Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '. Geospatial Tracking Platform: triggered by 'PostGIS proximity query p99 > 100ms under concurrent fleet t'.

Migration Step 2

Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'. Geospatial Tracking Platform: triggered by 'Location update API p99 > 200ms correlated with geofence cou'.

Migration Step 3

Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'. Geospatial Tracking Platform: triggered by 'PostgreSQL location_history table exceeding 100GB with query'.

PostgreSQL pg_locks showing high RowExclusiveLock contention on inventory_items for specific sku_ids; checkout p99 > 2s for contended SKUs; deadlock errors appearing in application logs during sale events; effective checkout throughput for hot SKUs well below per-request checkout latency would predict

Tier 1: Flash Sale Inventory Contention: Concurrent saga checkout attempts competing for the same inventory row via row-level locking. Recommended evolution: Introduce a per-SKU checkout serialization queue at the application layer : all concurrent checkout requests for the same SKU are queued and processed serially, converting lock contention into queue latency. Alternatively, use PostgreSQL advisory locks with non-blocking trylock: requests that cannot acquire the lock immediately return a "sold out" response rather than queuing. For very high flash sale volumes, pre-allocate inventory slots (reserve N slots per sale event, each slot is a row with one reservation) to spread lock contention across N rows instead of one. .

Checkout p99 tracking payment provider p99 almost linearly; connection pool utilization on the payment service rising during payment provider slowdowns; circuit breaker trip events appearing in payment service metrics; saga timeout events correlated with payment provider latency spikes

Tier 2: Payment Provider Latency Amplifying Checkout Latency: Checkout saga holding a database connection and an inventory reservation open for the duration of the payment provider call: payment latency directly amplifies connection pool pressure. Recommended evolution: Decouple the payment step from the synchronous checkout saga: reserve inventory and create the order record synchronously, then process payment asynchronously. The customer receives an "order confirmed, payment processing" state immediately; the payment step runs as a separate saga step triggered by an event. This reduces the synchronous checkout latency to the inventory reservation time, not the payment provider round-trip time. .

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. .