Comprehensive System Design Curriculum: From Novice to Principal Engineer

1. Fundamentals of System Design

1.1. Introduction to System Design

• What is system design?
• Importance in software engineering
• System design interview overview

1.2. Basic Principles and Concepts

• Modularity and abstraction
• Coupling and cohesion
• SOLID principles in system design

1.3. Trade-offs in System Design

• Performance vs. scalability
• Reliability vs. cost
• Consistency vs. availability

1.4. Non-Functional Requirements

• Scalability
• Reliability
• Availability
• Maintainability
• Extensibility

1.5. Back-of-the-Envelope Calculations

• Estimating system capacity
• Traffic estimates
• Storage estimates

1.6. Mini-Project

• Design and implement a simple key-value store in Go
• Implement basic CRUD operations
• Add simple persistence to disk

2. Network Protocols and Communication

2.1. OSI Model and TCP/IP Stack

• Understanding network layers
• Protocol encapsulation

2.2. TCP/IP Deep Dive

• Connection establishment (3-way handshake)
• Flow control and congestion control
• TCP vs. UDP: use cases and trade-offs

2.3. HTTP and HTTPS

• Request-response cycle
• HTTP methods and status codes
• HTTPS and TLS/SSL

2.4. WebSockets

• Real-time bidirectional communication
• WebSocket protocol
• Use cases and limitations

2.5. RESTful APIs

• REST principles
• Resource naming conventions
• HTTP methods in REST
• Idempotency and safety

2.6. gRPC

• Protocol Buffers
• Unary, server streaming, client streaming, and bidirectional streaming
• gRPC vs. REST

2.7. GraphQL

• Schema definition
• Queries and mutations
• Resolvers
• GraphQL vs. REST

2.8. Mini-Projects

• Build a RESTful API server in Go
• Implement a real-time chat application using WebSockets
• Create a gRPC service with bidirectional streaming

3. Databases and Data Storage

3.1. Relational Databases

• ACID properties
• Normalization and denormalization
• Transactions and isolation levels

3.2. SQL Deep Dive

• Advanced querying techniques
• Joins and subqueries
• Window functions
• Common Table Expressions (CTEs)

3.3. NoSQL Databases

• Types: Document, Key-Value, Column-family, Graph
• CAP theorem in practice
• Eventual consistency

3.4. Database Selection Criteria

• Use cases for different database types
• Evaluating database options for specific requirements

3.5. Data Modeling

• Entity-Relationship Diagrams (ERD)
• Object-Relational Mapping (ORM)
• Schema design best practices

3.6. Indexing Strategies

• B-tree and hash indexes
• Composite indexes
• Full-text search indexes

3.7. Query Optimization

• Execution plans
• Index usage and query tuning
• Optimizing slow queries

3.8. Mini-Projects

• Implement a simple document store with basic querying in Go
• Design and implement a relational schema for a complex domain
• Build a query optimizer for your document store

4. Caching Strategies

4.1. Caching Fundamentals

• Purpose and benefits of caching
• Cache hit/miss
• Time-to-live (TTL) and expiration policies

4.2. Caching Layers

• Browser caching
• CDN caching
• Application caching
• Database caching

4.3. Cache Placement Strategies

• Cache-aside (Lazy loading)
• Read-through
• Write-through
• Write-behind (Write-back)

4.4. Cache Eviction Policies

• Least Recently Used (LRU)
• Least Frequently Used (LFU)
• First In First Out (FIFO)
• Random Replacement

4.5. Distributed Caching

• Consistency challenges
• Cache invalidation strategies
• Thundering herd problem

4.6. In-Memory Caching with Redis

• Redis data structures
• Persistence options
• Redis Cluster for scalability

4.7. Content Delivery Networks (CDNs)

• CDN architecture
• Edge locations and point of presence (PoP)
• CDN caching strategies

4.8. Mini-Projects

• Implement an LRU cache from scratch in Go
• Add a caching layer to the key-value store from Chapter 1
• Build a simple CDN simulator

5. Load Balancing and Service Discovery

5.1. Load Balancing Concepts

• Purpose and benefits
• Layer 4 vs. Layer 7 load balancing
• Reverse proxy vs. load balancer

5.2. Load Balancing Algorithms

• Round Robin
• Least Connections
• Least Response Time
• Hash-based
• Weighted algorithms

5.3. Health Checks and Fault Tolerance

• Active vs. passive health checks
• Circuit breaking
• Handling server failures

5.4. Session Persistence

• Sticky sessions
• Session clustering
• Challenges with stateful applications

5.5. Global Server Load Balancing (GSLB)

• DNS-based load balancing
• Anycast
• Geographic load balancing

5.6. Service Discovery

• Client-side vs. server-side discovery
• Service registry
• Service mesh for discovery

5.7. Popular Load Balancing Solutions

• Nginx
• HAProxy
• AWS Elastic Load Balancing

5.8. Mini-Projects

• Implement a simple load balancer in Go
• Create a service discovery mechanism using etcd
• Build a global load balancing simulator

6. Microservices Architecture

6.1. Monolithic vs. Microservices Architecture

• Characteristics and trade-offs
• When to use microservices
• Challenges in adopting microservices

6.2. Designing Microservices

• Domain-Driven Design (DDD) principles
• Bounded contexts
• Service granularity

6.3. Interservice Communication

• Synchronous communication (REST, gRPC)
• Asynchronous communication (Message queues, Event streaming)
• API composition

6.4. API Gateways

• Routing and endpoint consolidation
• Authentication and authorization
• Rate limiting and throttling
• Request/response transformation

6.5. Data Management in Microservices

• Database per service
• Shared database antipattern
• SAGA pattern for distributed transactions

6.6. Deployment Strategies

• Blue-green deployment
• Canary releases
• Rolling updates

6.7. Testing Microservices

• Unit testing
• Integration testing
• Contract testing
• End-to-end testing

6.8. Monitoring and Observability

• Distributed tracing
• Log aggregation
• Metrics and alerting

6.9. Mini-Projects

• Design and implement a simple e-commerce system using microservices
• Build an API gateway for your microservices
• Implement the SAGA pattern for a distributed transaction

7. Containerization and Orchestration

7.1. Docker Fundamentals

• Containers vs. VMs
• Dockerfile best practices
• Docker networking
• Docker volumes and persistence

7.2. Docker Compose

• Multi-container applications
• Environment variables and secrets
• Local development with Docker Compose

7.3. Container Registries

• Docker Hub
• Private registries
• Image tagging strategies

7.4. Kubernetes Architecture

• Control plane components
• Node components
• Kubernetes API

7.5. Kubernetes Resources

• Pods
• Deployments
• Services
• ConfigMaps and Secrets
• Persistent Volumes

7.6. Kubernetes Networking

• Container Network Interface (CNI)
• Services and kube-proxy
• Ingress controllers

7.7. Helm - Kubernetes Package Manager

• Chart structure
• Templates and values
• Helm hooks

7.8. Service Mesh (Istio)

• Traffic management
• Security
• Observability

7.9. Mini-Projects

• Containerize the microservices from Chapter 6
• Deploy the containerized application to Kubernetes
• Implement Istio service mesh for your Kubernetes cluster

8. Distributed Systems

8.1. Fundamentals of Distributed Systems

• Characteristics of distributed systems
• Fallacies of distributed computing
• Design considerations

8.2. CAP Theorem

• Consistency, Availability, Partition tolerance
• CAP theorem implications
• Practical applications of CAP theorem

8.3. Consistency Models

• Strong consistency
• Eventual consistency
• Causal consistency
• Read-your-writes consistency

8.4. Time and Order in Distributed Systems

• Logical clocks
• Vector clocks
• Failure detectors

8.5. Distributed Consensus Algorithms

• Paxos
• Raft
• Practical Byzantine Fault Tolerance (PBFT)

8.6. Leader Election

• Bully algorithm
• Ring algorithm
• ZooKeeper's leader election

8.7. Quorum-based Systems

• Read and write quorums
• Sloppy quorums and hinted handoff

8.8. Gossip Protocols

• Epidemic protocols
• Anti-entropy and rumor mongering

8.9. Mini-Projects

• Implement the Raft consensus algorithm in Go
• Build a distributed key-value store with leader election
• Create a simple gossip protocol for information dissemination

9. Message Queues and Event-Driven Architecture

9.1. Message Queue Fundamentals

• Point-to-point vs. publish-subscribe
• Message persistence
• Delivery guarantees

9.2. Apache Kafka

• Topics and partitions
• Consumer groups
• Kafka Connect and Kafka Streams

9.3. RabbitMQ

• Exchanges and queues
• Routing strategies
• Dead letter queues

9.4. NATS

• Publish-subscribe
• Request-reply
• Queue groups

9.5. Event-Driven Architecture (EDA)

• Events vs. commands
• Event storming
• Benefits and challenges of EDA

9.6. Event Sourcing

• Event store
• Projections
• Snapshotting

9.7. Command Query Responsibility Segregation (CQRS)

• Read and write models
• Eventual consistency in CQRS
• CQRS with and without Event Sourcing

