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- The System Design Fundamentals HandbookThe load-bearing ideas behind every distributed system — the CAP theorem and consistency models, concurrency and locking, partitioning and replication, and consensus and coordination — each tied to a real worked design you can study interactively.Read →
- CAP Theorem & Consistency ModelsWhat a distributed system can promise when the network splits — CP vs AP, why "CA" is a myth, PACELC, and the full spectrum from linearizable to eventual consistency. Part of System Design Fundamentals.Read →
- Partitioning, Sharding & ReplicationHow one dataset becomes many — range vs hash partitioning, consistent hashing and virtual nodes, hot partitions, replication topologies, replication lag, and quorums. Part of System Design Fundamentals.Read →
- Consensus, Transactions & CoordinationHow nodes that can crash still agree on one truth — majority quorums, Raft and Paxos, leader election, two-phase commit vs the saga pattern, and idempotency for exactly-once effects. Part of System Design Fundamentals.Read →
- The Kubernetes HandbookThe one idea under all the YAML — declare desired state, and a control loop makes reality match. Covers the orchestration problem, pods, deployments and replicasets, services and networking, the reconciliation loop and self-healing, the scheduler, config/secrets and health probes, autoscaling, and when you actually need Kubernetes.Read →
- The Observability HandbookSeeing inside production — monitoring vs observability, the three pillars (metrics, logs, traces) and what each answers, structured logging, metric types and the cardinality trap, distributed tracing, the golden signals and SLIs/SLOs/error budgets, alerting on symptoms not causes, and correlating all three during an incident.Read →