Design real-time usage-based billing data architecture
Tests whether you can guarantee exactly-once billing at scale without data loss. Strong answers cover idempotent Kafka ingestion, ClickHouse aggregation, reconciliation, and audit trails. Red flag: claiming exactly-once instead of at-least-once dedup.
Tests whether you can architect for exactly-once billing semantics under high throughput. A strong answer maps four layers: idempotent event capture via Kafka with deterministic idempotency keys; real-time aggregation in ClickHouse partitioned by tenant and time; a programmable pricing engine applying rates to meter values; and continuous reconciliation loops comparing streamed totals against immutable event logs. You must also explain audit trails and late-arriving event handling without reprocessing entire windows.
Read the original → flexprice.io
- #system design
- #data architecture
- #distributed systems
- #billing
- #kafka
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