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'. Developer Tools Platform: triggered by 'First noisy neighbor incident where one tenant's CI burst de'.

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 '. Developer Tools Platform: triggered by 'Kafka publish failures rolling back pipeline completion tran'.

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'. Developer Tools Platform: triggered by 'Log search returning results from other tenants' pipelines 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 .

Redis job queue depth > 1000 correlated with a single tenant identifier; other tenants reporting p99 job start time > 60 seconds; tenant-level queue metrics showing one tenant holding > 50% of in-flight worker slots

Tier 1: Job Queue Tenant Noisy Neighbor: Shared Redis queue with shared worker pool allowing one tenant to monopolize available capacity. Recommended evolution: Implement per-tenant queue lanes in Redis (separate key namespaces per tenant, e.g., jobs:{tenant_id}:{priority}); implement a weighted fair scheduler at the worker dispatch layer that reads from tenant queues in round-robin order with priority weighting; cap the number of concurrently executing jobs per tenant to the tenant's quota, not to the total available worker count .

DDL migration duration > 10s on pipeline_runs, jobs, or artifacts tables; migration deployment causing timeout errors for active CI pipeline API calls during the deployment window; pg_locks showing AccessExclusiveLock held by ALTER TABLE statement

Tier 2: PostgreSQL Schema Migration Lock: High-volume tables requiring locking DDL changes during deployments with concurrent tenant activity. Recommended evolution: Adopt zero-downtime migration patterns exclusively: add columns with nullable defaults first (no table lock in PostgreSQL 11+), then backfill, then add constraints via NOT VALID followed by VALIDATE CONSTRAINT in a separate transaction; use pg_repack for table rewrites; never run concurrent index creation without CONCURRENTLY on any table with > 1M rows .