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A healthy scheduler heartbeat does not mean the expected job ran, finished successfully, produced correct output, or did so before the result became stale. Treat component health and work health as separate things: monitor whether each scheduled run appears, starts, completes, passes output checks, and meets its freshness or business deadline.
What a scheduler health check actually tells you
A heartbeat or liveness check answers a narrow question: is a scheduler component responding within its configured health threshold? It is not an end-to-end check of a particular run or its output.
Apache Airflow’s stable health-check documentation, showing version 3.3.2, describes a scheduler status derived from the latest scheduler heartbeat. Its default threshold is 30 seconds, but the threshold is configurable. In a multi-scheduler installation, the scheduler can be reported healthy when at least one scheduler is working. The endpoint also reports other component statuses, including the metadatabase, triggerer, and DAG processor. Airflow health-check documentation
Airflow cautions that the endpoint’s HTTP status code indicates whether the REST call succeeded; it should not be used alone to judge application health. The webserver-served endpoint also depends on a working webserver, so a check routed through it may not independently diagnose that layer.
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How green health and stale output can coexist
A scheduled workload passes through multiple states. The scheduler may be alive while an expected occurrence is never created; a run may exist but remain queued; execution may fail; or a technically successful run may produce empty, invalid, or outdated output. A result can also arrive after its useful deadline. These are different failure modes and need different checks.
- Component liveness: the scheduler is responding.
- Occurrence and execution: the expected run was created and started.
- Completion: the run reached a successful terminal state before its deadline.
- Correctness and freshness: the output is valid and current enough for its consumers.
Google’s SRE guidance makes the distinction between a successful response and a useful result: a response that appears successful but contains the wrong content is still an implicit error. End-to-end checks can catch wrong content that infrastructure-level health checks miss. Google SRE: Monitoring Distributed Systems
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Scheduler policies can delay or skip an occurrence
Kubernetes CronJobs
Kubernetes CronJob scheduling is approximate; depending on circumstances, an occurrence can be missed or duplicated. The documentation recommends making Jobs idempotent so repeated execution does not cause harmful effects. A created Job should also be associated with its intended schedule time rather than assumed to represent a fresh, on-time run.
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startingDeadlineSecondssets the whole-seconds limit for starting a Job after its scheduled time. Once that deadline passes, Kubernetes skips the occurrence. If the field is unset, there is no such deadline.concurrencyPolicy: Forbidskips a new occurrence when the prior Job is still running.
The Kubernetes documentation, last modified May 13, 2026, says the CronJob controller checks on a 10-second cadence and warns that a startingDeadlineSeconds value below 10 seconds may prevent scheduling. If more than 100 schedules have been missed in the relevant counting window, the controller does not start the Job and logs an error. These are Kubernetes-specific behaviors, not universal scheduler rules. Kubernetes CronJob documentation
On Kubernetes v1.32 and later, a created Job receives the batch.kubernetes.io/cronjob-scheduled-timestamp annotation with the original scheduled time. That timestamp can help monitoring compare intended and actual timing.
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Airflow runs and deadlines
Airflow distinguishes a DAG run’s logical date and data interval from its actual start and end times. A run’s logical date therefore does not by itself establish when processing began or whether it completed on time. Airflow’s Deadline Alerts, introduced in version 3.1, let a DAG respond when a run exceeds a configured time threshold. They can monitor run-level timing, but they do not replace checks that validate the resulting data. Airflow DAG run documentation
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Build monitoring around the work, not just the scheduler
Use separate signals so an alert identifies which stage has failed. The checks can run in the scheduler platform, an external monitor, or a consumer-side freshness monitor; make sure the check does not rely entirely on the same component it is intended to diagnose.
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- Check component liveness. Track heartbeat age or the scheduler’s health endpoint to detect an unresponsive component. Treat this as a component check only.
- Check expected-run arrival. Calculate when the next occurrence is due, allow a defined start delay, and alert if the expected run has not appeared or started within that window. Where supported, compare the run’s scheduled timestamp with its creation or start time.
- Check execution state and deadline. Record whether the run succeeded, failed, or remains queued or running. Alert on a missed run-level completion deadline, not just a missed heartbeat.
- Validate the output. Check a workload-appropriate invariant, such as a new timestamp, expected partition, reasonable row-count range, or domain-specific condition. A process exit code alone cannot establish that the result is useful.
- Check freshness and capacity. Measure the age of the latest valid output as seen by its consumers. When a run starts or finishes late, inspect queue time, worker health, and constrained resources to distinguish scheduling delay from execution bottlenecks.
Google’s SRE monitoring framework describes four golden signals: latency, traffic, errors, and saturation. For scheduled work, adapt those concepts to the workload: runtime and queue latency, expected run volume, failures or invalid results, and resource saturation. Google SRE: Monitoring Distributed Systems
Set alert thresholds from the work’s usefulness window
There is no universal acceptable delay. Set the allowed start delay and completion deadline from the schedule frequency, typical runtime and retries, the time the output remains useful, and the needs of downstream consumers. A late backup may still be valuable, while a report refresh that misses its delivery window may no longer serve its purpose.
Decide explicitly whether overdue work should be skipped, caught up, or run late. Align that choice with scheduler policy, idempotency, concurrency, and downstream behavior. A retry or catch-up can restore missing work, but it may also create duplicate processing, contention, or a result that arrives too late to matter.
Design alerts that identify the failure stage
Use distinct alerts for stale scheduler heartbeat, missing expected occurrence, delayed start, failed or overdue completion, invalid output, and stale consumer-visible data. Give each alert the relevant run or scheduled time, last successful output time, and the deadline it breached. This helps responders tell a control-plane problem from queueing, worker failure, or a data-quality issue.
Review the scheduler’s own rules before interpreting an alert. Deadline semantics, catch-up behavior, concurrency, and missed-run handling vary across platforms and can change by version or configuration. A green component check is useful evidence about that component—not proof that the scheduled work is current and correct.
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