First identify what is being duplicated: separate job records created for one event, competing workers claiming one stored job, a business action repeated after a retry, or recurring executions that are skipped or overlap. Each has a different fix. JobRunr documents optimistic locking for coordinating workers, but that does not deduplicate separate enqueue requests or guarantee exactly-once effects in an external system.
Identify where the duplicate or delay occurs
Use the dashboard and persisted job history to check whether the problem is at job creation, processing, an external side effect, or a recurring schedule. JobRunr’s introduction describes its persistent job storage, server processing, retries, and dashboard; its FAQ explains worker coordination and common recovery cases.
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| What you observe | Likely layer | First check |
|---|---|---|
| Multiple job records for one message or event | Producer/enqueue boundary | Check whether multiple listeners or application nodes received and scheduled the same event. |
| One stored job appears to be claimed by multiple workers | Storage-backed worker coordination | Review job history, server activity, and storage connectivity. |
| One job record, but an email, payment, or other external action happened twice | Business side effect during retry or recovery | Check whether the action completed before the process stopped or the job failed. |
| Recurring run is missing, late, or overlapping | Recurring registration and cadence | Check recurring ID, schedule, poll interval, downtime behavior, and overlap rules. |
| Job remains in PROCESSING | Worker, server health, storage, or orphan recovery | Check server heartbeats, logs, storage reachability, job history, and shutdown behavior. |
Prevent duplicate job creation at the producer
Optimistic locking addresses competing attempts to process a stored job; it cannot merge two distinct jobs that were independently enqueued for the same event. JobRunr’s FAQ describes a load-balanced service-bus scenario in which multiple listeners may receive the same message and each enqueue a job.
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Where a message bus can redeliver an event or deliver it to multiple listeners, derive a stable identifier from the source message, such as its correlation ID, and pass it through the scheduling path. That gives the application a consistent key with which to deduplicate at the producer boundary. Validate the behavior against the scheduling API and JobRunr version in use.
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Choose the desired duplicate semantics
JobRunr Pro documents JobIdentifier create-once behavior and enqueueOrReplace when the desired result is to keep the latest job for an identifier. These are Pro capabilities; do not assume they are available in the OSS edition. Decide whether a repeated event should be ignored or should replace pending work before selecting a mechanism. See the official enqueueing jobs documentation.
Make business effects safe to repeat
Retries and recovery can execute a job again even when some work from its previous attempt already happened. For example, a process can stop after submitting a payment request or sending an email but before recording completion. The recovered job may then repeat that action; JobRunr’s storage coordination is not a guarantee of exactly-once behavior in an external service.
Where the receiving system supports it, send a business idempotency key with the side effect and have the receiver reject or coalesce repeats. Otherwise, maintain application-level deduplication or a durable record around the action. Place the protection at the boundary where the irreversible effect occurs, rather than relying only on the job’s state.
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Keep recurring-job registrations stable
Recurring schedules have their own identity and timing rules. JobRunr’s recurring jobs documentation explains that a recurring job ID identifies its definition: registering again with the same ID updates that definition, while changing the ID can leave a second definition registered.
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Audit IDs and obsolete registrations
- Assign each recurring schedule an explicit, stable ID rather than generating a new ID at application startup.
- Check for registration code that runs on every startup and whether its identifier has changed between releases or nodes.
- When removing or renaming a programmatically registered recurring job, explicitly clean up the obsolete registration.
Check cadence, downtime, and overlap expectations
In JobRunr OSS, recurring occurrences missed while all servers are down are skipped. JobRunr Pro documents a catch-up feature for scheduling skipped occurrences. Under the documented default recurring behavior, a new occurrence is not created while the previous instance remains scheduled, enqueued, or processing; Pro also documents a configurable concurrency cap through maxConcurrentJobs.
Confirm the schedule’s timezone and registration behavior when an occurrence seems missing or shifted. The deployment guide gives a default polling interval of 15 seconds and warns that a poll interval longer than a frequent recurring interval can result in multiple instances launching together to catch up. Keep polling cadence and the most frequent recurring period aligned, and confirm version-specific behavior for the deployed release.
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Diagnose a job that remains in PROCESSING
A long-looking PROCESSING state is a clue to investigate, not proof that a job is broken. The JobRunr FAQ describes the IllegalThreadStateException message “Job was too long in PROCESSING state without being updated” as a case associated with stopping a JVM while it was processing a job; another server may later pick up the orphaned job. A recovered attempt can start the work again.
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- Confirm that at least one
BackgroundJobServeris enabled and active for processing; a dashboard or storage connection alone does not establish that a worker is running. - Review server health, recent heartbeats, logs, and job history around the time processing stopped.
- Verify that the configured storage provider is reachable and that database connectivity is healthy.
- Check whether a deployment or JVM stop interrupted the job, and compare the exception threshold with the job’s expected runtime and the deployed version.
JobRunr documents a default retry count of 10 in its FAQ, with configuration available to change it. Treat that as a version-sensitive default, not a promise that every job will be retried exactly ten times in every deployment; verify the effective configuration for your release.
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Reduce recovery problems during shutdown
JobRunr’s deployment guidance notes that unfinished work may be interrupted during graceful shutdown and retried from the start. Configure the shutdown wait and the orchestrator’s termination grace period so a worker has a realistic opportunity to finish its current work. If jobs regularly exceed that window, expect interruption and make their effects safe to repeat.
For a shutdown-related orphan, correlate the job history with deployment events, process logs, and heartbeat timing. Do not treat every long-running job as an orphan: compare its actual progress and workload with the platform’s recovery threshold and the application’s expected execution time.
Tune polling and worker capacity after finding the bottleneck
The documented default polling interval is 15 seconds. Lowering it can reduce the wait before a server discovers available work, but it increases database polling and does not necessarily raise job throughput. Change it only after distinguishing pickup latency from a backlog caused by limited workers, slow jobs, unavailable storage, or downstream limits.
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Quick Recap
- Check queue depth and job duration alongside server health, logs, and metrics.
- Adjust worker count to the workload and database capacity; more workers can increase contention or overload a constrained downstream service.
- Review external API or service rate limits before adding servers or concurrency.
- Verify effective polling and worker settings for the deployed JobRunr version and storage provider.
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