System Design & Distributed Systems: The Complete Guide
Consensus algorithms, caching strategies, database internals, networking protocols, and architecture patterns for large-scale systems.
Networking & Protocols
How data moves across the wire — from TCP/UDP to HTTP evolution and DNS.
What Happens When You Type a URL: The Complete Production Guide
Request lifecycle from DNS through TCP, TLS, CDN, and load balancing — with production latency debugging at every layer.
TCP vs UDP vs QUIC: Protocol Selection Under Production Load
What head-of-line blocking costs, how QUIC solves it, and how to choose the right transport for real-time and API workloads.
HTTP/1.1 vs HTTP/2 vs HTTP/3: The Protocol Evolution Guide
How HTTP evolved from sequential text to multiplexed binary streams over QUIC. What each version solves and when to upgrade.
DNS Records: The Complete Production Guide for Backend Engineers
Every DNS record type for production: A, CNAME, MX, TXT, CAA, SRV. TTL failover math, SPF/DKIM/DMARC, GeoDNS, and DNSSEC.
Data Infrastructure
Database internals, indexing, caching, and migrations at scale.
PostgreSQL Query Planner Internals: From EXPLAIN to Expert Tuning
How PostgreSQL's query optimizer decides, why it gets it wrong, and how to fix it with statistics targets and covering indexes.
Database Indexing Strategies: B-Trees, GIN, GiST, and Production Tuning
B-tree internals, composite index ordering, GIN for full-text search, partial indexes, and preventing index bloat in production.
Caching Strategies at Scale
Four caching patterns (cache-aside, write-through, write-behind, read-through), plus Go code for stampede prevention, multi-tier caching, and event-based invalidation.
Scaling Redis for High-Throughput Systems
Scale Redis: cluster sharding, hot key replication, connection pooling, pipelining, and eviction tuning for high-throughput workloads.
Zero-Downtime Database Migrations at Scale
Schema migrations on billion-row tables without downtime: expand-contract, pg_repack, gh-ost, blue-green migrations, and rollbacks.
Distributed Systems Patterns
Consensus, rate limiting, messaging, idempotency, and microservice architecture.
Understanding Raft Consensus: The Algorithm That Keeps Your Database Honest
How Raft works in production: leader election, log replication, safety guarantees, and failure modes in etcd and CockroachDB.
Consistent Hashing: The Algorithm Behind Scalable Distributed Systems
Adding one cache server shouldn't invalidate every key. Consistent hashing with virtual nodes and bounded loads — full Go and Java implementations.
Rate Limiter Algorithms: Token Bucket vs Sliding Window
Five rate limiting algorithms, their trade-offs, how to distribute them across a fleet, and client-side backoff that works.
Distributed Rate Limiting at Scale: The Probabilistic Drop Architecture
Probabilistic drop rate limiting: uncoordinated enforcement that takes Redis off the request path, with no coordination per request.
Idempotency Patterns: Building Retry-Safe Distributed Systems
Why exactly-once is a myth, and how idempotency keys, database constraints, and the outbox pattern make retries safe in Go and Java.
Kafka vs RabbitMQ vs NATS vs SQS: Choosing the Right Message Broker
Kafka vs RabbitMQ vs NATS vs SQS: delivery semantics, ordering, throughput, ops complexity, and a decision framework with Go code.
Microservices Architecture: From Monolith to Production-Ready Services
When to decompose a monolith, how to define boundaries, and the patterns that work: API gateways, sagas, and event-driven comms.
REST vs gRPC vs GraphQL: A Production Decision Guide
REST, gRPC, and GraphQL solve different client problems. A decision guide for when each one wins, and what running all three side by side costs.
