Usually the job did not fail in the sense your code would record. During a deploy, the worker process received a stop request. If it could not finish or hand off its active jobs before the platform killed it, those jobs were left half-done. What happens next depends on the queue library, its settings, and how your process handles termination signals. It is not one universal behavior.
The deploy sequence that interrupts work
An interrupted job is almost always the result of a short sequence of events. Each step can go right or wrong independently.
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- The deployment controller or orchestrator sends a termination signal, commonly SIGTERM on Linux, to the old worker process.
- A well-behaved worker stops claiming new jobs. Jobs already in progress keep running.
- The worker waits for active jobs to finish, or a handler checkpoints its progress and exits.
- If the process is still alive when the platform’s grace period ends, the platform sends SIGKILL. A killed process cannot run cleanup code, flush state, or write a final log line.
- The queue eventually notices that the job was abandoned. It recovers, retries, or drops the work according to its own rules.
Most “quietly abandoned” jobs come from step 4 or from a missing step 2. The worker either never stopped taking work, or it stopped taking work but did not wait long enough for the work it had already started.
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An exception is code that runs and returns an error. A SIGKILL is not an exception. The job’s handler does not reach a catch block or an error callback because the operating system removes the process. The symptoms are therefore indirect:
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- The worker’s last log line is an ordinary progress message, with no error and no shutdown message.
- A job that was “active” disappears from the active list, and later reappears as waiting, retried, stalled, or failed, depending on the queue.
- The same job runs twice, once on the old process and once on the new one, with no failure recorded for the first run.
- Nothing is recorded at all, if the queue’s recovery is not configured to re-deliver work.
This is why the problem reads as silent. The failure lives in the gap between the worker’s shutdown path and the platform’s kill deadline, not in application code.
Graceful shutdown and recovery are separate paths
Two different mechanisms are often confused. Graceful shutdown is what the worker does when it is told to stop. Recovery is what the queue does after a job has been interrupted and was not acknowledged as finished. A worker can shut down cleanly and still have recovery behavior that matters, and a worker can be killed and still recover cleanly if the queue is configured for it.
| Path | What triggers it | What it does | Who controls the behavior |
|---|---|---|---|
| Graceful shutdown | A termination signal the worker handles, or a close call in the worker’s code | Stops taking new jobs, then waits for active jobs to finish or fail, or checkpoints them | Your process, its shutdown timeout, and the platform grace period |
| Recovery after interruption | Forced kill, lost connection, or a job that stops renewing its lease or visibility | Returns the job to the queue, hands it to another worker, retries it, fails it, or drops it | The queue library, its version, and its acknowledgement and stall settings |
How the main queue libraries behave
The table below summarizes the documented behavior of three common stacks. The exact semantics are version-specific, so check the documentation for the version you run.
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| Stack | Stop signal or call | Behavior during shutdown | After forced termination |
|---|---|---|---|
| Sidekiq | TSTP, then TERM | TSTP stops fetching new work. TERM begins shutdown within the configured timeout. | Unfinished work is pushed back to Redis when the timeout passes, according to Sidekiq’s deployment guidance. |
| Celery | TERM (warm), QUIT (cold), KILL | Warm shutdown stops the work loop and lets executing tasks finish. Cold shutdown stops active tasks. KILL cannot be caught. | Depends on shutdown mode and acknowledgement settings. Late acknowledgement matters. Not stated as a single outcome. |
| BullMQ | await worker.close() |
Marks the worker as closing so it takes no new jobs, and waits for active jobs to be processed or failed. The call has no timeout of its own. | Killed active jobs can be marked stalled and picked up by another worker, after about 30 seconds by default. A job can be returned to waiting or moved to failed. Jobs that exceed the configured stalled count can fail permanently. |
Sidekiq
Sidekiq’s deployment guidance separates two signals. Send TSTP early, so the process stops fetching new work while current jobs keep running. Send TERM later, so the process shuts down within its configured timeout. The guide says deploy scripts should allow that timeout plus five seconds after TERM. The Sidekiq timeout and the five-second allowance are configuration examples, not universal values. Sending KILL too soon can lose jobs. With Sidekiq Pro’s super_fetch, it can also cause duplicate execution.
