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A deploy rarely throws an exception inside your job. The platform asks the worker process to stop, the worker is still holding a job, and the platform removes the process before that job finishes. No handler code reports an error, so the work just stops partway through. Whether the job is then retried, redelivered, pushed back, or lost depends on two things: how the worker handled the shutdown signal, and how the queue library recovers work that was left behind.
What a deploy sends to your worker
Most container platforms follow the same termination sequence, even though the names of the steps differ between stacks.
- The orchestrator or deploy script sends a termination signal, usually SIGTERM, to the process it started. In Kubernetes, that is the root process of the container.
- The worker stops claiming new jobs. Depending on the library, this is triggered by SIGTERM, by a separate “stop fetching” signal, or by a close call in your code.
- Active jobs either finish, or the handler saves a checkpoint and returns so the process can exit.
- If the process is still running when the grace period ends, the platform sends SIGKILL. The process cannot catch that signal and gets no chance to clean up.
The gap between step 1 and step 4 is the only window your worker has to behave well. If the shutdown path needs longer than that window, the platform decides the outcome for you.
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Why the job shows no error
A job fails with an exception when handler code raises one and the library records it. A SIGKILL skips all of that. The handler never returns, the error tracker receives nothing, and the job record in the queue still shows as active under a worker that no longer exists.
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The queue notices only later, through its own recovery mechanism. That is where a visible outcome finally appears: a stalled-job event, a job returned to waiting, a job moved to failed, or a job that runs again on another worker. If you are looking for an exception in your logs, you will not find one, because the process was stopped rather than failed.
Graceful shutdown and queue recovery are separate paths
Keep these two mechanisms apart when you debug. Graceful shutdown is what the worker does while it still has time: it stops taking work and lets active jobs complete. Recovery is what the queue does after a worker disappears while holding a job. Good shutdown reduces how often recovery is needed. Recovery decides what happens when shutdown did not finish.
| Path | Who acts | Trigger | Typical result |
|---|---|---|---|
| Graceful shutdown | Worker process | SIGTERM, a stop-fetching signal, or a close call, depending on the stack | No new jobs are claimed; active jobs finish or checkpoint; the process exits cleanly |
| Forced termination | Platform or orchestrator | The grace period expires and the process receives SIGKILL | The process is gone and any active job is left incomplete |
| Queue recovery | Queue library | A lock, visibility timeout, or stalled-job check fires for a job nobody is still renewing | The job may be returned to waiting, redelivered, pushed back to the queue, moved to failed, or, in some configurations, lost |
How each stack handles the signal
The exact signal sequence differs by library and version. Do not copy one system’s sequence into another without checking that system’s documentation.
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Sidekiq’s deployment guidance describes a two-signal sequence. Send TSTP early so the process stops fetching new work, then send TERM later so it can shut down within its configured timeout. The deploy script should wait for that timeout plus five seconds after TERM before it escalates. Sending KILL too early can lose jobs, and with Sidekiq Pro’s super_fetch it can cause duplicate execution. When the timeout expires, Sidekiq documents pushing unfinished work back to Redis.
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Sidekiq’s Kubernetes guidance adds two points. Kubernetes sends SIGTERM to the root process and SIGKILL after the pod’s termination grace period, and that grace period should be longer than the Sidekiq timeout. Wrapping the worker in a shell command can cause TERM to reach the shell instead of Sidekiq, so Sidekiq is killed when the grace period expires without ever shutting down.
Celery
Celery’s Workers Guide describes three shutdown signals. TERM starts 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, so executing work can be lost unless late acknowledgement is configured. The guide includes version-specific notes for Celery 5.2, 5.6, and 5.7, so confirm the note for your version before relying on exact semantics.
BullMQ
BullMQ’s graceful-shutdown guide calls for await worker.close() when the process receives a termination signal. As BullMQ’s documentation puts it: “The above call will mark the worker as closing so it will not pick up new jobs, and at the same time it will wait for all the current jobs to be processed (or failed).”
