Aglet

Learn From Recurring Duplicate Records

Learning from duplicate records means preserving the distinction between event identity, delivery behavior, and record revision. Record the pair that revealed the gap, the key used, and the output affected. Strengthen examples and ownership so a future repeat can be classified quickly without risky cleanup.

Keep the lesson for the next incident

  1. Document identity and revision

    Write down the logical identity fields, mutable fields, revision semantics, and legitimate repetition cases. Include exact repeats, conflicting copies, and similar distinct examples. State what happens when the key is absent or reused so the contract guides review instead of encouraging silent grouping.

  2. Keep delivery fixtures

    Add compact cases for retries, reordering, delayed delivery, changed revisions, and legitimate repeated events. Assert logical count and downstream totals where they mattered. Preserve source-shaped metadata and expected outcomes so the fixtures explain the failure boundary rather than only the final deduplication result.

  3. Review recurrence

    Assign owners for source keys, ingestion behavior, and affected reporting when responsibilities differ. Review repeat rates and conflicting copies by source or release on a defined cadence. Set a reopening trigger for new identity variants, unexpected count inflation, or a changed repeat pattern.

What to carry forward

Close the learning record with identity and revision rules, delivery fixtures, owners, and a recurrence trigger tied to repeat behavior or count changes. Keep ambiguous historical groups visible. The lasting outcome is faster classification and safer decisions about repeats, not a promise that every source is perfectly idempotent.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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