Problems / Async and streaming, medium

Two partitions, slow consumers

The topic has 2 partitions and the consumer group processes each event in about 5 ms. Events arrive at 2,000 a second and are kept for 30 seconds.

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Your goals

No data lost to retention, success ≥ 99.9%.

Test cases

Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 5; 2 of them are hidden until then.

  1. As given. The problem’s own traffic, scheduled failures and seed.
  2. Peak hour. 25% more traffic than the problem states, with the same failures.
  3. A server dies. One replica of the service with the most replicas is killed for part of the run.

The traffic

2,000 requests a second for 45 seconds: 100% event (writes).

The system

What each component does in this design, and what calls it. The simulator mimics each one from these settings.

ComponentIts job hereSettings
Load balancerLoad balancerTraffic enters here.Calls Ingest API.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header.–
Ingest APIApp serviceLoad balancer forwards to it.Calls Event stream (publishes).Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s.2 replicas × 200 slots · timeout 1.0 s
Event streamEvent streamIngest API publishes to it.Calls Consumer group.Kafka keeps order only within a partition, so the partition key decides what stays in sequence.2 partitions · retention 30 s
Consumer groupWorkerEvent stream consumes from it.Queues deliver at least once, so a worker can see the same message twice.2 concurrent jobs

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