Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'HIPAA audit requirement exposed during external security rev'. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '.

Migration Step 2

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'FHIR events being published to Kafka but corresponding clini'. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'.

Migration Step 3

Both scenarios define a migration step at this stage. Healthcare Records Platform: triggered by 'Facility acquisition or merger; compliance requirement for d'. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'.

Audit log table growing at > 500K rows/day; INSERT p99 on audit_log > 20ms; autovacuum unable to keep up with dead tuple accumulation from UPDATE operations on the audit log's index pages

Tier 1: Audit Log Write Throughput: Audit log receiving one row per record access creates I/O contention with clinical record writes on the same PostgreSQL primary. Recommended evolution: Partition the audit_log table by month using PostgreSQL declarative partitioning; child partitions allow VACUUM to operate on bounded table segments without scanning the entire history; index each partition independently to keep index size proportional to partition row count rather than total log size .

pg_locks showing RowExclusiveLock waits on clinical_records or encounter_notes during shift-change peak hours; write p99 > 100ms; occasional deadlock errors in application logs correlated with concurrent addenda writes to the same encounter

Tier 2: Concurrent Encounter Write Lock Contention: Multiple clinical staff members writing addenda to the same encounter simultaneously, or two processes updating encounter status concurrently. Recommended evolution: Implement optimistic locking with an encounter version column; reject concurrent writes with a conflict error and require the client to reload and retry; this eliminates lock waits by failing fast rather than waiting; ensure the application presents a clear conflict resolution UI: in a clinical context, silent overwrites of concurrent edits are a patient safety risk, not just a data integrity issue .

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. .