Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'. Observability Platform: triggered by 'Prometheus storage limits reached at production metric volum'.

Migration Step 2

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'. Observability Platform: triggered by 'Elasticsearch indexing pressure causing log rejection (HTTP '.

Migration Step 3

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'. Observability Platform: triggered by 'Alert evaluation latency > 1s causing missed alert firing wi'.

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) .

ClickHouse part merge frequency increasing; dashboard queries timing out on metrics with high label cardinality; ClickHouse system.metrics showing active_parts count elevated; new metric instrumentation causing sudden storage growth disproportionate to fleet size; query_log showing metrics queries scanning full column segments without pruning

Tier 1: Metric Cardinality Budget Exceeded: Unbounded label cardinality generating millions of distinct time series that exceed ClickHouse part merge capacity and query planner pruning effectiveness. Recommended evolution: Enforce a cardinality budget at ingestion: before a metric is accepted, evaluate the distinct value count of each label dimension against a per-dimension limit (e.g., max 100 distinct values for any single label key). Reject or rewrite metrics that exceed the budget: rewrite user_id labels to user_cohort or drop them entirely. Implement a cardinality analysis dashboard showing the top 10 highest-cardinality metric series sorted by storage cost. ClickHouse distributed table partitioning by metric name reduces the impact of a single high-cardinality metric on global query performance. .

Kafka log topic consumer lag growing > 1 million messages during incident periods; Elasticsearch indexing throughput metrics showing queue buildup; incident post-mortems noting that relevant log records were not available in the search interface during the incident; log consumer memory pressure from unbounded batch accumulation

Tier 2: Log Volume Spike Exceeding Consumer Throughput: Log Kafka consumer sized for normal throughput; unable to drain the spike volume produced during incident-driven log floods. Recommended evolution: Size the log consumer for 10x normal throughput, not 1x: observability platform capacity must be planned for the incident scenario, not the steady state. Implement consumer autoscaling triggered by consumer lag metric: when Kafka consumer lag exceeds a threshold, add consumer instances automatically. Implement log sampling at the producer side for DEBUG and INFO level messages during identified spike periods : preserve all ERROR and WARN messages, sample INFO at 10%, sample DEBUG at 1%. This bounds the worst-case log volume without sacrificing diagnostic signal. .