Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'Single game server instance running at connection ceiling; h'. Healthcare Records Platform: triggered by 'HIPAA audit requirement exposed during external security rev'.

Migration Step 2

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'Game server instances blocking on PostgreSQL writes at end-o'. Healthcare Records Platform: triggered by 'FHIR events being published to Kafka but corresponding clini'.

Migration Step 3

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'PostgreSQL event log table approaching 100B rows; reconnect '. Healthcare Records Platform: triggered by 'Facility acquisition or merger; compliance requirement for d'.

Game server instance file descriptor count approaching OS limit (typically 65k open connections); WebSocket accept latency increasing; new connection establishment p99 > 200ms; CPU on game server instances > 70% during peak concurrent player count

Tier 1: WebSocket Connection Ceiling per Instance: Single game server instance WebSocket connection count limit; OS-level fd_max or application-level connection accept queue saturation. Recommended evolution: Increase OS fd_max to 512k and application connection accept queue depth; tune SO_REUSEPORT to allow multiple accept threads per socket; add game server instances and update consistent hashing ring; the affinity layer automatically routes new game rooms to the new instances as the ring expands: existing rooms are unaffected .

Redis command throughput > 500k/second; Redis CPU > 60%; per-tick Redis write latency p99 > 5ms (above the acceptable state sync threshold); game tick rate visibly dropping below target (30 ticks/second falling to 20) under load

Tier 2: Redis Game State Write Amplification: Game state serialization to Redis per tick producing more writes than expected; unoptimized state struct serialization writing entire state blob on any field change. Recommended evolution: Implement delta state serialization: only changed fields are written to Redis per tick using HSET with only the modified keys, not full state replacement; profile Redis command distribution per game tick to identify specific state fields with high churn; consider moving ephemeral per-tick state (player positions, projectile states) to local server memory with only durable state (scores, inventory changes) written to Redis .

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 .