9.8. Stream Processing

• Stream processing vs. batch processing
• Windowing
• Watermarks and late data

9.9. Mini-Projects

• Build an event-driven system using Kafka and Go
• Implement event sourcing for a simple domain
• Create a real-time analytics pipeline using stream processing

10. Data Processing and Analytics

10.1. Batch Processing - MapReduce paradigm - Hadoop ecosystem - Apache Spark basics

10.2. Stream Processing - Apache Flink - Kafka Streams - Real-time vs. near-real-time processing

10.3. Lambda Architecture - Batch layer - Speed layer - Serving layer

10.4. Kappa Architecture - Log-based architecture - Reprocessing strategies

10.5. Data Warehousing - Dimensional modeling - ETL vs. ELT - Data marts

10.6. Data Lakes - Structured, semi-structured, and unstructured data - Data cataloging - Governance and security

10.7. OLAP Systems - Star and snowflake schemas - OLAP operations (drill-down, roll-up, slice, dice) - OLAP vs. OLTP

10.8. Machine Learning in Data Processing - Feature engineering - Model training and evaluation - Online vs. offline learning

10.9. Mini-Projects - Implement a simple MapReduce framework in Go - Build a real-time analytics dashboard using stream processing - Design and implement a data warehouse for an e-commerce system

11. Monitoring, Logging, and Observability

11.1. Monitoring Fundamentals - Metrics types (counters, gauges, histograms) - Push vs. pull monitoring - Alerting and on-call management

11.2. Logging Best Practices - Structured logging - Log levels and filtering - Centralized log management

11.3. Distributed Tracing - OpenTelemetry - Trace context propagation - Sampling strategies

11.4. Metrics Collection and Visualization - Prometheus - Grafana dashboards - InfluxDB and time-series databases

11.5. Log Aggregation and Analysis - ELK stack (Elasticsearch, Logstash, Kibana) - Log parsing and indexing - Full-text search in logs

11.6. Anomaly Detection - Statistical methods - Machine learning-based approaches - Real-time anomaly detection

11.7. Performance Profiling - CPU and memory profiling - Distributed profiling - Continuous profiling in production

11.8. SLIs, SLOs, and SLAs - Defining Service Level Indicators (SLIs) - Setting Service Level Objectives (SLOs) - Managing Service Level Agreements (SLAs)

11.9. Mini-Projects - Set up a comprehensive monitoring system using Prometheus and Grafana - Implement distributed tracing in your microservices architecture - Build an anomaly detection system for application logs

12. Security and Authentication

12.1. Cryptography Basics - Symmetric vs. asymmetric encryption - Hashing and salting - Digital signatures

12.2. Authentication Mechanisms - Password-based authentication - Multi-factor authentication (MFA) - Biometric authentication

12.3. OAuth 2.0 and OpenID Connect - OAuth 2.0 flows - OpenID Connect layers - Implementing an OAuth 2.0 server

12.4. JSON Web Tokens (JWT) - JWT structure - Signing and verifying JWTs - JWT best practices and security considerations

12.5. API Security - API keys - Rate limiting and throttling - Input validation and sanitization

12.6. Transport Layer Security (TLS) - TLS handshake - Certificate authorities and trust chains - Perfect forward secrecy

12.7. Security in Microservices - Service-to-service authentication - Secrets management - Zero trust architecture

12.8. Common Web Vulnerabilities - Cross-Site Scripting (XSS) - SQL Injection - Cross-Site Request Forgery (CSRF) - Security headers and Content Security Policy (CSP)

12.9. Mini-Projects - Implement an authentication service with OAuth 2.0 in Go - Create a JWT-based authentication system for your API - Build a rate limiting middleware for your web services

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13. Scalability Patterns

13.1. Scaling Fundamentals - Vertical vs. horizontal scaling - Scale cube: X, Y, and Z axes - Amdahl's Law and its implications

13.2. Database Sharding - Sharding strategies (range-based, hash-based, directory-based) - Consistent hashing - Challenges in sharded systems (joins, transactions, resharding)

13.3. Read Replicas and Write Concerns - Master-slave replication - Multi-master replication - Read preferences and write concerns

13.4. Caching at Scale - Distributed caching (e.g., Redis Cluster, Memcached) - Cache coherence protocols - Cache invalidation strategies at scale

