Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. 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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. 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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. IoT Telemetry Ingestion Platform: triggered by 'Multi-device aggregate queries (fleet-wide max/min/avg over '.

Kafka consumer group lag (bytes or offsets) growing for the analytics topic group; ClickHouse dashboard timestamps falling behind wall clock by > 60s; ClickHouse insert throughput < Kafka produce rate

Tier 1: Consumer Lag and Freshness Degradation: ClickHouse insert throughput insufficient for Kafka produce rate. Recommended evolution: Tune ClickHouse insert buffer size and async_insert settings; increase consumer parallelism up to the Kafka partition count; batch inserts into ClickHouse using the Buffer engine or materialized views with merge trees .

One Kafka partition offset growing significantly faster than others; one consumer instance CPU/network saturated while others are idle

Tier 2: Hot Partition and Skewed Consumer Load: Skewed partition key distribution: high-cardinality entity routing the same high-volume key to one partition. Recommended evolution: Add a secondary hash suffix to the partition key to distribute load; increase topic partition count (note: keyed ordering breaks for existing messages); re-evaluate partition key selection based on actual cardinality measurements .

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) .