Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'. API Gateway Platform: triggered by 'PostgreSQL hot path query p99 > 2ms under sustained request '.

Migration Step 2

Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '. API Gateway Platform: triggered by 'Rate limit enforcement allowing requests above the configure'.

Migration Step 3

Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'. API Gateway Platform: triggered by 'First Redis instance crash causing 100% gateway error rate f'.

Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances

Tier 1: Transcoding Worker Throughput: Transcoding consumer group undersized relative to peak upload volume. Recommended evolution: Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs .

MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content

Tier 2: CDN Origin Thundering Herd: CDN cache miss storm on first-play of new or recently-updated content. Recommended evolution: Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id .

Redis command latency p99 > 0.5ms; gateway hot path p99 exceeding 2ms with Redis as the bottleneck (not upstream service); Redis CPU > 60% sustained; Lua script execution visible in SLOWLOG at > 0.1ms frequency

Tier 1: Redis Rate Limit Throughput: Single Redis instance processing all rate limit Lua scripts serially for all tenants across all gateway replicas. Recommended evolution: Shard rate limit counters across Redis Cluster nodes by hashing tenant_id to a cluster slot; this distributes Lua script execution across nodes proportional to tenant count; ensure tenant_id-keyed counters use hash tags ({tenant_id}) so all keys for a tenant land on the same slot and Lua scripts can operate on them atomically; do not use Redis Cluster without testing Lua script compatibility against your cluster topology first .

Tenant reports that rate limit increase takes > 30 seconds to take effect across all gateway replicas; configuration change audit log shows primary PostgreSQL write completing, but gateway replicas still routing to old backend endpoints beyond the expected cache TTL window

Tier 2: Configuration Propagation Latency: Local in-process cache TTL too long, or cache invalidation signal (Redis pub/sub or Kafka) not reaching all replicas. Recommended evolution: Implement configuration change notification via Redis pub/sub: PostgreSQL configuration writes also publish a config_invalidated event to a Redis channel; each gateway replica subscribes to this channel and flushes the affected local cache key on receipt; this reduces propagation latency from TTL duration to sub-second pub/sub delivery without eliminating the local cache that protects Redis from per-request configuration lookups .