13.5. Stateless Applications - Benefits of stateless design - Session management in stateless applications - Challenges and solutions for stateful components

13.6. Database Connection Pooling - Connection pool sizing - Handling pool exhaustion - Monitoring and optimizing connection pools

13.7. Asynchronous Processing - Task queues (e.g., Celery, Bull) - Background jobs - Scheduling and prioritization

13.8. Content Delivery Networks (CDNs) at Scale - Global server load balancing - Dynamic content acceleration - CDN purging and invalidation strategies

13.9. Mini-Projects - Implement database sharding for the distributed key-value store - Build a distributed caching layer with consistency protocols - Create a scalable task processing system with prioritization

14. Resilience and Fault Tolerance

14.1. Failure Modes and Effects Analysis (FMEA) - Identifying potential failures - Assessing impact and likelihood - Mitigation strategies

14.2. Circuit Breakers - Circuit breaker states and transitions - Configuring thresholds and timeouts - Hystrix and other circuit breaker implementations

14.3. Retry Mechanisms - Exponential backoff - Jitter - Idempotency in retry scenarios

14.4. Bulkheads - Thread pool isolation - Semaphores - Bulkheads in microservices architectures

14.5. Timeouts and Deadlines - Configuring appropriate timeouts - Propagating deadlines across service calls - Handling timeout cascades

14.6. Graceful Degradation - Fallback mechanisms - Feature toggles for reliability - Partial failures in distributed systems

14.7. Chaos Engineering - Principles of chaos engineering - Designing and running chaos experiments - Tools for chaos engineering (e.g., Chaos Monkey)

14.8. Disaster Recovery - Recovery Point Objective (RPO) and Recovery Time Objective (RTO) - Backup strategies - Disaster recovery drills

14.9. Mini-Projects - Implement a circuit breaker library in Go - Build a resilient microservices architecture with bulkheads and timeouts - Design and run a chaos engineering experiment on your system

15. Performance Optimization

15.1. Performance Testing Fundamentals - Load testing - Stress testing - Soak testing

15.2. Profiling and Benchmarking - CPU profiling - Memory profiling - Go benchmarking tools

15.3. Database Performance Tuning - Index optimization - Query plan analysis - Database-specific optimizations (e.g., PostgreSQL, MySQL)

15.4. Network Optimization - TCP optimizations - HTTP/2 and HTTP/3 - Content compression

15.5. Caching Strategies for Performance - Application-level caching - Database query caching - Fragment caching in web applications

15.6. Concurrency Patterns in Go - Goroutines and channels - Synchronization primitives - Worker pools and fan-out/fan-in patterns

15.7. Memory Management and Garbage Collection - Understanding Go's garbage collector - Memory allocation patterns - Reducing GC pressure

15.8. Front-end Performance Optimization - Critical rendering path optimization - Asset minification and bundling - Lazy loading and code splitting

15.9. Mini-Projects - Optimize the performance of a previous project using profiling tools - Implement a high-performance, concurrent data processing pipeline in Go - Create a performance testing suite for your distributed system

16. Cloud-Native Architecture

16.1. Cloud Computing Models - IaaS, PaaS, SaaS - Serverless computing - Edge computing

16.2. Cloud Design Patterns - Strangler pattern - Sidecar pattern - Ambassador pattern - Circuit breaker pattern in cloud environments

16.3. Serverless Architectures - Function as a Service (FaaS) - Event-driven serverless applications - Serverless frameworks (e.g., AWS SAM, Serverless Framework)

16.4. Container Orchestration in the Cloud - Managed Kubernetes services (e.g., EKS, GKE, AKS) - Serverless containers (e.g., AWS Fargate) - Service mesh in cloud environments

16.5. Cloud Storage Solutions - Object storage (e.g., S3, Google Cloud Storage) - Block storage - File storage - Data lakes in the cloud

16.6. Infrastructure as Code (IaC) - Terraform - CloudFormation - Pulumi

16.7. Cloud Monitoring and Observability - Cloud-native monitoring solutions - Distributed tracing in cloud environments - Cost monitoring and optimization

16.8. Multi-Cloud and Hybrid Cloud Strategies - Designing for portability - Inter-cloud networking - Multi-cloud management tools

16.9. Mini-Projects - Deploy a serverless application using AWS Lambda and Go - Create a multi-region, highly available architecture on a cloud provider - Implement Infrastructure as Code for your entire system using Terraform

17. Graph Databases and Recommendation Systems

17.1. Graph Database Fundamentals - Property graphs - Labeled graphs - Graph database vs. relational database

17.2. Graph Data Modeling - Nodes, relationships, and properties - Modeling complex domains as graphs - Best practices in graph schema design

17.3. Graph Querying - Cypher query language (Neo4j) - Gremlin query language - GraphQL for graph databases

17.4. Graph Algorithms - Pathfinding algorithms (e.g., Dijkstra's, A*) - Centrality algorithms - Community detection algorithms

17.5. Recommendation System Architectures - Content-based filtering - Collaborative filtering - Hybrid recommendation systems

17.6. Building Recommendation Engines - User-item interaction matrices - Matrix factorization techniques - Deep learning in recommendation systems

17.7. Scaling Recommendation Systems - Offline vs. online computation - Approximate nearest neighbors (ANN) - Distributed graph processing

17.8. Evaluating Recommendation Systems - Offline evaluation metrics - A/B testing for recommendations - Handling cold start problems

17.9. Mini-Projects - Implement a social network backend using a graph database - Build a simple recommendation system using collaborative filtering - Create a real-time recommendation engine with Neo4j and Go

18. Machine Learning Systems Design

18.1. ML System Architecture - Training pipelines - Inference systems - Online learning systems

18.2. Feature Engineering and Selection - Feature stores - Automated feature engineering - Feature selection techniques

18.3. Model Deployment Strategies - Model serialization - A/B testing for ML models - Canary deployments for ML

18.4. ML Model Serving - Model servers (e.g., TensorFlow Serving, Seldon Core) - Batch vs. real-time inference - Hardware acceleration for inference (GPUs, TPUs)

18.5. ML Pipelines - Data ingestion and preprocessing - Model training and evaluation - Continuous training and deployment

18.6. ML Monitoring and Observability - Model performance monitoring - Data drift detection - Explainability and interpretability

18.7. Scaling ML Systems - Distributed training - Parameter servers - Federated learning

18.8. MLOps - Version control for ML (e.g., DVC) - Experiment tracking - Model registry and lifecycle management

18.9. Mini-Projects - Design and implement an ML model serving system in Go - Build an end-to-end ML pipeline with continuous training - Create a real-time anomaly detection system using streaming data

19. Blockchain and Distributed Ledgers

19.1. Blockchain Fundamentals - Distributed ledger technology - Consensus mechanisms (PoW, PoS, DPoS) - Public vs. private blockchains

19.2. Cryptography in Blockchain - Hash functions - Digital signatures - Merkle trees

19.3. Smart Contracts - Solidity programming - Smart contract security - Gas optimization

19.4. Blockchain Scalability - Sharding - Layer 2 solutions (e.g., Lightning Network, Plasma) - Sidechains

19.5. Blockchain Interoperability - Cross-chain communication protocols - Atomic swaps - Blockchain bridges

19.6. Decentralized Applications (DApps) - Web3 architecture - Decentralized storage (e.g., IPFS) - Decentralized identity

19.7. Blockchain in Enterprise - Hyperledger frameworks - Consortium blockchains - Integration with existing systems

19.8. Blockchain Security and Privacy - 51% attacks - Sybil attacks - Zero-knowledge proofs

19.9. Mini-Projects - Implement a simple blockchain in Go - Create a basic smart contract and deploy it on a test network - Build a decentralized application (DApp) with a blockchain backend

20. Edge Computing and IoT

20.1. Edge Computing Architecture - Edge devices and gateways - Fog computing - Mobile edge computing (MEC)

20.2. IoT Protocols - MQTT - CoAP - LoRaWAN

20.3. Data Collection and Preprocessing at the Edge - Sensor data acquisition - Edge analytics - Data filtering and aggregation

20.4. Edge-Cloud Coordination - Data synchronization strategies - Offline-first applications - Edge-triggered cloud functions

20.5. IoT Security - Device authentication - Secure communication protocols - Over-the-air (OTA) updates

20.6. IoT Data Management - Time-series databases for IoT - Data lakes for IoT - IoT data governance

20.7. Edge AI and Machine Learning - Model compression techniques - Federated learning in IoT - Tiny ML for resource-constrained devices

20.8. IoT Platforms and Middleware - AWS IoT - Azure IoT - Open-source IoT platforms (e.g., ThingsBoard)

20.9. Mini-Projects - Build an IoT data collection and processing system using MQTT - Implement an edge computing solution for real-time analytics - Create a secure IoT device management system

Conclusion

I wanna make this as a long term goal to cover everything in this by the time I reach Principal Engineer position.