Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'Single game server instance running at connection ceiling; h'. Developer Tools Platform: triggered by 'First noisy neighbor incident where one tenant's CI burst de'.

Migration Step 2

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'Game server instances blocking on PostgreSQL writes at end-o'. Developer Tools Platform: triggered by 'Kafka publish failures rolling back pipeline completion tran'.

Migration Step 3

Both scenarios define a migration step at this stage. Gaming Backend Platform: triggered by 'PostgreSQL event log table approaching 100B rows; reconnect '. Developer Tools Platform: triggered by 'Log search returning results from other tenants' pipelines d'.

Game server instance file descriptor count approaching OS limit (typically 65k open connections); WebSocket accept latency increasing; new connection establishment p99 > 200ms; CPU on game server instances > 70% during peak concurrent player count

Tier 1: WebSocket Connection Ceiling per Instance: Single game server instance WebSocket connection count limit; OS-level fd_max or application-level connection accept queue saturation. Recommended evolution: Increase OS fd_max to 512k and application connection accept queue depth; tune SO_REUSEPORT to allow multiple accept threads per socket; add game server instances and update consistent hashing ring; the affinity layer automatically routes new game rooms to the new instances as the ring expands: existing rooms are unaffected .

Redis command throughput > 500k/second; Redis CPU > 60%; per-tick Redis write latency p99 > 5ms (above the acceptable state sync threshold); game tick rate visibly dropping below target (30 ticks/second falling to 20) under load

Tier 2: Redis Game State Write Amplification: Game state serialization to Redis per tick producing more writes than expected; unoptimized state struct serialization writing entire state blob on any field change. Recommended evolution: Implement delta state serialization: only changed fields are written to Redis per tick using HSET with only the modified keys, not full state replacement; profile Redis command distribution per game tick to identify specific state fields with high churn; consider moving ephemeral per-tick state (player positions, projectile states) to local server memory with only durable state (scores, inventory changes) written to Redis .

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 .