Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. 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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. 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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. API Gateway Platform: triggered by 'First Redis instance crash causing 100% gateway error rate f'.

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 .

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 .