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 '. Audit and Compliance Platform: triggered by 'Compliance audit finding that audit records were modified af'.

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, '. Audit and Compliance Platform: triggered by 'Compliance report generation taking > 5 minutes against Post'.

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 → '. Audit and Compliance Platform: triggered by 'SIEM consumer lag causing it to fall behind retention window'.

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 .

PostgreSQL write p99 > 20ms with low connection count; pg_stat_activity showing transactions serialized on the same partition's chain-tip read; ingestion throughput plateauing well below hardware limits; auto_explain showing sequential scan on audit_events for "SELECT hash FROM audit_events ORDER BY id DESC LIMIT 1"

Tier 1: Integrity Chain Write Serialization: Per-partition chain-tip read before each insert serializing concurrent audit writers. Recommended evolution: Introduce partition-level chain sequence tables: a single row per partition tracking the current chain tip with an advisory lock, eliminating the full table read. Alternatively, shard the integrity chain by source system or tenant, accepting per-shard chains rather than a single global chain. Use PostgreSQL INSERT ... RETURNING with sequence-assigned IDs to eliminate the pre-insert read entirely, deferring chain hash computation to an async integrity sealer that appends hashes in order without blocking the write path. .

Compliance investigator queries returning in > 30s; PostgreSQL showing high sequential scan counts on audit_events partitions; investigator-facing API p99 > 10s; pg_stat_statements showing actor_id-scoped queries without partition pruning in the query plan

Tier 2: Actor Query Full-Partition Scan: Missing secondary index table for actor_id and resource_id lookup paths across time-partitioned audit data. Recommended evolution: Build a secondary index table audit_events_by_actor(actor_id, event_time, event_id) populated synchronously on insert. Accept the additional write per event as the cost of O(log n) actor-scoped queries. Alternatively, route actor-scoped queries to ClickHouse where columnar storage makes actor_id filters efficient without a secondary B-tree index. .