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'. Audit and Compliance Platform: triggered by 'Compliance audit finding that audit records were modified af'.

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'. Audit and Compliance Platform: triggered by 'Compliance report generation taking > 5 minutes against Post'.

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'. Audit and Compliance Platform: triggered by 'SIEM consumer lag causing it to fall behind retention window'.

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 .

PostgreSQL write p99 > 20ms with low connection count; pg_stat_activity showing transactions serialized on the same partition's chain-tip read; ingestion throughput plateauing well below hardware limits; auto_explain showing sequential scan on audit_events for "SELECT hash FROM audit_events ORDER BY id DESC LIMIT 1"

Tier 1: Integrity Chain Write Serialization: Per-partition chain-tip read before each insert serializing concurrent audit writers. Recommended evolution: Introduce partition-level chain sequence tables: a single row per partition tracking the current chain tip with an advisory lock, eliminating the full table read. Alternatively, shard the integrity chain by source system or tenant, accepting per-shard chains rather than a single global chain. Use PostgreSQL INSERT ... RETURNING with sequence-assigned IDs to eliminate the pre-insert read entirely, deferring chain hash computation to an async integrity sealer that appends hashes in order without blocking the write path. .

Compliance investigator queries returning in > 30s; PostgreSQL showing high sequential scan counts on audit_events partitions; investigator-facing API p99 > 10s; pg_stat_statements showing actor_id-scoped queries without partition pruning in the query plan

Tier 2: Actor Query Full-Partition Scan: Missing secondary index table for actor_id and resource_id lookup paths across time-partitioned audit data. Recommended evolution: Build a secondary index table audit_events_by_actor(actor_id, event_time, event_id) populated synchronously on insert. Accept the additional write per event as the cost of O(log n) actor-scoped queries. Alternatively, route actor-scoped queries to ClickHouse where columnar storage makes actor_id filters efficient without a secondary B-tree index. .