Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Content Management Platform: triggered by 'Content read API p99 > 100ms during peak traffic; PostgreSQL'. 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. Content Management Platform: triggered by 'Full-text search p99 > 500ms; inability to implement faceted'. 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. Content Management Platform: triggered by 'Month-end content performance reports generating sequential '. IoT Telemetry Ingestion Platform: triggered by 'Multi-device aggregate queries (fleet-wide max/min/avg over '.

PostgreSQL pg_stat_statements showing > 10 distinct query patterns with high call counts from the content list API path; database queries per second growing linearly with API request rate for content list endpoints (should be sub-linear with proper batch fetching); p99 for content list API > 200ms during moderate traffic

Tier 1: N+1 Query Amplification: ORM-level N+1 patterns in content relationship traversal: author fetch, category fetch, related content fetch as independent queries per article. Recommended evolution: Audit every content API response with query logging enabled and count queries per request for each content type; implement eager loading for all included relationships (JOIN for 1:1, IN-clause batch for 1:N); validate that content list endpoints produce a fixed number of queries regardless of list size (O(1) queries, not O(N)); add a query count assertion to integration tests for content list endpoints to prevent regression .

PostgreSQL read replica CPU spike correlated exactly with publish events; Redis cache hit rate dropping to near 0% immediately after publish for popular content; content API p99 spiking from < 20ms to > 500ms during the 1–3 second window after a high-traffic content item is published

Tier 2: Cache Invalidation Thundering Herd: Cache key deletion on publish triggering simultaneous cache miss stampede for all concurrent readers of popular content. Recommended evolution: Implement cache-aside with probabilistic early expiration (PER): before the cache TTL expires, a fraction of reads proactively refresh the cache value while other reads continue serving the cached value; this eliminates the hard expiry boundary that causes simultaneous misses; alternatively, on publish, write the new content value directly into the cache key before invalidating the old one (update-in-place rather than delete-and-miss) to eliminate the invalidation gap .

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) .