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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