Questions
7 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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07 / 10

How would you implement a distributed task queue in Go without using an external framework?

Use Redis lists (BLPOP/RPUSH) or Kafka topics as the queue, goroutines as workers, and implement heartbeat/lease mechanisms for at-least-once delivery with a dead letter queue for failures.

Redis-based task queue
Production considerations
  1. 1

    At-least-once delivery: use RPOPLPUSH to move task to processing list, remove after success

  2. 2

    Idempotency: store processed task IDs in DB with a UNIQUE constraint to handle duplicate delivery

  3. 3

    asynq library: production-grade Redis-based queue with retries, scheduling, and dashboard

  4. 4

    Kafka with segmentio/kafka-go: durable, ordered, replayable — prefer over Redis for high-volume event streams

  5. 5

    Dead letter queue: capture failed tasks for manual inspection and replay after bug fixes