Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Developer Tools Platform: triggered by 'First noisy neighbor incident where one tenant's CI burst de'. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'.

Migration Step 2

Both scenarios define a migration step at this stage. Developer Tools Platform: triggered by 'Kafka publish failures rolling back pipeline completion tran'. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '.

Migration Step 3

Both scenarios define a migration step at this stage. Developer Tools Platform: triggered by 'Log search returning results from other tenants' pipelines d'. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '.

Redis job queue depth > 1000 correlated with a single tenant identifier; other tenants reporting p99 job start time > 60 seconds; tenant-level queue metrics showing one tenant holding > 50% of in-flight worker slots

Tier 1: Job Queue Tenant Noisy Neighbor: Shared Redis queue with shared worker pool allowing one tenant to monopolize available capacity. Recommended evolution: Implement per-tenant queue lanes in Redis (separate key namespaces per tenant, e.g., jobs:{tenant_id}:{priority}); implement a weighted fair scheduler at the worker dispatch layer that reads from tenant queues in round-robin order with priority weighting; cap the number of concurrently executing jobs per tenant to the tenant's quota, not to the total available worker count .

DDL migration duration > 10s on pipeline_runs, jobs, or artifacts tables; migration deployment causing timeout errors for active CI pipeline API calls during the deployment window; pg_locks showing AccessExclusiveLock held by ALTER TABLE statement

Tier 2: PostgreSQL Schema Migration Lock: High-volume tables requiring locking DDL changes during deployments with concurrent tenant activity. Recommended evolution: Adopt zero-downtime migration patterns exclusively: add columns with nullable defaults first (no table lock in PostgreSQL 11+), then backfill, then add constraints via NOT VALID followed by VALIDATE CONSTRAINT in a separate transaction; use pg_repack for table rewrites; never run concurrent index creation without CONCURRENTLY on any table with > 1M rows .

Kafka consumer group lag (bytes or offsets) growing for the analytics topic group; ClickHouse dashboard timestamps falling behind wall clock by > 60s; ClickHouse insert throughput < Kafka produce rate

Tier 1: Consumer Lag and Freshness Degradation: ClickHouse insert throughput insufficient for Kafka produce rate. Recommended evolution: Tune ClickHouse insert buffer size and async_insert settings; increase consumer parallelism up to the Kafka partition count; batch inserts into ClickHouse using the Buffer engine or materialized views with merge trees .

One Kafka partition offset growing significantly faster than others; one consumer instance CPU/network saturated while others are idle

Tier 2: Hot Partition and Skewed Consumer Load: Skewed partition key distribution: high-cardinality entity routing the same high-volume key to one partition. Recommended evolution: Add a secondary hash suffix to the partition key to distribute load; increase topic partition count (note: keyed ordering breaks for existing messages); re-evaluate partition key selection based on actual cardinality measurements .