Distributed Systems · core
Event Streaming & Kafka
Partitioned logs, producers, consumer groups, offsets, ordering, delivery semantics, backpressure, and stream processing.
Mental model
Kafka turns an append-only log into a coordination boundary. Ordering is per partition, progress is an offset, and consumers own replay and idempotency.
How to study Event Streaming & Kafka
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Kafka: a Distributed Messaging System for Log Processing to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Event Streaming & Kafka with Message Queues, Caching. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Event Streaming & Kafka and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Event Streaming & Kafka
Partitioned logs, producers, consumer groups, offsets, ordering, delivery semantics, backpressure, and stream processing. Implement designOutline() returning non-empty values for: partitioning, deliverySemantics, consumerRecovery. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with partitioning, deliverySemantics, consumerRecovery plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Kafka guarantees ordering per partition. What does that force on your partition key choice?
Build evidence
Synthesize: Distributed Systems
Reason about coordination, data placement, logs, durable workflows, consistency, and recovery under partial failure. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks