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🏆 Ultimate System Design & Cloud Interview Cheat Sheet 🚀

#systemdesign #cloud #programming #career

System design and cloud architecture interviews are critical for software engineers, backend developers, and cloud architects. This cheat sheet covers system design fundamentals, cloud concepts, scalability, database strategies, and real-world interview questions to help you ace your next interview. 💡

🏗️ System Design Fundamentals

Key Concepts

  • Scalability: Ensuring a system can handle increasing loads efficiently.
  • Availability: Ensuring uptime and reliability with failover mechanisms.
  • Latency vs Throughput: Response time vs number of processed requests.
  • Consistency vs Partition Tolerance (CAP Theorem):
    • CP (Consistency, Partition Tolerance): Strong consistency (e.g., traditional RDBMS).
    • AP (Availability, Partition Tolerance): Eventual consistency (e.g., NoSQL, DynamoDB).
    • CA (Consistency, Availability): Only works if there's no network partition.
  • Horizontal vs Vertical Scaling:
    • Horizontal Scaling: Adding more machines (e.g., load balancing).
    • Vertical Scaling: Increasing CPU, RAM on a single machine.
  • Load Balancing: Distributing traffic across multiple servers to ensure reliability.
  • Caching: Improving performance by storing frequently accessed data (Redis, Memcached).
  • Rate Limiting: Controlling API access to prevent abuse (Token Bucket, Leaky Bucket).

Common System Design Interview Questions

  1. Design Twitter (Feed System, Rate Limiting, Scaling)
  2. Design a URL Shortener like Bit.ly
  3. Design an E-commerce Checkout System
  4. Design a File Sharing System (Google Drive, Dropbox)
  5. Design a Distributed Messaging Queue (Kafka, RabbitMQ)

🌩️ Cloud Architecture & Distributed Systems

Cloud Providers & Services

  • AWS, Azure, Google Cloud: Leading cloud providers.
  • Compute Services:
    • AWS EC2, Lambda | GCP Compute Engine, Cloud Functions | Azure VMs, Functions.
  • Storage Services:
    • AWS S3, EBS | GCP Cloud Storage, Persistent Disk | Azure Blob Storage.
  • Database Services:
    • AWS RDS, DynamoDB | GCP Firestore, Cloud SQL | Azure CosmosDB, SQL Database.
  • Networking Services:
    • AWS VPC, CloudFront | GCP VPC, Load Balancer | Azure Virtual Network.

Cloud Design Patterns

  • Microservices: Breaking down applications into independent, loosely coupled services.
  • Serverless Architecture: Using cloud functions to run event-driven workloads without managing servers.
  • Event-Driven Architecture: Services communicate asynchronously via events (Kafka, SNS, Pub/Sub).
  • Multi-Region Deployment: Distributing applications across multiple regions for redundancy.
  • Service Mesh: Managing communication between microservices (Istio, Linkerd).

Database Scaling & Optimization

  • Sharding: Splitting a database into smaller chunks distributed across multiple servers.
  • Replication: Copying data across multiple nodes for high availability.
  • Indexing: Speeding up queries with efficient lookups (B-Trees, Hash Indexing).
  • SQL vs NoSQL:
    • SQL (Relational): Strong consistency (e.g., PostgreSQL, MySQL, SQL Server).
    • NoSQL (Non-Relational): Scales horizontally (e.g., MongoDB, DynamoDB, Cassandra).

Caching Strategies

  • Write-Through Cache: Writes to both database and cache simultaneously.
  • Write-Back Cache: Writes to cache first, then periodically updates the database.
  • Cache Invalidation: Removing stale cache entries (Least Recently Used - LRU, Time-To-Live - TTL).

🛡️ Security & Reliability

Common Cloud Security Concerns

  • Authentication & Authorization:
    • JWT, OAuth, OpenID, API Gateway authentication.
  • Data Encryption:
    • At rest (AWS KMS, GCP Cloud KMS), In transit (TLS, SSL).
  • DDoS Protection:
    • Cloudflare, AWS Shield, GCP Armor.
  • Secrets Management:
    • HashiCorp Vault, AWS Secrets Manager, GCP Secret Manager.
  • Monitoring & Logging:
    • AWS CloudWatch, GCP Stackdriver, Azure Monitor.

High Availability & Fault Tolerance

  • Failover Mechanisms: Redirecting traffic when a failure occurs.
  • Auto-Scaling: Adjusting resources dynamically to handle changing loads.
  • Distributed Consensus Algorithms: Ensuring consistency across distributed systems (Paxos, Raft).
  • Chaos Engineering: Introducing failures to test system resilience (Netflix Chaos Monkey).

🔥 Final Tips for System Design & Cloud Interviews

✅ Understand trade-offs between availability, consistency, and performance.
✅ Use diagrams to explain architecture decisions clearly.
✅ Think in terms of scalability and how your design handles increased load.
✅ Discuss monitoring and failure recovery – interviewers love resilience planning.
✅ Stay updated with cloud technologies – AWS, GCP, Azure keep evolving.

🚀 Good luck with your system design and cloud interviews! You've got this! 🔥

💬 What’s the toughest system design question you’ve faced? Let’s discuss in the comments!

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