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To alert when at least N deliveries fail within a time window, count terminal failures, evaluate that count on a schedule, and persist whether the alert condition has already been announced. The metric answers how many failures occurred; durable alert state prevents every cron run from generating another notification. Choose a custom poller when this is a focused application-specific need. Use a native alerting stack when you also need routing, grouping, silences, or escalation policies.
Define the failure condition before writing the poller
Be precise about what counts as a failure. For delivery monitoring, the useful event is usually a terminal delivery failure—not every temporary error that may succeed on retry. Then define the question your alert should answer: did at least N qualifying failures occur in the interval ending now?
Keep the threshold, window, and evaluation cadence distinct. The window determines which events count; the cadence determines how often the worker checks; the threshold determines when the condition becomes active. A scheduled poller cannot detect a threshold crossing until its next evaluation. Choose these values based on expected volume, user impact, acceptable detection delay, and tolerance for transient noise rather than treating any one setting as universal.
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Choose a metric that matches the question
Count failures for volume thresholds
A counter is appropriate for recording failures. Prometheus defines a counter as a cumulative value that can increase or reset to zero on restart, and its instrumentation guidance recommends incrementing it when a failure occurs. See Prometheus metric types and instrumentation practices.
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A lifetime counter total does not by itself answer how many failures occurred in a recent window. The query must derive the increase over the chosen interval using the semantics of your metrics backend. Counter resets, missed scrapes, duplicate event recording, and overly variable labels can affect the result, so test the actual aggregation against those cases. No backend-independent query expression is established here.
Count attempts too if you need a rate
If the operational question is whether delivery reliability has degraded, record attempts as well as failures and compare both over the same interval. A failure count and a failure ratio answer different questions: a fixed number of failures may be serious at low volume but ordinary at high volume.
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Use durable state to prevent repeated notifications
The metric is measurement; the alert record is notification history. Persist enough state to establish whether the condition is inactive, active, or recovering, and whether the corresponding transition has been announced. The scheduled worker can then evaluate the current metric while consulting durable state instead of interpreting every poll as a fresh event.
There is no single required schema or locking method. Design the state transition around the failure points in your own delivery path:
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- Choose an idempotency key or deduplication boundary for each alert transition.
- Decide how concurrent worker runs claim or coordinate the same alert.
- Define what happens if the worker records a transition but crashes before sending, or sends and crashes before recording completion.
- Specify how retries, timeouts, and terminal notification failures affect state.
- Define how recovery is detected, recorded, and communicated.
Do not promise exactly-once notification unless the implementation can provide that guarantee across its storage and delivery boundaries. State the actual behavior—for example, whether a retry can produce a duplicate—and retain recipient, message, and detailed error information in logs or an audit record rather than putting arbitrary text into metric labels.
Choose a cadence and persistence policy
Evaluate often enough to meet the acceptable detection delay, while accounting for the additional query and worker activity a tighter schedule creates. Prometheus offers a related approach in alert rules: a for clause requires an expression to remain active for a configured duration before firing, and keep_firing_for can delay resolution after the expression stops matching. The latter can help reduce flapping and false resolutions caused by data loss. See Prometheus alerting rules.
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Prometheus guidance recommends allowing slack for small blips and alerting on symptoms tied to user impact. For batch jobs, it advises alerting when a job has not succeeded recently enough to cause user-visible problems, with a threshold that generally allows at least two full runs. That is batch-job guidance, not a universal threshold for delivery counters. See Prometheus alerting practices.
Monitor the scheduled worker itself
A poller that stops running cannot report the failures it was built to detect. Record successful check-ins or otherwise monitor whether the worker runs on its expected cadence; a heartbeat monitor can detect missed check-ins. Treat this as a separate signal from the delivery-failure threshold.
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Also test the notification path end to end. Prometheus guidance recommends a black-box check of the path through Pushgateway, Prometheus, and Alertmanager to email, rather than relying only on separate component checks. If you use Grafana, its alerting meta-monitoring documentation describes collecting alerting metrics, including alert states and scheduled-rule counts.
Decide whether a custom poller or native alerting fits
A custom counter-and-poller can be a focused, auditable solution when the condition is application-specific and the team is prepared to own durable state, retries, recovery, and worker health. A monitoring stack becomes more attractive when alert routing and notification policy are part of the requirement.
| Decision area | Custom poller | Prometheus alerting stack |
|---|---|---|
| Evaluation | Application-specific aggregation over a business-event counter. | Reusable metric expressions evaluated by alerting rules. |
| Alert state | Owned by the application and its durable state mechanism. | Rule evaluation tracks pending and firing alert instances; Prometheus exposes synthetic ALERTS time series. |
| Notification policy | Implemented and maintained in the application’s delivery path. | Alertmanager adds summarization, rate limiting, silencing, and alert dependencies. |
| Operational dependencies | Database or other state store, scheduled worker, and notification path. | Metrics collection, rule evaluation, Alertmanager, and monitoring of those components. |
| Best fit | A narrow, application-owned condition where custom behavior is worth maintaining. | Teams that need established routing, grouping, silences, or broader notification controls. |
Prometheus describes alerting rules as useful for identifying what is broken, while Alertmanager handles notification management. Read the alerting-rule documentation alongside the Alertmanager documentation when deciding whether to implement notification policy yourself.
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