Distributed Systems

The dangerous assumptions every distributed system must unlearn — why networks fail partially, clocks lie, and 'it worked on one machine' stops being true.

What CAP really forces you to choose, and the spectrum from eventual to strict serializability — so you pick a consistency model on purpose, not by accident.

Why wall-clock timestamps can't order events across machines, and how logical clocks, version vectors, and anti-entropy reason about order instead.

How nodes agree on one value despite failures — two-phase commit, Raft, Paxos, and the ownership-election primitives that make leaders safe.

What scaling actually means — scale up vs out vs down, the twin principles of replication and optimization, statelessness, and why Amdahl's law caps your gains.

How a load balancer spreads requests across stateless replicas — Layer 4 vs Layer 7, distribution policies, health checks, elastic autoscaling, and the cascading-failure defenses that keep it all standing.

How caching buys capacity by not asking the database — cache-aside vs read/write-through, TTLs and eviction, hit-rate economics, and HTTP/CDN caching at the edge.

Scaling the data tier — read replicas, partitioning and sharding, leader-follower vs leaderless replication, NoSQL data models, and the consistency knobs real engines expose.

Decoupling producers from consumers with queues and logs — persistence and delivery guarantees, pub/sub, competing consumers, dead-letter queues, and the event-log shift Kafka makes.

The three co-located container patterns — sidecar augments, ambassador brokers, adapter normalizes — that turn one machine's containers into reusable distributed-system building blocks.

The three multi-node serving topologies — replicate to scale requests, shard to scale data, scatter/gather to scale time — plus the readiness, hot-sharding, and straggler realities that govern them.

Patterns for short-lived, parallel data processing — the work queue, the event-driven coordination primitives (copier, filter, splitter, sharder, merger), and the coordinated join/reduce that produces aggregates.