System Design Recommender

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Recommended Components

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System Design Reference

Component What It Solves Caveats Examples
Load Balancer Distributes traffic across workers, improves reliability and availability. Single point of failure, adds complexity. Nginx, HAProxy, AWS ELB
SQL Database Strong ACID properties, structured data, complex queries. Limited scalability, schema management. MySQL, PostgreSQL, MS SQL Server
NoSQL Database Flexible schema, horizontal scalability, high performance. Eventual consistency, limited transaction support. MongoDB, Cassandra, DynamoDB
Cache Reduces latency, decreases load on databases. Cache consistency issues, potential for stale data. Redis, Memcached
Message Broker Facilitates message exchange, supports multiple patterns. Bottleneck potential, delivery guarantees. Apache Kafka, RabbitMQ, ActiveMQ
API Gateway Single entry point, manages authentication and routing. Can become a bottleneck, adds latency. Kong, Apigee, AWS API Gateway
Service Registry Tracks services and their instances. High availability required, consistency issues. Consul, Eureka, Zookeeper
Powers of Two Table
Power Approx Value (Bytes) Bytes
10 1 thousand 1 KB
16 65 thousand 64 KB
20 1 million 1 MB
30 1 billion 1 GB
32 4 billion 4 GB
40 1 trillion 1 TB
50 1 quadrillion 1 PB
Latency Comparison Numbers
  • L1 cache can be accessed 14x faster than L2 cache.
  • Main memory access is 20x slower than L2, 200x slower than L1.
  • Read sequentially from main memory at 4 GB/sec.
  • Read randomly/sequentially from SSD at ~ 1 GB/sec.
  • Read sequentially from disk at 30 MB/sec.
  • Send data over 1 Gbps network at 100 MB/sec.
  • Round-trips within a data center at 2,000 trips/sec.
  • World-wide round trips at 6-7 trips/sec.
Request Calculations
  • 2.5 million seconds per month
  • 1 million reqs/month → 0.4 reqs/sec
  • 2.6 million reqs/month → 1 reqs/sec
  • 5 million reqs/month → 2 reqs/sec
  • 10 million reqs/month → 4 reqs/sec
  • 100 million reqs/month → 40 reqs/sec
  • 1 billion reqs/month → 400 reqs/sec
Common System Design Patterns

Microservices architecture is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API.

Benefits: Independent deployment, technological diversity, fault isolation.

Challenges: Distributed system complexity, eventual consistency, network unreliability.

Event-driven architecture promotes the production, detection, consumption of, and reaction to events. This architectural pattern can be applied to systems that process events asynchronously.

Benefits: Loose coupling, better scalability, improved responsiveness.

Challenges: Complex debugging, maintaining event order, event versioning.

CQRS is a pattern that separates read and update operations for a data store. This pattern can maximize performance, scalability, and security.

Benefits: Optimized read and write operations, scalability, independent scaling.

Challenges: Increased complexity, eventual consistency, multiple models to maintain.

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