System Design Patterns
Master the art of building scalable systems through essential design patterns. Explore 12 patterns and architectural best practices.
Load Balancer
Distribute incoming network traffic across multiple servers to ensure no single server becomes a bottleneck.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Types:
- Vertical scaling (scaling up)
- Horizontal scaling (scaling out)
Considerations:
- Load balancing
- Statelessness
- Data partitioning