Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Geospatial Tracking Platform: triggered by 'PostGIS proximity query p99 > 100ms under concurrent fleet t'. Notification Delivery Platform: triggered by 'Notification provider latency (SendGrid, FCM) adding 200–500'.

Migration Step 2

Both scenarios define a migration step at this stage. Geospatial Tracking Platform: triggered by 'Location update API p99 > 200ms correlated with geofence cou'. Notification Delivery Platform: triggered by 'Product requirement to add mobile push notifications and SMS'.

Migration Step 3

Both scenarios define a migration step at this stage. Geospatial Tracking Platform: triggered by 'PostgreSQL location_history table exceeding 100GB with query'. Notification Delivery Platform: triggered by 'User complaints about notification volume increasing; spam c'.

Redis used_memory > 75% of maxmemory; Redis evictions appearing in INFO stats; GEOSEARCH returning stale or missing entity positions; Redis OOM errors in application logs during fleet expansion events; proximity query latency increasing above 10ms baseline

Tier 1: Redis Geospatial Memory Pressure: Redis memory exhausted by unbounded geospatial entity growth without entity expiry or cleanup. Recommended evolution: Implement entity-scoped Redis key TTL tied to the last received update timestamp. Entities that have not sent a position update in > 5 minutes are expired from Redis automatically (Redis EXPIRE on the ZSET entry using a per-entity auxiliary key pattern, since ZSET members do not support per-member TTL natively). Alternatively, introduce a background reconciliation job that removes entities from the live position surface after an inactivity threshold. Shard the geospatial index across multiple Redis instances by geographic region using consistent hashing on the region key. .

TimescaleDB write p99 > 50ms; WAL volume > 200MB/minute sustained; disk I/O utilization > 80% on TimescaleDB data volume; chunk creation log entries during fleet expansion events correlated with write latency spikes; TimescaleDB worker queue depth growing during ingestion bursts

Tier 2: TimescaleDB Write Throughput Ceiling: TimescaleDB hypertable chunk write throughput saturated by high-frequency location update volume; chunk creation DDL causing write stalls during expansion. Recommended evolution: Tune TimescaleDB chunk_time_interval to match the ingestion rate: smaller chunks (1-hour intervals instead of 1-day) reduce per-chunk write volume but increase chunk creation frequency. Use timescaledb-parallel-copy for bulk historical ingestion. Move the TimescaleDB WAL to a dedicated NVMe volume. Introduce write batching at the application layer: buffer 500ms of position updates per entity and write as a single multi-row INSERT, reducing the per-update overhead from N single-row INSERTs to N/batch_size batch INSERTs. .

RabbitMQ queue depth for email channel growing > 100k messages; SendGrid 429 responses visible in delivery worker logs; email delivery p95 latency > 2 minutes; delivery worker retry thread pool saturated; dead-letter queue receiving messages from retry exhaustion despite provider being available

Tier 1: Provider Rate Limit Backlog: Delivery worker retry policy not aligned with provider rate limit reset window; workers retrying before the rate limit has reset, accumulating failed attempts. Recommended evolution: Implement provider-aware retry backoff: on 429 response, parse the Retry-After header and schedule the next retry attempt at exactly that time, not on a fixed exponential backoff schedule; add per-provider circuit breakers that open after 5 consecutive 429s and attempt a probe request at the retry-after interval; scale email consumer replicas to process the backlog faster when the rate limit window resets .

Kafka consumer lag for notification event consumers growing; notification delivery volume much higher than upstream business event volume (ratio > 5:1); RabbitMQ aggregate message rate across all channel queues elevated; one upstream event type (e.g., new_comment) accounting for disproportionate share of notification volume

Tier 2: Notification Fan-Out Amplification: Upstream event fan-out generating more notification sends per event than expected; or a notification rule misconfiguration triggering notifications for every event regardless of user preference. Recommended evolution: Audit notification fan-out ratio per upstream event type; add fan-out cost metrics (notifications_generated per upstream event) as a monitored SLA; implement notification preference filtering before fan-out: only generate delivery attempts for users with the corresponding notification type enabled; implement a notification aggregation layer that batches multiple low-priority events into digest notifications rather than individual sends .