Scenario 4: Explain a Multi-Function PromQL Query

Context

A new team member is reviewing an existing Grafana dashboard and encounters a complex PromQL query they do not understand. They need a clear plain-English explanation before they can maintain or modify it safely.

Query Under Review

histogram_quantile(
  0.95,
  sum by (le, job) (
    rate(http_request_duration_seconds_bucket{job="checkout-service"}[5m])
  )
)

Task

Produce an explanation file query-explanation.md that covers:

1. What metric is being queried — name, inferred type, and what it measures.
2. What each function does — explain rate(), sum by(), and histogram_quantile() in plain English in the context of this specific query.
3. The calculation performed — describe step-by-step what the query computes, starting from the innermost expression.
4. Output Labels — list which labels appear in the result vector.
5. Expected Result Structure — state whether the result is an instant vector, range vector, or scalar, and how many series to expect.
6. Common Pitfalls — note at least one mistake to avoid when modifying this query (e.g., incorrect aggregation that loses the le label).

Output Specification

Produce a single file query-explanation.md with all six sections.