Full-Stack & Distributed Systems Architecture
Master software engineering at production scale. Move beyond basic CRUD apps to event-driven architectures, write-ahead logging databases, Redis caching topologies, and zero-downtime distributed deployments.
Networking Protocols, Memory & Modern Backend
HTTP/2 & HTTP/3 QUIC, TCP handshakes, TLS 1.3, asynchronous I/O event loops, and relational data modeling.
What you're experiencing: "Event Loops & Concurrency"
You thought web development was just sending JSON, until you encounter connection pool exhaustion and database locks. You realize performance is an engineering discipline.
You are shifting from writing sequential scripts to architecting asynchronous server systems capable of servicing 100,000 concurrent sockets.
- Performing N+1 database queries inside loops instead of batch fetching or JOIN queries.
- Not setting connection pool timeouts and socket read deadlines, causing thread worker starvation.
Computer Networking: TCP, HTTP/2, QUIC & TLS 1.3
Socket lifecycle, TCP flow control, head-of-line blocking, and cryptographic TLS handshakes.
Relational Database Internals & Indexing
B-Trees, Write-Ahead Logs (WAL), MVCC concurrency control, and query execution plans in PostgreSQL.
Capstone: Distributed Token-Bucket Rate Limiter with Redis & Lua
Implement a zero-race-condition distributed API rate limiter supporting Sliding Window Counter and Token Bucket algorithms using atomic Redis Lua scripts with microsecond latency guarantees.
Distributed Systems & Data at Scale
Distributed consensus (Raft/Paxos), event-driven messaging (Kafka), caching topologies, and partition tolerance.
What you're experiencing: "Eventual Consistency & Raft Consensus"
You stop thinking in terms of single-server guarantees. You realize network partitions, clock skew, and partial server crashes are inevitable realities.
You have elevated from developer to system architect. You design systems where any node can fail without bringing down the platform.
- Assuming wall-clock time (`Date.now()`) is monotonically synchronized across different servers.
- Not designing consumers with idempotency, causing duplicate charge/processing during message retries.
Distributed Architecture & Consensus (Raft/Paxos)
CAP theorem, linearizability, vector clocks, leader election, and distributed consensus.
Capstone: Event-Driven Order Processing Engine with Transactional Outbox
Build an event-driven microservice system with PostgreSQL and Apache Kafka. Implement the Transactional Outbox Pattern with Debezium CDC to guarantee exactly-once event delivery, inventory reservations, and payment processing.