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'. API Gateway Platform: triggered by 'PostgreSQL hot path query p99 > 2ms under sustained request '.

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'. API Gateway Platform: triggered by 'Rate limit enforcement allowing requests above the configure'.

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'. API Gateway Platform: triggered by 'First Redis instance crash causing 100% gateway error rate f'.

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 .

Redis command latency p99 > 0.5ms; gateway hot path p99 exceeding 2ms with Redis as the bottleneck (not upstream service); Redis CPU > 60% sustained; Lua script execution visible in SLOWLOG at > 0.1ms frequency

Tier 1: Redis Rate Limit Throughput: Single Redis instance processing all rate limit Lua scripts serially for all tenants across all gateway replicas. Recommended evolution: Shard rate limit counters across Redis Cluster nodes by hashing tenant_id to a cluster slot; this distributes Lua script execution across nodes proportional to tenant count; ensure tenant_id-keyed counters use hash tags ({tenant_id}) so all keys for a tenant land on the same slot and Lua scripts can operate on them atomically; do not use Redis Cluster without testing Lua script compatibility against your cluster topology first .

Tenant reports that rate limit increase takes > 30 seconds to take effect across all gateway replicas; configuration change audit log shows primary PostgreSQL write completing, but gateway replicas still routing to old backend endpoints beyond the expected cache TTL window

Tier 2: Configuration Propagation Latency: Local in-process cache TTL too long, or cache invalidation signal (Redis pub/sub or Kafka) not reaching all replicas. Recommended evolution: Implement configuration change notification via Redis pub/sub: PostgreSQL configuration writes also publish a config_invalidated event to a Redis channel; each gateway replica subscribes to this channel and flushes the affected local cache key on receipt; this reduces propagation latency from TTL duration to sub-second pub/sub delivery without eliminating the local cache that protects Redis from per-request configuration lookups .