Start by clarifying the scenario: a conversation spanning two days means messages arrive with timestamps across multiple days, and the pipeline must handle late-arriving data and reprocessing without duplicating results. Then propose a design that uses deterministic keys (e.g., conversation_id + message_id) and idempotent operations (upserts, deduplication, or exactly-once semantics) to ensure that re-running the pipeline for any day produces the same final state.
Pro tip: Emphasize that idempotency is about the final state, not the process—so focus on making writes idempotent (e.g., using MERGE/UPSERT with unique keys) rather than trying to prevent duplicate processing entirely. Also mention that you'd validate idempotency with tests that replay the same data and assert no changes.
Ask about the data sources, expected volume, latency requirements, and whether exactly-once processing is needed. Confirm what 'two days' means: messages arriving late, or a single conversation being processed across two batch runs?
Design a schema with a unique composite key (e.g., conversation_id, message_id, timestamp) and store the raw events immutably. This allows deduplication and reprocessing without ambiguity.
Use upserts (INSERT ... ON CONFLICT DO UPDATE) or MERGE statements to ensure that reprocessing the same message does not create duplicates. For aggregations, use idempotent operations like overwriting partitions or using delta tables with merge.
Implement watermarks or allowed lateness in stream processing, or use batch reprocessing with partition overwrites. Ensure that updates to past days trigger recomputation of affected aggregates idempotently.
Write tests that replay the same input multiple times and verify the output remains unchanged. Monitor for duplicate keys or unexpected row counts in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.