A failure alert from a metrics API query should prove which rule and tenant cohort were evaluated, what interval and data the query covered, what the evaluator decided, whether that data was fresh, and whether notification was attempted. Keep the observation, decision, incident state, and delivery attempt linked by a stable incident key and a unique evaluation ID. That makes it possible to distinguish a new failure from a repeated evaluation, a query problem, or a delivery retry.
What should a failure alert from a metrics API query prove?
An alert is not just a threshold and a number. When someone investigates it later, they need enough evidence to reconstruct the path from scheduled poll to notification:
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- Scope: the rule, its configuration version, and the tenant or cohort it covers.
- Input: the requested query interval, dimensions, aggregation, and relevant observed values.
- Data quality: whether the query succeeded and whether the newest usable sample was fresh enough for the rule.
- Decision: the evaluator outcome and reason, including whether the incident changed state.
- Delivery: whether a notification was attempted and how the receiver responded.
A stored metric value without its interval, dimensions, freshness, and evaluation outcome cannot reliably explain why an alert fired. Keep the detailed evidence in an access-controlled record when tenant-level data is sensitive or should not be exposed in broadly visible metric labels.
Give every evaluation and incident a durable identity
Use an incident key for the continuing condition
Build a stable incident key from the identity of the rule and the relevant tenant or cohort. Repeated scheduled evaluations of the same condition should resolve to the same incident key. This lets an operator find the history of a condition rather than treating every poll as an unrelated alert.
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Use an evaluation ID for one scheduled run
Generate a unique evaluation ID for each poll evaluation. Store it with the incident key so a reviewer can distinguish the individual query and decision from the incident’s longer history. These are recommended design identifiers, not names mandated by a metrics API standard.
Also record the rule or configuration version used for the run. If the rule changes later, the stored version helps explain why the historical evaluation reached its particular result.
Make each poll cycle reconstructable
Treat polling, evaluation, state transition, and notification as explicit stages. Persist the decision before attempting delivery so a notification failure does not erase evidence that the rule fired.
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- Create an evaluation: assign the evaluation ID and record the scheduled run time, incident key, rule/configuration version, and applicable tenant or cohort.
- Issue a documented query: save the requested time interval, dimensions, aggregation, and query or request identity needed to understand what was asked of the API.
- Classify the response: distinguish a transport or query error, missing data, stale samples, invalid results, and a valid observation. Do not turn an error or absent sample into a fabricated zero.
- Evaluate the rule: record the observed value or an appropriately protected summary, the evaluator outcome, and a human-readable reason.
- Persist the decision and transition: store prior and next incident state, state version, and the evidence supporting the transition.
- Attempt notification separately: record the idempotency key, attempt time, and response classification for each delivery attempt.
A useful evaluation record can contain the following fields. The exact storage format depends on the system; these names are descriptive rather than a required schema.
- Incident key, evaluation ID, rule/configuration version, and tenant or cohort identifier.
- Requested query interval, dimensions, aggregation, and query/transport outcome.
- Raw result or a suitably protected summary, newest sample timestamp, and freshness calculation.
- Evaluator result and reason, prior and next incident state, and state version.
- Notification idempotency key and a separate record of delivery attempt times and response classifications.
Keep detailed tenant evidence in a store with appropriate access controls and retention rules. Bounded metric labels can help limit cardinality, but the right label strategy depends on the backend and data policy; metric labels should not be assumed to serve as a durable audit trail.
Handle query errors, missing data, and stale telemetry as distinct outcomes
A failed query says that the evaluator could not establish the condition from that request; it does not establish that the metric was zero or that the service was healthy. Missing data and stale data are also different: there may be no usable observation, or an observation may exist but be too old for the alert’s purpose. Keep these outcomes distinct from a valid value that does or does not meet the failure rule.
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Choose freshness rules based on the expected telemetry behavior and the service objective. Record both the newest sample timestamp and how freshness was assessed, so a later reviewer can see why an observation was accepted or rejected. Avoid treating a stale positive reading as proof of a current failure.
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Operational telemetry can reveal failures in the machinery that supplies alert evidence. Supabase Realtime’s observability inventory lists per-tenant measures for poller stops and crashes, query and preparation exceptions, dispatched rows, and skipped rows; it lists /tenant-metrics as a per-tenant endpoint. These are Supabase Realtime-specific signals, not a universal tenant-metrics schema.
