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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