Distributed Systems
8 guidesKafka delivery, idempotency, transactions, consensus, and trace propagation.
Distributed Systems Foundations: Consistency, Retries, and Delivery Semantics
Separate consistency guarantees from delivery guarantees and design for the partial failures that make retries necessary.
Kafka Consumer Groups: Architecture & Common Pitfalls
Consumer groups are the mechanism that turns Kafka partitions into scalable, failure-tolerant work units. Learn how rebalances, offsets, and lag interact under load before you ship your first serious pipeline.
Idempotency and the Transactional Outbox: Making Retries Safe
Learn why network retries create duplicate effects and how idempotency keys and an outbox close common reliability gaps.
Distributed Systems Advanced: Rebalancing, Sagas, Consensus, and Trace Context
Design around Kafka group churn, payment idempotency, coordination limits, sagas, CQRS, and context propagation across async boundaries.
Consensus and Observability Advanced: Quorums, Split Brain, and Trace Context
Reason about leader election across partitions and preserve causal context across Kafka, thread pools, and service boundaries.
Event-Time Windows: Watermarks, Late Data, and Correctable Results
Design a minute-level dashboard that tolerates delayed events without treating a watermark as proof that every event arrived.
Fencing tokens: reject workers whose lease has expired
Protect a shared resource from stale owners instead of assuming expired leases stop processes.
Causal order: what a logical clock can and cannot tell you
Distinguish event causality from wall-clock order with Lamport and vector clocks.