Grafana Tempo documents polling-health measures including tempodb_blocklist_poll_errors_total, tempodb_blocklist_poll_duration_seconds, tempodb_blocklist_length, tempodb_blocklist_tenant_index_errors_total, tempodb_blocklist_tenant_index_builder, and tempodb_blocklist_tenant_index_age_seconds. Its documentation describes how stale poll-driven index state can affect which blocks are queryable. These examples illustrate why monitoring the evidence-supply mechanism matters alongside monitoring the underlying condition.
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Check the metric’s contract before deriving a time-window value
If an API returns a cumulative counter or only one aggregate point, verify its documented semantics before calculating a time-window delta. Do not assume that a point is a window total, or that subtracting two values is valid, without confirming how the source handles resets, aggregation, and timestamps.
Model incident state and concurrent evaluations explicitly
For stateful thresholds, define what each state means and which evidence causes a transition. A policy may use pending, firing, resolved, and an unknown or insufficient-data state, but the names and transitions should match the service’s needs.
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- Firing: the rule’s failure condition is met under the selected policy.
- Resolved: new valid evidence meets the recovery condition.
- Unknown or insufficient data: the evaluator cannot make a reliable determination because data is missing, stale, invalid, or unavailable.
If the policy requires consecutive failures, choose that threshold from the service objective and observed telemetry; there is no universally correct count or polling interval. Record the evidence and state version at each transition. On recovery, preserve the recovery evidence and decide explicitly whether the system sends a resolution notification.
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When multiple pollers can evaluate the same incident, guard state changes with compare-and-set or another atomic update. Otherwise, concurrent runs may overwrite each other’s state or issue notifications based on a transition that another poller has already recorded.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate alert decisions from webhook delivery
Evaluation and notification are different operations. Record that a rule fired even if the webhook request fails, and keep each delivery attempt separate from the decision record. This lets an operator answer three different questions: did the rule fire, was a request attempted, and did the receiver acknowledge it?
Check the actual receiver contract for idempotency. Unless it guarantees otherwise, design for retryable requests that may be delivered more than once. Use a stable idempotency key tied to the incident transition or notification event, and record each attempt’s timestamp and response classification. A retry of the same notification should be distinguishable from a new failure transition; do not create a new incident merely because delivery is being retried.
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Choose managed alerting or a custom poller by the evidence you need
Metric alert products expose their own evaluation and payload controls, while a custom poller gives the team responsibility for query behavior, state, persistence, and delivery. Neither approach is universally superior. Compare them against the operational and reconstruction requirements:
| Decision area | Questions to answer |
|---|---|
| Query control | Can you set and retain the query interval, aggregation, and dimensions needed to reproduce the evaluation? |
| Tenant and cohort scope | Can the system evaluate the required tenant/cohort dimensions without exposing sensitive detail or creating unsuitable metric cardinality? |
| Freshness and no-data behavior | Can you distinguish stale or missing telemetry from a healthy value and a query failure? |
| Decision evidence | Can you retain the input, rule/configuration version, evaluator reason, and state transition for later review? |
| State and suppression | Can you configure pending, firing, recovery, and suppression behavior to fit the service objective? |
| Payload context | Can the receiver get useful incident and evaluation context without treating the payload as the only audit record? |
| Delivery and concurrency | Are retries, duplicate handling, idempotency, and competing evaluations controlled and observable? |
| Operational limits | Who owns the poller or alert configuration, and do API/series limits and cost fit the expected polling frequency? |
Azure Monitor documents metric-alert criteria and dimensions, static thresholds and aggregation types, dynamic-threshold failing periods, and custom properties that can be appended to webhook payloads. Its documentation also describes the minimum number of violations within a selected lookback window required to raise an alert. These are product-specific controls to compare with a custom evaluator’s requirements, not guarantees that all metrics APIs behave alike.
For log-based metrics, Microsoft Application Insights offers the ai.include-query-payload option to return the KQL statement used to fetch a metric alongside time-series data. That can preserve useful query provenance in that Microsoft-specific context; it is not a general capability of metrics APIs.
Quick Recap
Use a practical acceptance checklist before relying on the alert
- A reviewer can identify the exact rule/configuration version, tenant or cohort, query interval, and evaluation ID.
- Query failure, missing data, stale data, invalid output, and valid observations have different recorded outcomes.
- The alert decision and incident-state transition are persisted independently from notification delivery.
- Every notification attempt has a traceable idempotency key, timestamp, and response classification.
- Retries cannot be mistaken for new failure transitions, and concurrent evaluators cannot silently overwrite incident state.
- Detailed tenant evidence is accessible only to the appropriate audience, and metric labels are not mistaken for the audit record.
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