Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'.

Migration Step 2

Both scenarios define a migration step at this stage. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'.

Migration Step 3

Both scenarios define a migration step at this stage. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'.

Redis cache miss spike visible in monitoring; PostgreSQL query rate spiking for listing reads despite stable write volume; p99 listing API latency > 500ms during traffic spike events

Tier 1: Viral Listing Thundering Herd: Cache TTL expiry on hot listings during peak traffic: all concurrent requests bypass cache simultaneously. Recommended evolution: Implement staggered TTL jitter on listing cache entries; use probabilistic early refresh (refresh before TTL expiry when remaining TTL < 20% and request rate is high); implement single-flight/request coalescing at the application layer to collapse concurrent cache misses into a single database read .

Saga compensation events appearing in order event log; checkout p99 > 2s; pg_locks showing contended rows on inventory_reservations table; idempotency key conflicts increasing in payment service logs

Tier 2: Checkout Saga Contention: Concurrent checkout transactions competing for the same inventory rows; saga timeout thresholds too aggressive. Recommended evolution: Increase inventory reservation table partition count; tune saga step timeout to 2x the observed p99 for each step under load; implement a per-listing checkout serialization queue to prevent N concurrent sagas competing for the same inventory .

Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal

Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .

Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online

Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .