Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'LLM responses requiring more factual accuracy or domain-spec'. 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. AI Retrieval-Augmented Generation Platform: triggered by 'Document ingestion p99 > 500ms due to embedding API call in '. 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. AI Retrieval-Augmented Generation Platform: triggered by 'Index scan range too large for per-query latency targets; te'. Healthcare Records Platform: triggered by 'Facility acquisition or merger; compliance requirement for d'.

Retrieval quality metrics (MRR, NDCG) declining despite stable query volume; users reporting irrelevant context being surfaced; pgvector IVFFlat probes set below recommended value for current document count

Tier 1: Vector Index Recall Degradation: IVFFlat index not rebuilt after significant document additions; or probes too low for current index size. Recommended evolution: Schedule periodic index rebuilds triggered by document count growth (e.g., rebuild at 2x the document count present at last index build); increase ivfflat.probes to improve recall at cost of query latency; evaluate HNSW for recall-critical workloads .

PostgreSQL process memory > 8GB; OOM killer events on the database host; vector query p99 latency increasing as shared_buffers evicts vector index pages; pg_stat_bgwriter showing high buffers_clean rate

Tier 2: PostgreSQL Memory Pressure from Vector Operations: Vector index (HNSW or large IVFFlat) and embedding storage competing with relational data for shared_buffers. Recommended evolution: Increase PostgreSQL shared_buffers to 40% of available RAM; move vector tables to a dedicated tablespace on NVMe; partition large vector tables by document category to reduce per-query index scan range; evaluate dedicated pgvector replica for query isolation .

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 .