Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. API Gateway Platform: triggered by 'PostgreSQL hot path query p99 > 2ms under sustained request '. Notification Delivery Platform: triggered by 'Notification provider latency (SendGrid, FCM) adding 200–500'.

Migration Step 2

Both scenarios define a migration step at this stage. API Gateway Platform: triggered by 'Rate limit enforcement allowing requests above the configure'. Notification Delivery Platform: triggered by 'Product requirement to add mobile push notifications and SMS'.

Migration Step 3

Both scenarios define a migration step at this stage. API Gateway Platform: triggered by 'First Redis instance crash causing 100% gateway error rate f'. Notification Delivery Platform: triggered by 'User complaints about notification volume increasing; spam c'.

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 .

RabbitMQ queue depth for email channel growing > 100k messages; SendGrid 429 responses visible in delivery worker logs; email delivery p95 latency > 2 minutes; delivery worker retry thread pool saturated; dead-letter queue receiving messages from retry exhaustion despite provider being available

Tier 1: Provider Rate Limit Backlog: Delivery worker retry policy not aligned with provider rate limit reset window; workers retrying before the rate limit has reset, accumulating failed attempts. Recommended evolution: Implement provider-aware retry backoff: on 429 response, parse the Retry-After header and schedule the next retry attempt at exactly that time, not on a fixed exponential backoff schedule; add per-provider circuit breakers that open after 5 consecutive 429s and attempt a probe request at the retry-after interval; scale email consumer replicas to process the backlog faster when the rate limit window resets .

Kafka consumer lag for notification event consumers growing; notification delivery volume much higher than upstream business event volume (ratio > 5:1); RabbitMQ aggregate message rate across all channel queues elevated; one upstream event type (e.g., new_comment) accounting for disproportionate share of notification volume

Tier 2: Notification Fan-Out Amplification: Upstream event fan-out generating more notification sends per event than expected; or a notification rule misconfiguration triggering notifications for every event regardless of user preference. Recommended evolution: Audit notification fan-out ratio per upstream event type; add fan-out cost metrics (notifications_generated per upstream event) as a monitored SLA; implement notification preference filtering before fan-out: only generate delivery attempts for users with the corresponding notification type enabled; implement a notification aggregation layer that batches multiple low-priority events into digest notifications rather than individual sends .