Here's a breakdown of the key topics you should focus on:
1. Scalability
- Vertical vs. Horizontal Scaling: Understand how to scale systems by adding more resources to a single node (vertical) versus adding more nodes (horizontal).
- Load Balancing: Learn about different load-balancing strategies (e.g., round-robin, least connections) and tools (e.g., Nginx, HAProxy).
2. High Availability and Reliability
- Redundancy: Implementing redundancy in systems to prevent single points of failure.
- Failover Strategies: Automatic switching to a standby database, server, or network.
- Replication: Database replication strategies like master-slave, master-master, etc.
3. Distributed Systems
- Consistency Models: Learn about CAP theorem, eventual consistency, strong consistency, and how they apply to distributed systems.
- Data Partitioning: Sharding and partitioning techniques for handling large datasets.
- Consensus Algorithms: Understand Paxos, Raft, and Byzantine Fault Tolerance for achieving consensus in distributed systems.
- Eventual Consistency: Understanding when and how to use eventual consistency in distributed databases and systems.
4. Database Design
- Normalization and Denormalization: Balancing normalization for consistency and denormalization for performance.
- Indexing: Understand B-trees, hash indexes, and full-text indexing to optimize database performance.
- NoSQL Databases: Learn about key-value stores, document stores, column-family stores, and graph databases.
- Data Modeling: Designing schemas that can efficiently store and retrieve data.
5. Caching
- Cache Invalidation Strategies: Know when and how to invalidate caches (e.g., LRU, LFU).
- Distributed Caches: Tools like Redis and Memcached, and understanding their role in reducing database load.
6. Message Queues and Asynchronous Processing
- Message Brokers: Kafka, RabbitMQ, and their use cases in decoupling services.
- Event-Driven Architecture: Designing systems that react to events in real-time.
- Pub/Sub Model: Implementing systems that need to notify multiple subscribers of events.
7. Security
- Authentication and Authorization: OAuth, JWT, and RBAC (Role-Based Access Control).
- Encryption: SSL/TLS, encryption at rest, and in transit.
- Security Best Practices: Preventing common vulnerabilities (e.g., SQL injection, XSS).
8. API Design
- REST vs. GraphQL vs. gRPC: Understand the strengths and weaknesses of different API paradigms.
- Versioning: How to handle API versioning gracefully.
- Rate Limiting: Protecting your API from abuse and ensuring fair usage.
9. Monitoring and Logging
- Observability: Implementing logging, metrics, and tracing to monitor and understand system behavior.
- Alerting: Setting up alerts for critical issues before they impact users.
- Distributed Tracing: Tools like Jaeger, Zipkin for tracing requests across microservices.
10. Microservices Architecture
- Service Discovery: Tools like Consul, Eureka, and how they help in identifying microservices in a distributed system.
- Circuit Breakers: Implementing circuit breakers to prevent cascading failures.
- Data Management: Managing data consistency across microservices.
11. Networking
- Protocols: HTTP/HTTPS, TCP/IP, WebSockets, and how they impact system design.
- DNS: Understanding how DNS works and how to optimize it for your services.
- CDN: How Content Delivery Networks work and when to use them.
12. DevOps and CI/CD
- Infrastructure as Code: Tools like Terraform, Ansible, and how they enable scalable infrastructure management.
- CI/CD Pipelines: Jenkins, GitLab CI, and automating build, test, and deployment processes.
- Containerization and Orchestration: Docker, Kubernetes, and managing containerized applications.
13. Cloud Architecture
- Cloud Providers: AWS, GCP, Azure, and understanding their core services.
- Serverless Computing: Lambda, Cloud Functions, and when to use serverless architectures.
- Multi-Cloud and Hybrid Cloud: Designing systems that work across multiple cloud providers or integrate on-premise and cloud resources.
14. Trade-offs and Decision Making
- Latency vs. Throughput: Understand how to optimize for one while managing trade-offs with the other.
- Cost vs. Performance: Balancing the cost of resources with system performance.
- Build vs. Buy: Deciding when to build in-house solutions versus using third-party services.
15. Case Studies and Practical Application
- Study real-world architectures from companies like Netflix, Uber, and Amazon.
- Design systems from scratch, considering the above topics and making decisions based on specific requirements and constraints.
Mastering these topics will significantly elevate your system design skills and help you progress to a very senior level in software development.
