Aglet

How to learn from API latency variance patterns and tails

API variance learning records which request shapes waited, where their timelines diverged, and how clients were expected to respond. Keep the grouping dimensions and response contract visible so future data, dependency, or query changes can be compared before the tail surprises users.

Keep the lesson for the next incident

  1. Catalog representative request shapes

    Keep structural examples for small and large results, common and selective filters, empty responses, cache states, and relevant statuses without retaining sensitive values. Describe the waiting workflow and the interval that varied. Record observation limits so a narrow sample is not treated as universal.

  2. Document response and retry ownership

    State which response may update client state, when a retry is safe, how timeout differs from server failure, and how duplicate submission is prevented. Pair the contract with a repeatable journey. A latency guard is incomplete if client behavior can amplify the original variance.

  3. Recheck after data and dependency changes

    Review the representative shapes when queries, payload fields, result limits, dependency calls, serialization, or client retry logic change. Compare distributions and interval boundaries again. Add a new shape when a route gains a workflow with different timing sensitivity instead of reusing an unrelated case.

What to carry forward

The learning record should retain request shapes, interval boundaries, client response ownership, retry rules, and recurrence triggers. It is complete when engineers can reproduce the harmful tail, distinguish a data boundary from shared infrastructure, and verify both timing and contract behavior.

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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