Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Notification Delivery Platform: triggered by 'Notification provider latency (SendGrid, FCM) adding 200–500'. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'.

Migration Step 2

Both scenarios define a migration step at this stage. Notification Delivery Platform: triggered by 'Product requirement to add mobile push notifications and SMS'. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'.

Migration Step 3

Both scenarios define a migration step at this stage. Notification Delivery Platform: triggered by 'User complaints about notification volume increasing; spam c'. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '.

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 .

Elasticsearch index CDC consumer lag > 10s; search results showing items that no longer exist or missing recently published items; CDC connector health dashboard showing processing rate below write rate

Tier 1: Index Freshness Degradation: CDC consumer or Elasticsearch bulk indexer not keeping pace with PostgreSQL write rate. Recommended evolution: Increase Elasticsearch bulk indexer thread count; tune bulk index batch size and flush interval; profile CDC connector bottleneck (network vs Elasticsearch write throughput vs mapping complexity) .

Elasticsearch JVM heap usage > 75% sustained; GC pause events visible in cluster logs; query p99 latency spikes during GC; cluster health showing yellow (unassigned shards during GC recovery)

Tier 2: Search Cluster Heap Pressure: Large aggregation queries or high document count per shard exceeding JVM heap budget. Recommended evolution: Increase Elasticsearch heap to 50% of node RAM (max 30GB for ZGC); reduce shard count to keep per-shard document count < 50M; disable dynamic mapping and explicitly define all field types; move to doc values for all non-analyzed fields .