A high-volume 311 topic must scale across workers while preserving order for each citizen request key. What Kafka mechanism provides that balance?
Select an answer to reveal the explanation.
Short Explanation
Partitions are like multiple checkout lanes: the store moves faster overall, but each lane still keeps its own line in order. Same idea for keyed civic events.
Full Explanation
A high-volume 311 topic scales across workers while preserving order for each citizen request key by splitting into partitions for parallelism while retaining order within each partition. Keys hash to partitions so related events stay ordered without global serialization. A single global lock that serializes the entire city’s events destroys throughput. Deleting ordering guarantees to maximize chaos breaks case timeline integrity. Storing every message only in browser local storage is not a cluster scaling mechanism. Exam caveat: order is per partition, not across the whole topic—choose keys (for example request id) that match the ordering domain you need. Operational check: publish ordered events for two request keys, consume with multiple workers, and verify per-key order holds even as partitions process in parallel. In production terms for partitions for parallel ordered ingest, assign an owning team, wire a dashboard that shows the pattern is healthy under peak civic load, and require a staging drill that rehearses failure and recovery before resident impact. Revisit the choice when traffic shape, retention, or integrity needs change, and keep a named rollback owner on the change record. Re-test both the happy path and the failure path for partitions for parallel ordered ingest after material dependency upgrades so regressions are caught before the next public release.