trend 📈 Increasing Action Items: Refactor user authentication module (High Priority) Add unit tests for payment service (Low Effort, High Impact) Update API documentation (Schedule for next sprint) Review architectural patterns in core services
Modern Industry Trends to Integrate
Let’s Dive In
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AI-First Architecture Thinking
AI-First Architecture AI Core Engine ML Models Neural Networks Training Pipeline Inference Engine Data Intelligence Layer Real-time Processing Vector Databases Knowledge Graphs Intelligent APIs Natural Language Computer Vision Predictive Analytics Decision Making Smart Orchestration Workflow Automation Resource Optimization Load Balancing Self-Healing Adaptive Interfaces Personalization Context Awareness Multi-modal Input Real-time Adaptation AI-Enhanced User Experience Conversational UI Proactive Assistance Intelligent Automation AI-driven decision making at every layer Intelligent services auto-scale and adapt Data flows intelligently processed Foundation models power everything
MLOps & AIOps
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MLOps & AIOps Intelligent Operations Ecosystem MLOps Machine Learning Operations Data Pipeline • Data Ingestion • Feature Engineering • Data Validation Model Training • Experiment Tracking • Hyperparameter Tuning • Model Versioning Model Deployment • CI/CD Pipelines • A/B Testing • Canary Releases Model Monitoring • Performance Metrics • Data Drift Detection • Model Decay Analysis Governance • Model Registry • Compliance Tracking • Audit Trails Automated Retraining & Feedback Loops Continuous learning and model improvement AIOps Artificial Intelligence for IT Operations Data Collection • Log Aggregation • Metrics Ingestion • Event Streaming Anomaly Detection • Pattern Recognition • Threshold Analysis • Behavioral Baselines Root Cause Analysis • Correlation Analysis • Dependency Mapping • Impact Assessment Predictive Analytics • Capacity Planning • Failure Prediction • Performance Forecasting Automation • Self-Healing • Auto-Scaling • Incident Response Intelligent Operations & Decision Making AI-driven infrastructure management Shared Infrastructure & Platform Kubernetes Docker Cloud Services Observability Security GitOps API Gateway Service Mesh Data Storage Networking Feedback Loop Model Insights Key Benefits • Reduced MTTR • Automated Operations Integration Points • Shared Monitoring • Cross-Platform Analytics
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Composable Architecture
Composable Architecture Modular, Flexible, and Scalable System Design Core Principles • Modular Components • Loose Coupling • High Cohesion • Reusability • Interoperability Key Benefits • Faster Time to Market • Reduced Development Cost • Easy Maintenance • Technology Flexibility • Vendor Independence Experience Layer Web App Mobile App Voice UI IoT Interface API Portal API Gateway & Orchestration Layer Authentication Rate Limiting Load Balancing Composition Business Services Layer User Service Authentication Profile Mgmt Product Service Catalog Inventory Order Service Processing Fulfillment Payment Service Processing Billing Notification Service Email/SMS Push Analytics Service Tracking Reporting Search Service Indexing Query Data Layer SQL Database NoSQL DB Cache Layer Message Queue File Storage Data Lake Infrastructure Layer Kubernetes Docker Cloud Platform Service Mesh Monitoring Dynamic Composition Service Orchestration Pluggable Components Business Logic Isolation Data Source Abstraction Platform Independence
Event Mesh vs Service Mesh
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Event Mesh vs Service Mesh Event Mesh Event-Driven Architecture Producer A Order Events User Events Product Events Producer B Payment Events Shipping Events Inventory Events Producer C Analytics Events Audit Events Error Events Event Brokers & Routers Kafka RabbitMQ Pulsar NATS Stream Processing Real-time Analysis Event Filtering Event Store Event Sourcing Replay Capability Consumer A Email Service Notification Alerts Consumer B Analytics DB Reporting Dashboard Consumer C Audit Service Compliance Monitoring Event Mesh Characteristics • Asynchronous Communication: Fire-and-forget, non-blocking • Event-Driven: React to state changes and business events • Temporal Decoupling: Producers and consumers independent • Event Streaming: Real-time data flows and processing • Scalable: Handle high-volume event streams Service Mesh Request-Response Architecture Service A User API Authentication Proxy Envoy Service B Order API Processing Proxy Envoy Service C Payment API Billing Proxy Envoy Control Plane Service Discovery Traffic Management Security Policies Load Balancing Circuit Breaking Observability Retry Logic mTLS Service D Inventory API Stock Mgmt Proxy Envoy Service E Shipping API Logistics Proxy Envoy Service F Analytics API Reporting Proxy Envoy Service Mesh Characteristics • Synchronous Communication: Request-response pattern • Service-to-Service: Direct API calls between services • Infrastructure Layer: Network proxy for each service • Traffic Management: Load balancing, routing, security • Observability: Metrics, logging, distributed tracing Key Differences Event Mesh • Asynchronous, event-driven • Temporal decoupling • Event streaming & processing Service Mesh • Synchronous, request-response • Direct service coupling • Infrastructure & security layer Use Cases Event Mesh: Real-time analytics, IoT, streaming Service Mesh: Microservices, API management Both can complement each other
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Multi-Tenant SaaS
