System Design Patterns
Here are some system design patterns that are essential for building scalable and efficient systems and frequently come up in system design interviews.
Load Balancer
Distribute incoming network traffic across multiple servers to ensure no single server becomes a bottleneck.
Key Concepts:
- algorithms: Round-robin, Least connections, IP hash
- types: Hardware load balancers, Software load balancers (e.g., HAProxy, Nginx)
Caching
Reduce latency and increase throughput by storing copies of frequently accessed data.
Key Concepts:
- types: In-memory caches (e.g., Redis, Memcached), CDN caches
- strategies: Cache-aside, Write-through, Write-back
Database Sharding
Split a large database into smaller, more manageable pieces to improve performance and scalability.
Key Concepts:
- methods: Horizontal partitioning (by rows), Vertical partitioning (by columns)
- challenges: Data distribution, Rebalancing shards, Handling joins across shards
Replication
Improve data availability and fault tolerance by duplicating data across multiple servers.
Key Concepts:
- types: Master-slave replication, Multi-master replication
- consistencyModels: Strong consistency, Eventual consistency
Queueing
Decouple components to handle a high volume of requests asynchronously and ensure smooth processing.
Key Concepts:
- examples: Message queues (e.g., RabbitMQ, Kafka), Task queues (e.g., Celery)
- patterns: Producer-consumer, Publish-subscribe
Microservices
Decompose a monolithic application into smaller, independent services that can be developed, deployed, and scaled independently.
Key Concepts:
- advantages: Scalability, Maintainability, Fault isolation
- challenges: Service communication, Data consistency, Monitoring
Content Delivery Network (CDN)
Reduce latency and improve load times by serving content from edge locations closer to users.
Key Concepts:
- examples: Akamai, Cloudflare
- benefits: Reduced server load, Improved performance, Scalability
Event-Driven Architecture
Build systems that react to events asynchronously to enhance scalability and decoupling.
Key Concepts:
- examples: Event sourcing, Pub/sub messaging (e.g., Kafka, AWS SNS)
- advantages: Flexibility, Real-time processing, Decoupled components
Rate Limiting
Control the rate at which requests are handled to prevent abuse and ensure fair resource usage.
Key Concepts:
- algorithms: Token bucket, Leaky bucket
- applications: API rate limiting, DDoS protection
Circuit Breaker
Prevent cascading failures in distributed systems by stopping the flow of requests to a failing service.
Key Concepts:
- examples: Hystrix, Resilience4j
- states: Closed, Open, Half-open
- benefits: Increased system resilience, Fault tolerance
Monitoring and Logging
Track the health and performance of systems and applications for proactive maintenance and troubleshooting.
Key Concepts:
- examples: Prometheus, ELK stack (Elasticsearch, Logstash, Kibana)
- components: Metrics collection, Log aggregation, Alerting
Scalability Patterns
Ensure the system can handle increased load by adding resources.
Key Concepts:
- types: Vertical scaling (scaling up), Horizontal scaling (scaling out)
- considerations: Load balancing, Statelessness, Data partitioning