Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'. Content Management Platform: triggered by 'Content read API p99 > 100ms during peak traffic; PostgreSQL'.

Migration Step 2

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'. Content Management Platform: triggered by 'Full-text search p99 > 500ms; inability to implement faceted'.

Migration Step 3

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'. Content Management Platform: triggered by 'Month-end content performance reports generating sequential '.

Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal

Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .

Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online

Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .

PostgreSQL pg_stat_statements showing > 10 distinct query patterns with high call counts from the content list API path; database queries per second growing linearly with API request rate for content list endpoints (should be sub-linear with proper batch fetching); p99 for content list API > 200ms during moderate traffic

Tier 1: N+1 Query Amplification: ORM-level N+1 patterns in content relationship traversal: author fetch, category fetch, related content fetch as independent queries per article. Recommended evolution: Audit every content API response with query logging enabled and count queries per request for each content type; implement eager loading for all included relationships (JOIN for 1:1, IN-clause batch for 1:N); validate that content list endpoints produce a fixed number of queries regardless of list size (O(1) queries, not O(N)); add a query count assertion to integration tests for content list endpoints to prevent regression .

PostgreSQL read replica CPU spike correlated exactly with publish events; Redis cache hit rate dropping to near 0% immediately after publish for popular content; content API p99 spiking from < 20ms to > 500ms during the 1–3 second window after a high-traffic content item is published

Tier 2: Cache Invalidation Thundering Herd: Cache key deletion on publish triggering simultaneous cache miss stampede for all concurrent readers of popular content. Recommended evolution: Implement cache-aside with probabilistic early expiration (PER): before the cache TTL expires, a fraction of reads proactively refresh the cache value while other reads continue serving the cached value; this eliminates the hard expiry boundary that causes simultaneous misses; alternatively, on publish, write the new content value directly into the cache key before invalidating the old one (update-in-place rather than delete-and-miss) to eliminate the invalidation gap .