Multi-Tenant SaaS Architecture Load Balancer API Gateway Tenants Tenant A Users: 500 Tenant B Users: 1,200 Tenant C Users: 800 + More Tenants Application Layer App Server 1 Node.js Multi-tenant Logic App Server 2 Node.js Multi-tenant Logic App Server 3 Node.js Multi-tenant Logic Auth Service JWT/OAuth Tenant Isolation Data Layer Shared Database PostgreSQL Row-Level Security tenant_id in all tables Tenant DBs Dedicated Schemas Enterprise Tenants Better Isolation Cache Layer Redis Tenant-keyed Session Store File Storage AWS S3 Tenant folders Access control Monitoring Logs Metrics Per-tenant High Risk Medium Risk Low Risk
Internal Developer Platforms
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Internal Developer Platform Architecture Developer Experience Layer IDE Extensions VS Code IntelliJ CLI Tools Local Dev Docker Compose Tilt/Skaffold Hot Reload Developer Portal Backstage/Port Service Catalog Templates Documentation API Docs Runbooks Tutorials Self-Service Environment Provisioning Resource Mgmt Automation & CI/CD Layer CI/CD Pipelines GitHub Actions GitLab CI Jenkins/Tekton GitOps ArgoCD/Flux Config Mgmt Drift Detection Security SAST/DAST Container Scan Policy Engine Testing Unit/Integration E2E Testing Quality Gates Infrastructure & Runtime Layer Container Runtime Kubernetes Docker CRI-O/containerd Service Mesh Istio/Linkerd Traffic Mgmt mTLS/Observ Databases PostgreSQL Redis/MongoDB Operators Message Queue Kafka/RabbitMQ Event Streaming Pub/Sub Storage Persistent Vols Object Storage Backup/DR Observability & Operations Monitoring Prometheus Grafana Alertmanager Logging ELK/Loki Fluentd/Fluent Log Aggregation Tracing Jaeger/Zipkin OpenTelemetry Distributed Trace Cost Mgmt Kubecost Resource Usage FinOps Platform Engineering Team Maintains, operates, and evolves the platform Provides golden paths and reduces cognitive load Tech Debt Risk High Medium Low
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Design Tokens & Systems
Design Tokens & Systems Design Tokens Colors primary-600 cyan-500 red-500 Typography Heading font-size: 24px Body Text font-size: 16px Spacing xs: 4px sm: 8px md: 16px Border Radius sm: 4px md: 8px lg: 12px Components Button Uses: primary-600, md spacing Card Uses: md radius, lg spacing Input field Input Uses: sm radius, body text Warning Alert This is an alert message Uses: warning colors, sm radius Badge Badge Uses: cyan-500, full radius Design System Benefits: Consistency across products Faster development cycles Easier maintenance & updates Better collaboration System Structure: Documentation & Guidelines Component Library Design Tokens Token Flow: Tokens → Components → Applications → Consistent User Experience
Infrastructure Scaffolding via GenAI
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Infrastructure Scaffolding via GenAI Developer Input Natural Language "Deploy microservice with Redis cache" Requirements • High Availability • Auto-scaling • Monitoring Preferences AWS/GCP/Azure Terraform/Pulumi Team Standards Context Existing infra Team policies Compliance Templates Golden paths Best practices Org standards 🤖 GenAI Processing Engine Intent Understanding NLP • Context Analysis Architecture Design Pattern Matching • Optimization Code Generation IaC • Configs • Scripts Validation Security • Compliance Generated Infrastructure Code Terraform/Pulumi resource "aws_ecs_*" auto-scaling config security groups K8s Manifests Deployment.yaml Service.yaml HPA.yaml Helm Charts values.yaml templates/ Chart.yaml CI/CD Pipelines .github/workflows GitLab CI Tekton tasks Configs Monitoring Logging Alerting Documentation README.md Architecture docs Runbooks Automated Deployment Infrastructure Auto-provisioning Resource tagging Cost optimization Applications Container deploy Service mesh Load balancing Monitoring Metrics collection Dashboards Alerts Security IAM policies Network security Compliance Continuous Learning & Feedback Performance metrics • User feedback • Pattern recognition Model improvement • Template updates • Best practice evolution Tech Debt Risk High - Generated code quality Medium - Template drift Low - Standardized output
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Prompt Engineering Patterns
Prompt Engineering Patterns Essential patterns for effective AI communication Chain-of-Thought Pattern: "Let's think step by step..." Example: 1. Identify the problem 2. Break down components 3. Solve systematically 4. Verify the solution Few-Shot Learning Pattern: Provide examples before the task Structure: Example 1: Input → Output Example 2: Input → Output Your task: Input → ? Role-Based Prompting Pattern: "You are an expert [role]..." Roles: Technical Expert Creative Writer Data Analyst Template Pattern Structure: Context: [Background information] Task: [What you want done] Format: [Output specification] Constraints: [Limitations/rules] Examples: [Sample outputs] Reusable & Consistent Constraint Pattern Purpose: Define boundaries and limitations Examples: • "In exactly 100 words" • "Without using technical jargon" • "For a 10-year-old audience" • "Using only bullet points" Iterative Refinement Process: Initial Prompt Review Output Refine & Retry Continuous improvement cycle Meta-Prompting Concept: Ask AI to improve your prompts Example: "Help me improve this prompt to get better results: [your original prompt]" Output Formatting Specify format: Control how responses are structured Formats: • JSON structure • Markdown table • Numbered list • XML tags 💡 Best Practices • Be specific and clear in your instructions • Combine multiple patterns for complex tasks • Test and iterate to find what works best • Use examples to clarify expectations • Break complex tasks into smaller steps • Specify output format and constraints