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'. AI Retrieval-Augmented Generation Platform: triggered by 'LLM responses requiring more factual accuracy or domain-spec'.

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'. AI Retrieval-Augmented Generation Platform: triggered by 'Document ingestion p99 > 500ms due to embedding API call in '.

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 '. AI Retrieval-Augmented Generation Platform: triggered by 'Index scan range too large for per-query latency targets; te'.

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 .

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 .