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Define what counts as a failure
Count a delivery failure only when the delivery attempt reaches the terminal state that matters to your operation. A retryable timeout is not necessarily a terminal failure; if retries are part of the delivery process, decide whether the metric records each failed attempt or only deliveries that remain unsuccessful after retry policy is exhausted. Those choices answer different questions.
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Prometheus defines a counter as a cumulative value that increases, apart from resets such as a process restart, and identifies errors as a suitable use case. Its instrumentation guidance recommends incrementing a counter when a failure occurs and generally tracking attempts as well, so a failure ratio can be calculated. See Prometheus metric types and Prometheus instrumentation practices.
- A lifetime total answers how many failures have accumulated since the counter began.
- A threshold over a recent window answers how many failures occurred during a defined interval.
- A failure ratio compares failures with attempts over the same interval.
For a recent-window alert, use a query and aggregation that match your metrics backend and instrumentation. Counter resets, missed scrapes, duplicate events, and excessive label cardinality can affect results; there is no single backend-independent query established here. Keep high-variability details such as recipient addresses, message bodies, and arbitrary error text in logs or audit records rather than encoding them into metric labels.
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Choose the threshold and polling cadence
Set N and the window according to the impact of delayed detection, normal delivery volume, and the transient noise the team can tolerate. The polling cadence determines how soon a condition can be noticed after it begins: polling more often may reduce detection delay, but increases query and worker activity. Neither a particular threshold nor a particular interval is right for every system.
Prometheus rule evaluation offers a related way to require a condition to persist: a for clause leaves an alert pending until its expression remains active for the configured duration. An optional keep_firing_for can keep it firing after the expression stops matching, which can reduce flapping or 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 suggests alerting when a job has not succeeded recently enough to create user-visible problems, with a threshold that generally allows at least two full runs. That is batch-job guidance, not a universal threshold rule for every failure counter. See Prometheus alerting practices.
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Separate the measurement from the alert state
The metric answers, “How many failures occurred in this window?” A durable alert record answers, “Has this condition, or its transition, already been announced?” Keep those responsibilities separate. Recomputing the metric on each poll is normal; treating each poll as a fresh failure event is not.
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A useful state model distinguishes at least the condition currently evaluated from whether its entry into an alerting state has been announced. Decide whether and how to record a transition back to normal, too. The exact database schema, locking approach, claim mechanism, and recovery protocol depend on the storage and notification system; no one design is established for every implementation.
Review the crash and retry cases
- What identifies one alert transition for deduplication?
- Which state is durable, and which values are recomputed from metrics on each run?
- How are concurrent workers prevented from claiming or announcing the same transition?
- What happens if a worker crashes after recording a transition but before sending the notification?
- What happens if it sends the notification but crashes before recording completion?
- How do timeouts, retries, and terminal notification failures affect the state machine?
- How are recovery or resolution transitions recorded and delivered?
These cases determine the real delivery guarantee. Do not promise exactly-once notification unless the implementation can support that claim; describe its actual retry and deduplication behavior instead. The topic-specific design guidance emphasizes duplicate pages, retries, and recovery transitions, but does not prescribe a universal transaction protocol. See the design discussion.
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Monitor the scheduled worker itself
A poller that stops running cannot report the failures it was meant to catch. Record successful check-ins or otherwise alert when the worker misses its expected cadence. This heartbeat is a separate signal from the delivery-failure threshold: it tells you the evaluator may be absent, not that deliveries have failed.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesPrometheus recommends checking the monitoring system itself. Its alerting guidance describes a black-box test of the path from Pushgateway through Prometheus and Alertmanager to email as a stronger end-to-end signal than relying only on independent component checks. See Prometheus alerting practices. Teams using Grafana can also consult its documentation on collecting alerting metrics, including alert states and scheduled-rule counts, for meta-monitoring: Grafana alerting monitoring.
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When a native alerting stack is a better fit
A focused cron poller can suit an application-specific aggregation and a small notification workflow. If the team needs established routing, grouping, rate limiting, silences, or alert dependencies, compare that custom state machine with a monitoring stack already designed to manage alert instances and notifications.
Prometheus alerting rules evaluate expressions and track active alert instances; pending and firing states are represented by synthetic ALERTS time series. Prometheus documentation also distinguishes rule evaluation from complete notification management: Alertmanager adds summarization, notification rate limiting, silencing, and alert dependencies. See Prometheus alerting rules and Alertmanager.
| Decision area | Cron poller | Prometheus with Alertmanager |
|---|---|---|
| Detection | Application-specific aggregation over a business-event counter. | Reusable metric expressions evaluated by alerting rules. |
| State and deduplication | State machine owned by the application. | Alert state managed by the monitoring stack. |
| Notification policy | Custom delivery behavior. | Grouping, routing, rate limiting, silencing, and alert dependencies. |
| Operational dependency | Database and scheduled worker, both of which need monitoring. | Metrics collection, rule evaluation, Alertmanager, and their meta-monitoring. |
| Maintenance | A smaller, focused implementation whose retry, deduplication, and recovery behavior must be maintained. | A broader stack to configure and operate. |
These are decision axes, not a universal winner. Choose based on the notification policy and operational dependencies your team is prepared to own.
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