Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Distributed Job Queue Platform: triggered by 'Background jobs competing with user-facing API requests for '. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'.

Migration Step 2

Both scenarios define a migration step at this stage. Distributed Job Queue Platform: triggered by 'High-priority transactional jobs (e.g., payment processing, '. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'.

Migration Step 3

Both scenarios define a migration step at this stage. Distributed Job Queue Platform: triggered by 'Multi-step jobs (e.g., ingest file → validate → transform → '. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '.

Worker idle rate > 20% despite queue depth > 10k pending jobs; PostgreSQL pg_locks showing wait events on job table index; worker job claim p99 latency > 50ms (claim should be sub-10ms with correct indexing); CPU on PostgreSQL elevated from index scan overhead on job claim queries

Tier 1: Job Claim Lock Contention: Missing or misconfigured partial index on the job claim query; high worker concurrency driving SELECT FOR UPDATE SKIP LOCKED contention on a narrow hot page. Recommended evolution: Add a partial index on (priority DESC, created_at ASC) WHERE status = 'pending' AND run_at <= NOW(): the WHERE clause reduces the index to only claimable jobs, dramatically reducing index scan range; if contention persists, implement a job dispatch service (single dispatcher process) that batches claim queries and distributes job IDs to workers via an in-memory channel, removing per-worker database claims; tune FILLFACTOR on the job table to 70% to reduce hot page contention on SKIP LOCKED .

High-priority job queue depth growing despite workers available; low-priority batch jobs showing high throughput while transactional job latency (time from enqueue to execution start) p95 > 30s; worker pool metrics showing workers claiming jobs uniformly across priority levels rather than draining the high- priority queue first

Tier 2: Priority Inversion Under Load: Single worker pool consuming from all priority queues with equal weight; no priority-weighted polling implementation. Recommended evolution: Separate worker pools per priority tier (e.g., dedicated transactional workers for high-priority jobs, shared workers for low-priority batch); or implement priority-weighted polling in a unified worker pool (poll high-priority queue N times before polling low-priority queue once, where N is the priority weight ratio); add high-priority job execution latency as a first-class SLA metric with alerting threshold separate from batch job latency .

Elasticsearch index CDC consumer lag > 10s; search results showing items that no longer exist or missing recently published items; CDC connector health dashboard showing processing rate below write rate

Tier 1: Index Freshness Degradation: CDC consumer or Elasticsearch bulk indexer not keeping pace with PostgreSQL write rate. Recommended evolution: Increase Elasticsearch bulk indexer thread count; tune bulk index batch size and flush interval; profile CDC connector bottleneck (network vs Elasticsearch write throughput vs mapping complexity) .

Elasticsearch JVM heap usage > 75% sustained; GC pause events visible in cluster logs; query p99 latency spikes during GC; cluster health showing yellow (unassigned shards during GC recovery)

Tier 2: Search Cluster Heap Pressure: Large aggregation queries or high document count per shard exceeding JVM heap budget. Recommended evolution: Increase Elasticsearch heap to 50% of node RAM (max 30GB for ZGC); reduce shard count to keep per-shard document count < 50M; disable dynamic mapping and explicitly define all field types; move to doc values for all non-analyzed fields .