Prometheus / Editorial solution guide
A query is empty or vector matching fails
Trace selection, sample windows, grouping, and matching keys before broadening a query or forcing a fallback zero.
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Recognize the symptoms
A panel becomes empty after adding rate, division, or metadata enrichment. A query may report a matching error, return fewer operations than expected, or change after a label migration. Another panel can still show the raw metric because it asks a different type or population question.
Understand the likely causes
- Possible causes include mismatched selectors, sparse range samples, counter or label resets, absent numerator series, changed grouping, nonunique metadata, and incomplete matching keys. Empty output is not inherently zero. Adding group_left or or without a defined relationship can hide missing evidence or create another semantic defect.
Freeze the expression and intended population
Record the complete expression, fixed reference and installed version, evaluation time, range, step, source scope, and expected label set. Keep credentials and private identifiers out of links and notes. State the question in ordinary language: an operation error fraction differs from a current total or selected trace count. Compare the last working contract with the new one. This guide evaluates only fixed Python data, not a live query, and supplies no instruction to broaden access or select every environment.
Add one semantic layer at a time
Start with the intended narrow selector, then examine the range-vector input, function, aggregation, and binary operation in sequence through authorized evidence. Check whether rate has enough relevant samples and whether labels or process identity changed within the window. Compare numerator and denominator scope. A negative matcher can include missing-label series; a broad by list can merge environments, while retained labels can fragment results. Do not assume that a dashboard time range supplies the range input required by a function inside the expression.
Check uniqueness and unmatched keys
The offline fixture separates two synthetic operation rows by whether their target key has metadata. It reveals that reserve lacks a matching record instead of silently treating its disappearance as no traffic. The model does not implement PromQL matching. In actual arithmetic matching, the designated one side must satisfy the documented uniqueness rule. Compare full key scope, missing values, and duplicate metadata at the same evaluation time. Removing another label or choosing the first duplicate can attach the wrong ownership and should not be used as a cosmetic repair.
rows = [("lookup", ("warehouse-lab", "a")), ("reserve", ("warehouse-lab", "b"))]
metadata = {("warehouse-lab", "a"): "stock-team"}
matched = [operation for operation, key in rows if key in metadata]
unmatched = [operation for operation, key in rows if key not in metadata]
print("matched", matched)
print("unmatched", unmatched)
matched ['lookup']
unmatched ['reserve']Only fixed tuple keys are compared. The fixture performs no vector matching, backend query, target discovery, or authorization check.
Repair the contract rather than painting the panel
Choose the narrow correction supported by the failed layer: migrate selectors, restore required instrumentation, align units and grouping, fix metadata identity, or define justified no-error behavior. Keep missing source and no traffic distinct. A fallback zero needs an explicit expected label population and coverage evidence. Version the consumer transition and preserve known-value cases, including unequal traffic and missing labels. This guide makes no query-engine, configuration, or monitoring change and claims no engine result for the local dictionary model.
Verify the fix
Validate expected values and exact result labels with representative synthetic inputs using the correct authorized isolated tool when available. Include missing, duplicate, sparse, reset, and no-traffic cases. Compare the final panel and alert contracts separately. State whether actual PromQL ran or only this local key comparison was checked; numerical agreement alone does not prove population equivalence.
Keep it from recurring
- Maintain versioned metric and query contracts with units, grouping, key uniqueness, timing, and empty-data behavior. Review label migrations with every consumer, and preserve independent semantic fixtures rather than snapshots that merely copy current output. Keep raw and derived series meanings visible so a later editor does not apply a second rate or aggregate incompatible populations.