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'. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'.

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 '. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'.

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'. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'.

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 .

Redis cache miss spike visible in monitoring; PostgreSQL query rate spiking for listing reads despite stable write volume; p99 listing API latency > 500ms during traffic spike events

Tier 1: Viral Listing Thundering Herd: Cache TTL expiry on hot listings during peak traffic: all concurrent requests bypass cache simultaneously. Recommended evolution: Implement staggered TTL jitter on listing cache entries; use probabilistic early refresh (refresh before TTL expiry when remaining TTL < 20% and request rate is high); implement single-flight/request coalescing at the application layer to collapse concurrent cache misses into a single database read .

Saga compensation events appearing in order event log; checkout p99 > 2s; pg_locks showing contended rows on inventory_reservations table; idempotency key conflicts increasing in payment service logs

Tier 2: Checkout Saga Contention: Concurrent checkout transactions competing for the same inventory rows; saga timeout thresholds too aggressive. Recommended evolution: Increase inventory reservation table partition count; tune saga step timeout to 2x the observed p99 for each step under load; implement a per-listing checkout serialization queue to prevent N concurrent sagas competing for the same inventory .