Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'HIPAA audit requirement exposed during external security rev'. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'.

Migration Step 2

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'FHIR events being published to Kafka but corresponding clini'. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'.

Migration Step 3

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'Facility acquisition or merger; compliance requirement for d'. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'.

Audit log table growing at > 500K rows/day; INSERT p99 on audit_log > 20ms; autovacuum unable to keep up with dead tuple accumulation from UPDATE operations on the audit log's index pages

Tier 1: Audit Log Write Throughput: Audit log receiving one row per record access creates I/O contention with clinical record writes on the same PostgreSQL primary. Recommended evolution: Partition the audit_log table by month using PostgreSQL declarative partitioning; child partitions allow VACUUM to operate on bounded table segments without scanning the entire history; index each partition independently to keep index size proportional to partition row count rather than total log size .

pg_locks showing RowExclusiveLock waits on clinical_records or encounter_notes during shift-change peak hours; write p99 > 100ms; occasional deadlock errors in application logs correlated with concurrent addenda writes to the same encounter

Tier 2: Concurrent Encounter Write Lock Contention: Multiple clinical staff members writing addenda to the same encounter simultaneously, or two processes updating encounter status concurrently. Recommended evolution: Implement optimistic locking with an encounter version column; reject concurrent writes with a conflict error and require the client to reload and retry; this eliminates lock waits by failing fast rather than waiting; ensure the application presents a clear conflict resolution UI: in a clinical context, silent overwrites of concurrent edits are a patient safety risk, not just a data integrity issue .

Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal

Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .

Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online

Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .