EP160: Top 20 System Design Concepts You Should Know

This article outlines 20 essential system design concepts crucial for building scalable, reliable, and efficient distributed systems. It covers foundational principles like load balancing, caching, and databases, as well as advanced topics such as consensus algorithms and microservices. The guide serves as a comprehensive reference for engineers preparing for system design interviews or real-world implementations.

Core Technical Concepts/Technologies Discussed

• Load Balancing
• Caching (CDN, Redis, Memcached)
• Databases (SQL, NoSQL, Sharding, Replication)
• Proxies (Forward/Reverse)
• CAP Theorem
• ACID vs. BASE
• Consistent Hashing
• Leader Election
• Message Queues (Kafka, RabbitMQ)
• Microservices
• Rate Limiting
• Distributed File Systems (HDFS)
• Peer-to-Peer Networks
• Polling vs. Webhooks
• Heartbeat Mechanism
• Checksum
• API Gateway
• SLA, SLO, SLI
• Redundancy & Replication
• Consensus Algorithms (Paxos, Raft)

Main Points

• Load Balancing: Distributes traffic across servers to optimize resource use (e.g., Round Robin, Least Connections).
• Caching: Reduces latency by storing frequently accessed data (CDNs for static content, Redis/Memcached for dynamic data).
• Database Scaling: Vertical (upgrading hardware) vs. horizontal (sharding) scaling; replication ensures high availability.
• CAP Theorem: Trade-offs between Consistency, Availability, and Partition Tolerance in distributed systems.
• Microservices: Decouples functionality into independent services, improving scalability but adding complexity.
• Rate Limiting: Protects systems from abuse (e.g., token bucket, leaky bucket algorithms).
• Consensus Algorithms: Paxos/Raft ensure agreement in distributed systems despite failures.

Technical Specifications & Examples

• Consistent Hashing: Minimizes data redistribution when nodes are added/removed (used in DynamoDB, Cassandra).
• Leader Election: ZooKeeper’s Zab protocol or Raft for coordinating distributed systems.
• Message Queues: Kafka’s pub-sub model vs. RabbitMQ’s queue-based messaging.

Key Takeaways

1. Trade-offs are fundamental: CAP theorem and ACID vs. BASE highlight the need to prioritize based on use cases.
2. Scalability requires planning: Techniques like sharding, caching, and load balancing are critical for growth.
3. Redundancy ensures reliability: Replication and heartbeat mechanisms prevent single points of failure.
4. Microservices offer flexibility: But require robust API gateways and monitoring (SLOs/SLIs).
5. Real-world systems combine multiple concepts: E.g., Netflix uses CDNs, microservices, and rate limiting.

Limitations & Further Exploration

• Some concepts (e.g., Paxos) are complex to implement and may require deeper study.
• Emerging technologies (e.g., serverless, service mesh) could expand this list.
• Practical implementation details (e.g., tuning Redis eviction policies) are beyond the scope.