Event-driven logistics orchestration
A multi-region logistics platform suffered cascading failures during peak demand. The monolithic event processor couldn't scale horizontally, and retry storms amplified outages. We redesigned the event architecture for predictable throughput.
Context
A logistics operator processed shipment events across 12 regional hubs. During peak periods, the single-threaded event processor fell behind, causing cascading delays.
Constraint
The system had to handle 3x current peak load without proportionally scaling infrastructure cost, and couldn't drop or duplicate events.
Intervention
Decomposed the event processor into domain-specific consumers with explicit back-pressure. Added dead-letter queues for poison messages, circuit breakers at external API boundaries, and horizontal scaling per partition.
Key decisions
- Domain-partitioned event consumers
- Dead-letter queues with automated retry
- Circuit breakers at external boundaries
- Horizontal pod autoscaling per partition
- Real-time observability dashboards
- Chaos engineering validation
Outcomes
Peak throughput increased 3x while infrastructure cost dropped 40%. P99 latency during peak went from 12s to 180ms.
Why it matters
Predictable event processing means on-time deliveries and accurate tracking—directly impacting customer experience and operational cost.
Implementation
Practical technology choices that matched the constraints.
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