The close call has no timeout of its own. If your handler runs longer than the platform’s grace period, the host’s deadline ends the process before close completes, so the termination deadline still governs the outcome. If closure cannot complete, stalled-job handling lets another worker resume the job. BullMQ’s production guide gives about 30 seconds by default as the waiting time before stalled jobs can be picked up by new workers. Its stalled-job guide explains that active jobs must keep signalling that they are alive, and that a job can be returned to waiting or moved to failed if the worker stops doing so. A job that exceeds the configured stalled count can fail permanently. From BullMQ 2.0 onward, a QueueScheduler is no longer required for stalled-job recovery; older installations need guidance for their own version.
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Side-by-side comparison
| Stack | Signal or call that stops new work | Waits for active work | Own time limit on the wait | What happens on forced stop |
|---|---|---|---|---|
| Sidekiq | TSTP first, then TERM | Yes, until the configured Sidekiq timeout | Yes, the configured timeout | Unfinished jobs are pushed back to Redis at timeout; early KILL can lose jobs or cause duplicates with super_fetch |
| Celery | TERM starts a warm shutdown | Yes, executing tasks are allowed to finish | Not stated in the Celery Workers Guide | KILL cannot be caught; executing work may be lost unless late acknowledgement is configured |
| BullMQ | await worker.close() |
Yes, close waits for active jobs to process or fail | None; the host’s termination deadline applies | Stalled-job handling lets another worker resume the job; jobs over the stalled limit can fail permanently |
Set the grace period from real job durations
The grace period has to cover your longest normal job plus the time the shutdown path needs. Microsoft’s Azure Architecture Center advises allowing enough time for typical work to complete. Kubernetes defaults to a 30-second termination grace period unless the pod spec overrides it, as noted in Sidekiq’s Kubernetes guidance. Confirm the effective value in your cluster rather than assuming the default.
In a Kubernetes pod spec, the field is terminationGracePeriodSeconds. Launch the worker in a way that lets it receive the signal directly. The example below uses illustrative values only: a Sidekiq timeout of 25 seconds with a 60-second grace period, which leaves room for the shutdown to finish.
spec:
terminationGracePeriodSeconds: 60
containers:
- name: worker
image: example/worker:latest
command: ["bundle", "exec", "sidekiq"]If you must use a shell wrapper, use exec so the shell hands its process over to the worker and the worker receives the signal.
Jobs in Kubernetes Jobs and suspension
Kubernetes’ Job documentation describes a specific case: suspending a Job terminates its running Pods with SIGTERM, honors each Pod’s graceful termination period, and expects the Pod to handle the signal, for example by saving progress or undoing partial changes. That behavior applies to Job suspension. Confirm which controller created your worker pods, because a Deployment rollout in your cluster may follow a different path, and the Job-specific guidance does not describe it.
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Make replay safe before you tune timeouts
Once a job can run again, a second execution must not corrupt state or repeat an irreversible side effect. None of the libraries above guarantees exactly-once execution, so design for at-least-once behavior. Practical measures include:
- Use an idempotency key for external side effects such as payments, emails, and webhooks, and check it before acting.
- Record completion in the same database transaction as the state change it depends on.
- Checkpoint long jobs in stages so a rerun can resume instead of starting over.
- Avoid acknowledging or marking work complete before its effects are durable.
Microsoft’s Azure guidance for queue-driven tasks says to complete the current message 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 it.
Diagnose an interrupted worker
- Confirm which process receives the deploy signal. Inside the container, run
ps -o pid,args -p 1. If PID 1 is a shell rather than your worker, the worker will not see SIGTERM. - Check worker logs and queue state around the deploy time. Look for shutdown messages, stalled events, jobs returned to waiting, and jobs moved to failed.
- For BullMQ, review stalled events and the configured stalled-job limit (
maxStalledCountin worker options). A job can be moved to failed once it exceeds that limit. - Check whether the worker stops fetching when shutdown begins and whether the handler awaits active work before the process exits.
- Compare the longest normal job duration plus shutdown time with the platform’s grace period. If the job takes longer, the platform will force the stop before the handler can finish.
- Verify signal, timeout, and acknowledgement settings against the documentation for your exact library version. Celery’s acknowledgement behavior and BullMQ’s stalled-job behavior both depend on configuration.
If the platform sends SIGKILL and the job is repeatedly lost, shorten the job or checkpoint it first. Lengthening the grace period only helps when the platform is actually giving the worker enough time to run its shutdown path.
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