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# Sepsis Engine Design Decisions
## Why Redis for SIRS state, not a PostgreSQL time-range query
At a medium hospital with 200 concurrent inpatients, each generating five observations
per patient per minute, the sepsis engine processes approximately 17 observation events
per second at steady state.
A PostgreSQL alternative would look like this on every event:
```sql
SELECT observation_code
FROM observations
WHERE encounter_id = :encounterId
AND observation_code IN ('TEMP_C', 'HEART_RATE', 'RESP_RATE', 'WBC_K_UL')
AND recorded_at >= NOW() - INTERVAL '30 minutes'
AND (
(observation_code = 'TEMP_C' AND (value > 38.3 OR value < 36.0)) OR
(observation_code = 'HEART_RATE' AND value > 90) OR
(observation_code = 'RESP_RATE' AND value > 20) OR
(observation_code = 'WBC_K_UL' AND (value > 12.0 OR value < 4.0))
);
This query hits the observations table on every event. Under load it competes for
I/O with the ingest path writing new rows — both want the same composite index. With
Redis: four SET/DEL operations and one MGET, all O(1), all in-memory. No disk
I/O, no lock contention with the write path.
The TTL enforces the 30-minute sliding window automatically. Without Redis (or an equivalent in-memory store), a background job would be needed to clean up stale criteria — another failure point, another deployment concern.
Why not Apache Flink
Flink is a distributed stream processor designed for stateful computation at scale (millions of events per second across a fleet). It brings real costs:
- A Flink cluster (JobManager + TaskManagers) is infrastructure that must be deployed, monitored, and upgraded independently of the application.
- Flink state backends (RocksDB, heap) add operational complexity that is not justified unless the stream volume saturates what a single consumer thread can handle.
- Flink's exactly-once semantics require Kafka transactions, which add latency and require tuning separate from the rest of the application.
At a single hospital (200 inpatients, ~17 observations/second), a Kafka consumer + Redis state store handles the volume with single-digit millisecond latency per event and no additional infrastructure. The trade-off: if this system needed to scale to a multi-hospital network with 50,000+ concurrent inpatients (~5,000 observations/second), Flink would become the right choice. The architecture decision is correct at this scale and defensible at interview with a clear scale inflection point named.