·8 min read
Scalability: Scaling Up vs Scaling Out
My app fell over at 5,000 users and my first instinct was to buy a bigger server. Here's what I learned about vertical vs horizontal scaling and why statelessness is the real unlock.
→System Design Fundamentals
Learn the building blocks of designing large-scale systems. From scalability and load balancing to caching, databases, consistency trade-offs, fault tolerance, and the cross-cutting concerns that keep production systems alive. Each post explains the why before the how, with concrete examples and the trade-offs that actually come up in interviews and on the job.
Fundamentals
Core ideas that frame every design decision: how systems scale and how we measure them
Traffic and Caching
Distributing requests across machines and serving data closer to the user
·8 min read
Load Balancing: How One Address Feeds Many Servers
Once I had ten servers, I had a new problem: how does traffic reach all of them from one URL? Here's what I learned about load balancers, algorithms, L4 vs L7, and health checks.
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·8 min read
Caching: Strategies, Eviction, and the Hard Part
Caching made my app fast and then served stale data to thousands of users. Here's what I learned about cache layers, write strategies, eviction, and why invalidation is genuinely hard.
→Data and Consistency
Storing data at scale and reasoning about the consistency vs availability trade-off
·9 min read
Databases at Scale: SQL vs NoSQL, Replication, and Sharding
I picked NoSQL because it sounded scalable, then desperately missed joins. Here's what I learned about SQL vs NoSQL, replication, sharding, and indexing, and how to choose without the hype.
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·8 min read
The CAP Theorem: Consistency, Availability, and Reality
I thought CAP meant 'pick two of three.' That framing confused me for years. Here's what the CAP theorem actually says, what consistency models mean, and why PACELC completes the picture.
→Resilience and Async
Surviving failure and decoupling work with queues and messaging
·9 min read
Reliability and Fault Tolerance: Designing for Failure
One slow third-party API took my whole app down with it. Here's what I learned about redundancy, graceful degradation, and the resilience patterns that stop one failure from becoming an outage.
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·8 min read
Asynchronous Processing and Messaging: Decoupling with Queues
My signup endpoint took 8 seconds because it sent the welcome email inline. Here's what I learned about message queues, pub/sub, delivery guarantees, and backpressure.
→Communication
How services talk to each other and to clients
Operations
Cross-cutting concerns that keep systems healthy in production
