System Design
Architecture patterns, scalable system design, and distributed systems for building robust applications
Choosing the Right Load Balancer: A Practical Guide
A practical guide to understanding different types of load balancers (Layer 4 vs. Layer 7) and algorithms (Round Robin, Least Connections) to improve system scalability and reliability.
A Guide to Designing a Scalable Caching Strategy
Learn how to design and implement a scalable caching strategy using patterns like cache-aside, read-through, and write-through to improve application performance.
Designing for Failure: A Guide to Building Fault-Tolerant Systems
A practical guide to designing fault-tolerant systems using techniques like redundancy, failover, and graceful degradation to ensure high availability.
Introduction to Message Queues for Asynchronous Communication
An introduction to message queues, explaining how they enable asynchronous communication in distributed systems to improve reliability and scalability.
An Introduction to Observability: Logs, Metrics, and Traces
An introduction to the three pillars of observability—logs, metrics, and traces—and how they work together to provide deep insights into system behavior.
Securing Microservices: A Guide to API Gateways and Service Meshes
Learn how to secure microservices using API Gateways for centralized edge security and Service Meshes for managing inter-service communication.
Understanding Database Replication: A Step-by-Step Guide
A step-by-step guide to understanding database replication, including primary-secondary and primary-primary patterns, to improve the reliability and performance of your system.
Frequently Asked Questions
What are the fundamental principles of scalable system design?
Core system design principles include: stateless application services, horizontal scaling, asynchronous background processing, multi-tier caching (edge, memory, database), database read replicas, and graceful degradation under load spikes.
What is the CAP theorem and how does it apply to modern systems?
The CAP theorem states that a distributed data store can guarantee at most two out of three properties: Consistency, Availability, and Partition Tolerance. In the presence of real-world network partitions, architects must choose between returning errors for consistency or stale data for availability.
How does rate limiting protect distributed web services?
Rate limiting restricts the volume of requests a client can submit within a time window (using algorithms like Token Bucket or Leaky Bucket), protecting downstream services from denial-of-service spikes, brute-force attacks, and noisy neighbor resource exhaustion.