Questions
4 of 10
1Explain the fan-out and fan-in concurrency pattern with a practical example.
2How does the select statement work with multiple channels, including a default case?
3How does Go's GMP scheduler work and how does it affect concurrency design?
4Describe how you would approach a Go service that needs to process Kafka events with exactly-once semantics.
5What is a goroutine leak and how do you detect and prevent one?
6When should you use sync.Mutex vs channels for shared state?
7How would you implement a distributed task queue in Go without using an external framework?
8What is a goroutine, and how does it differ from an OS thread?
9What is the difference between a buffered and unbuffered channel?
10Design a worker pool for processing 100,000 jobs with back-pressure and graceful shutdown.
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04 / 10

Describe how you would approach a Go service that needs to process Kafka events with exactly-once semantics.

True exactly-once requires idempotent consumers with deduplication stored in the DB, the transactional outbox pattern for producing events, and Kafka consumer group offset commits tied to successful processing.

Idempotent consumer pattern
Exactly-once checklist
  1. 1

    Idempotent consumer: processed_events table with UNIQUE(event_id) — ON CONFLICT DO NOTHING skips duplicates

  2. 2

    Transactional outbox: write event and state change in same DB transaction — relay publishes to Kafka separately

  3. 3

    Commit offsets only after successful DB write — ensures re-processing on failure

  4. 4

    Kafka transactions + transactional producers for Kafka-to-Kafka pipelines (not DB sinks)

  5. 5

    segmentio/kafka-go or confluent-kafka-go for production Kafka consumers in Go