Problems / Async and streaming, easy

Consumers can’t keep up

Kafka keeps events for 30 seconds. One consumer processes them, slower than they arrive.

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Your goals

No data lost to retention.

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.6 partitions · retention 30 s
Consumer groupWorkerEvent stream consumes from it.Queues deliver at least once, so a worker can see the same message twice.1 concurrent jobs

Hints

Try it first. Open one hint at a time if you get stuck.

Hint 1

Watch the lag on the stream.

Hint 2

Lag older than retention is deleted unread.

One way to solve it

Add consumers (up to the number of partitions).

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