Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'. IoT Telemetry Ingestion Platform: triggered by 'PostgreSQL write p99 > 100ms at sustained device fleet load;'.

Migration Step 2

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'. IoT Telemetry Ingestion Platform: triggered by 'Alerting system query latency > 500ms due to TimescaleDB que'.

Migration Step 3

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'. IoT Telemetry Ingestion Platform: triggered by 'Multi-device aggregate queries (fleet-wide max/min/avg over '.

PgBouncer pool wait queue > 0 during peak hours; application errors reporting "connection pool exhausted" or pool timeout; pool utilization > 90%

Tier 1: Connection Pool Exhaustion: Aggregate tenant connection demand exceeding PgBouncer pool_size. Recommended evolution: Implement per-tenant connection quotas at the application layer before hitting the pool; increase pool_size incrementally; identify top-N connection consumers by tenant and implement connection reuse within tenant request handlers .

pg_stat_activity shows one tenant's queries dominating query runtime; other tenants reporting p99 latency regression while their own query counts are stable; PostgreSQL shared_buffers cache eviction rate increasing

Tier 2: Noisy Tenant I/O Saturation: Single large tenant displacing other tenants' working sets from shared buffer cache. Recommended evolution: Implement pg_cgroups or connection-level resource groups if available; consider database-per-tenant for the top N largest tenants while keeping the shared schema for smaller tenants (hybrid isolation model) .

TimescaleDB write latency p99 > 50ms for batch INSERT operations; pg_stat_activity showing wait events on WAL flush; TimescaleDB active chunk autovacuum running continuously; Kafka consumer group lag for storage writers growing steadily at baseline (non-storm) load

Tier 1: TimescaleDB Write Throughput Ceiling: TimescaleDB single-node write throughput ceiling (~50k–100k rows/second depending on row width and chunk size configuration). Recommended evolution: Tune TimescaleDB chunk_time_interval to match write cadence (smaller chunks = faster compression, lower WAL amplification per chunk); enable native compression on chunks older than 1 hour to reduce on-disk footprint; add a dedicated NVMe volume for WAL separate from data directory; consider TimescaleDB multi-node for horizontal write distribution across data nodes .

Kafka consumer group lag jumping from baseline (<100k) to >10M messages within minutes; Kafka broker disk write rate elevated; TimescaleDB write thread pool fully saturated; Redis last-known-value update latency acceptable but historical storage significantly behind real-time; device reconnect event visible in device authentication logs correlating with lag spike

Tier 2: Kafka Consumer Lag from Reconnect Storm: Kafka consumer pool sized for steady-state throughput, not burst from device reconnect storm; insufficient storage writer parallelism for burst absorption. Recommended evolution: Pre-scale storage writer consumer replicas before anticipated high-risk windows (maintenance events, regional failovers); implement burst-aware consumer scaling using consumer group lag as the autoscale signal; tune Kafka consumer max.poll.records to batch storage INSERTs into TimescaleDB for higher per-consumer throughput (target 500–1000 rows per INSERT batch rather than single-row inserts) .