Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Audit and Compliance Platform: triggered by 'Compliance audit finding that audit records were modified af'. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'.

Migration Step 2

Both scenarios define a migration step at this stage. Audit and Compliance Platform: triggered by 'Compliance report generation taking > 5 minutes against Post'. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'.

Migration Step 3

Both scenarios define a migration step at this stage. Audit and Compliance Platform: triggered by 'SIEM consumer lag causing it to fall behind retention window'. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'.

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. .

PgBouncer pool wait queue > 0 during peak hours; application errors reporting "connection pool exhausted" or pool timeout; pool utilization > 90%

Tier 1: Connection Pool Exhaustion: Aggregate tenant connection demand exceeding PgBouncer pool_size. Recommended evolution: Implement per-tenant connection quotas at the application layer before hitting the pool; increase pool_size incrementally; identify top-N connection consumers by tenant and implement connection reuse within tenant request handlers .

pg_stat_activity shows one tenant's queries dominating query runtime; other tenants reporting p99 latency regression while their own query counts are stable; PostgreSQL shared_buffers cache eviction rate increasing

Tier 2: Noisy Tenant I/O Saturation: Single large tenant displacing other tenants' working sets from shared buffer cache. Recommended evolution: Implement pg_cgroups or connection-level resource groups if available; consider database-per-tenant for the top N largest tenants while keeping the shared schema for smaller tenants (hybrid isolation model) .