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'. Audit and Compliance Platform: triggered by 'Compliance audit finding that audit records were modified af'.

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'. Audit and Compliance Platform: triggered by 'Compliance report generation taking > 5 minutes against Post'.

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'. Audit and Compliance Platform: triggered by 'SIEM consumer lag causing it to fall behind retention window'.

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. .

PostgreSQL write p99 > 20ms with low connection count; pg_stat_activity showing transactions serialized on the same partition's chain-tip read; ingestion throughput plateauing well below hardware limits; auto_explain showing sequential scan on audit_events for "SELECT hash FROM audit_events ORDER BY id DESC LIMIT 1"

Tier 1: Integrity Chain Write Serialization: Per-partition chain-tip read before each insert serializing concurrent audit writers. Recommended evolution: Introduce partition-level chain sequence tables: a single row per partition tracking the current chain tip with an advisory lock, eliminating the full table read. Alternatively, shard the integrity chain by source system or tenant, accepting per-shard chains rather than a single global chain. Use PostgreSQL INSERT ... RETURNING with sequence-assigned IDs to eliminate the pre-insert read entirely, deferring chain hash computation to an async integrity sealer that appends hashes in order without blocking the write path. .

Compliance investigator queries returning in > 30s; PostgreSQL showing high sequential scan counts on audit_events partitions; investigator-facing API p99 > 10s; pg_stat_statements showing actor_id-scoped queries without partition pruning in the query plan

Tier 2: Actor Query Full-Partition Scan: Missing secondary index table for actor_id and resource_id lookup paths across time-partitioned audit data. Recommended evolution: Build a secondary index table audit_events_by_actor(actor_id, event_time, event_id) populated synchronously on insert. Accept the additional write per event as the cost of O(log n) actor-scoped queries. Alternatively, route actor-scoped queries to ClickHouse where columnar storage makes actor_id filters efficient without a secondary B-tree index. .