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What a heartbeat should prove
A scheduler can invoke a target successfully even if the application later fails to process a payment or order. Conversely, a scheduler-level error tells you an invocation failed, but does not by itself describe the state of work already underway. Track both layers.
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At the application layer, persist a run record with a unique run identifier, job name, start time, completion time, terminal outcome, and enough context to find the affected work. Maintain a separate last_success_at value and update it only after the job reaches its defined success checkpoint—for example, after the intended set of eligible records has been processed and the resulting application state has been durably saved. A process-start event or scheduler acknowledgment is not a success heartbeat.
Choose the checkpoint to match the job. If a run can partially complete, record that state explicitly rather than marking the whole run successful simply because one item finished. Use durable run history to investigate individual outcomes; use the last-success timestamp as a compact liveness signal.
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Choose cadence and staleness threshold
Write down the expected interval, the schedule’s timezone, and how long completion may reasonably take. Alert when last_success_at is older than the expected cadence plus an intentional grace period. Base that grace period on the business deadline and observed job runtime; there is no universal threshold specified by the scheduler documentation.
For example, a job expected once per hour may have a business window that tolerates a late completion. Set the alert threshold to reflect that window, not merely the exact scheduled minute. Make the alert actionable: include the job, last successful completion, latest run identifier and outcome, and links or identifiers operators need to inspect its work.
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Make retries and replay safe
Retries create repeated opportunities to process the same work. Amazon Web Services describes EventBridge Scheduler delivery as at least once: “at least one delivery succeeds with a response from the target.” That does not guarantee the target performed the intended payment or order operation exactly once.
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Use a stable business-operation key for the same logical action and enforce durable state checks before changing payment or order state. A retry or operator replay should recognize work already completed and avoid issuing the same business action again. Keep the key stable across attempts that represent the same operation; use a new key only for a genuinely new operation. These are application-design recommendations derived from repeated-delivery semantics, not a gateway-specific API prescription.
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Set bounded retries and define a final-failure destination, such as a dead-letter queue (DLQ) where supported. Operators should inspect exhausted failures and the corresponding application run state before replaying anything. Scheduler retries do not replace application-level recovery or reconciliation.
Set up the job in a safe sequence
- Define the job contract. Document the expected interval, timezone, acceptable completion delay, success checkpoint, and what counts as partial or failed work.
- Persist execution state. Create a run identifier for each execution and store start time, terminal status, and completion time. Update
last_success_atonly after the chosen business checkpoint is complete. - Protect business actions. Use stable operation keys and durable state checks so duplicate delivery, retry, and manual replay do not repeat a charge or order action.
- Configure the actual scheduler. Use its documented expression dialect and timezone behavior; do not assume that cron syntax, precision, delivery, or retries are portable between products.
- Bound failure handling. Set retry limits appropriate to the business window, configure a DLQ or equivalent final-failure path where available, and ensure someone monitors it.
- Alert on both layers. Monitor stale application success, scheduler target errors, and failed or dead-lettered invocations. An invocation metric is not a substitute for the application heartbeat.
- Exercise recovery safely. In a safe environment, check missed schedules, target failures, retries, duplicate delivery, and operator replay. Verify that run history distinguishes incomplete work from successful completion.
Use the cron dialect and timezone of your scheduler
AWS EventBridge Scheduler
EventBridge Scheduler supports at, rate, and cron expressions. Its cron expression has six fields: minutes, hours, day of month, month, day of week, and year. The API also provides a schedule timezone, target, and retry policy. See the CreateSchedule API reference for the expression and configuration details.
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EventBridge Scheduler’s documented retry-policy bounds are a maximum event age of 60 to 86,400 seconds and a maximum retry-attempt value of 0 to 185. Retries use exponential backoff until the configured attempt count or event-age limit is reached. These are API limits, not recommended settings; choose bounds that fit the job’s business window. The RetryPolicy API reference documents the limits and behavior.
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The EventBridge Scheduler user guide identifies InvocationAttemptCount, TargetErrorCount, and, when a DLQ is configured, InvocationsSentToDeadLetterCount as metrics to monitor. Consult the current EventBridge Scheduler User Guide for metric details and availability. Pair these scheduler signals with the application’s completion record.
GitHub Actions scheduled workflows
GitHub Actions uses POSIX cron syntax for scheduled workflows, unlike EventBridge Scheduler’s six-field expression. GitHub’s scheduled-workflow minimum interval is once every five minutes, and schedules default to UTC. GitHub also documents that scheduled workflows run against the latest commit on the default branch. The workflow file must exist on that branch. See GitHub’s events that trigger workflows documentation.
GitHub warns that scheduled events can be delayed during periods of high load and, under sufficient load, queued runs may be dropped. Public-repository schedules are automatically disabled after 60 days without repository activity. These properties make a scheduled workflow a poor choice for a precise payment execution clock unless the design accounts for delays and includes reconciliation. Do not treat the fact that a workflow was scheduled or started as proof that its business work finished.
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Decide what to monitor and what operators do next
- Application completion: stale
last_success_at, latest run status, and any partial-work state. - Scheduler delivery: invocation attempts and target errors; for EventBridge Scheduler, also monitor DLQ delivery metrics when a DLQ is in use.
- Final failures: DLQ or equivalent queue depth and age, plus an alert path that reaches the team responsible for the job.
- Recovery: inspect run history and reconcile payment or order state before replaying work. Confirm whether an operation already completed before triggering it again.
Thresholds, heartbeat schema, and runbook steps are implementation choices, not values prescribed by the cited AWS or GitHub documentation. Payment-provider idempotency rules and reconciliation procedures also vary by provider and jurisdiction; consult the provider’s current official documentation before implementing provider-specific behavior.
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