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'. Read-Heavy SaaS API: triggered by 'Connection pool exhaustion or p99 read latency > 200ms under'.

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'. Read-Heavy SaaS API: triggered by 'Primary CPU > 70% during peak read hours'.

Migration Step 3

Geospatial Tracking Platform has a defined migration; Read-Heavy SaaS API does not at this stage.

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. .

p99 database latency rising; connection wait queue growing; requests timing out with "too many connections" or pool queue full errors

Tier 1: Connection Exhaustion: Database connection pool saturated or max_connections exceeded. Recommended evolution: Add PgBouncer connection pooler in transaction mode.

Database CPU > 80% sustained; read query p99 rising; cache miss rate stable but overall latency increasing

Tier 2: Read Throughput Ceiling: Single PostgreSQL primary saturated with read traffic. Recommended evolution: Add one or more streaming read replicas; implement lag-aware replica routing.