Celery
Celery’s Workers Guide documents TERM as a warm shutdown. The worker stops its work loop and lets currently executing tasks finish. QUIT starts a cold shutdown and stops active tasks. KILL cannot be caught by the worker, so any executing task may be lost unless late acknowledgement is configured. Celery’s guide includes version-specific notes, including changes in 5.2, 5.6, and 5.7. If you describe Celery behavior to a team, state the exact version, because the recovery outcome depends on the acknowledgement mode in use.
BullMQ
BullMQ’s graceful-shutdown guide calls worker.close() from your shutdown handler:
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process.on('SIGTERM', async () => {
await worker.close();
process.exit(0);
});Closing marks the worker as closing, so it will not pick up new jobs, and it waits for active jobs to be processed or failed. The call does not time out on its own. If the host’s deadline passes first, the process is killed, and stalled-job handling is the recovery path. BullMQ’s documentation says the QueueScheduler is no longer required for stalled-job recovery from version 2.0 onward. Installations older than 2.0 need version-appropriate guidance.
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Give the worker time to finish: grace periods
A platform’s grace period must cover the worker’s full shutdown path, including the longest in-flight job, the time to stop intake, and any checkpoint write. If the grace period is shorter than the shutdown path, the worker is killed partway through, no matter how well the code is written.
Kubernetes workers
Kubernetes sends SIGTERM to the container’s root process and sends SIGKILL after the pod’s termination grace period. Sidekiq’s Kubernetes guide recommends setting the grace period longer than the Sidekiq timeout. The guide cites 30 seconds as the Kubernetes default. Confirm the effective value on your own cluster, because clusters and manifests can override it.
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spec:
terminationGracePeriodSeconds: 60 # example value; size it to your longest shutdown path
containers:
- name: worker
command: ["bundle", "exec", "sidekiq"]The worker must be the root process to receive the signal. If the container runs a shell command wrapper, the shell can receive TERM and not forward it to the worker. The worker then runs until the grace period expires and is killed. A simple check is to run ps -o pid,comm -p 1 inside the running container. The process at PID 1 should be the worker, not sh or a launch script.
Kubernetes Job documentation describes a related path. Suspending a Job sends SIGTERM to running Pods, honors their graceful termination period, and expects the application to handle the signal, for example by saving progress or undoing partial changes. This is Job-suspension behavior. Deployments of long-running workers follow the rollout path of their controller, so confirm which controller owns the worker in your environment.
General platform guidance
Microsoft’s Azure Architecture Center recommends that a background task receiving termination should stop accepting new work, complete or checkpoint the current work item, and exit cleanly. For queue-driven tasks, it recommends completing the current message where possible to avoid unnecessary redelivery. If the work cannot finish within the grace period, the task should checkpoint progress, or let message visibility expire so another instance can take the message. This is general platform guidance, not a guarantee for every queue or hosting service.
Diagnose the interruption in order
- Confirm which process receives the deployment signal. Check PID 1 in the running container, as above. If the signal reaches a shell rather than the worker, fix the entrypoint before changing any timeouts.
- Read worker logs and queue state around deploy time. For BullMQ, look for stalled events, then check the configured stall interval and maximum stalled count. A job can be returned to waiting or moved to failed if the worker stops renewing its active state.
- Check whether intake stops when shutdown begins. Confirm the worker stops fetching new jobs on the first signal, and that the handler awaits active work rather than returning early.
- Compare timing. Measure the longest normal job duration and the time a shutdown takes in a staging deploy. Compare both with the platform’s grace period. The grace period should be longer than the sum of the two, with margin for slow nodes.
- Verify the signal and acknowledgement settings for your exact library version. Do not copy one system’s signal sequence into another. A Sidekiq TSTP-then-TERM sequence means nothing to Celery or BullMQ.
Make replayed work safe
Recovery usually means some work runs again. A job that was interrupted after sending an email, charging a card, or calling a third-party API may send the email again, charge again, or call the API again. The libraries described above do not provide exactly-once execution, so the application has to make replay safe.
Quick Recap
- Make external side effects idempotent, for example by sending an idempotency key with each payment or message request.
- Write progress checkpoints for long jobs, and resume from the last checkpoint rather than from the beginning.
- Record completion in the same database transaction as the state change it represents, where the database allows it.
- Keep jobs short enough that the worst-case shutdown path fits the grace period. Split long work into chunks that each complete independently.
- Treat a job that has been killed and re-run as a normal case in your alerts, and track it separately from ordinary exceptions.
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