I am working on a Python application that processes background jobs submitted by users, and I am experiencing one specific problem where the same job is occasionally processed more than once even though each job should only be executed a single time. Under normal circumstances, a job is added to the application’s queue, a worker picks it up, performs the required Python function, and then marks the job as completed. Most of the time this workflow behaves exactly as expected, but occasionally I can see the same job being executed twice, resulting in the application performing the same operation more than once. The duplicate execution is not caused by the user intentionally submitting the job twice, because I can trace the original job identifier and confirm that the application received only one submission. The problem is therefore specifically related to how an already-created background job is being handled by the Python worker process, where the same queued job can apparently become eligible for processing again even though another worker has already started or completed it.
The behavior is intermittent and does not happen for every job, which makes it particularly difficult to reproduce consistently. Most jobs enter the queue, are picked up once, and complete normally, while occasionally a job appears in the worker logs twice with the same identifier. In some cases the two executions happen very close together, while in other cases the second execution happens after the first worker has already performed most or all of the actual work. The application does not intentionally enqueue a second copy of the job, and I have checked the code responsible for creating the queue entry to make sure the initial submission is performed only once. I have also added logging around the point where workers retrieve jobs so I can compare the job ID, worker process, start time, and completion time. These logs show that the duplicate processing occurs after the original job already exists, which makes me think the issue is related to the transition between a job being available, being claimed by a worker, and finally being acknowledged as completed. I would like to understand what could allow the same job to become available to another worker during that lifecycle.
I have already checked the worker code for places where a job could accidentally be submitted again after an exception or retry, but I have not found an obvious path that intentionally creates a duplicate queue entry. The Python function responsible for processing the job also does not create another job with the same identifier. What I am trying to determine is whether the queue implementation or worker logic can re-deliver a job when the worker takes too long, loses its connection temporarily, or fails to acknowledge completion within the expected time. The actual processing function can sometimes take a while because it performs several operations before the job can be marked as complete, so I am wondering whether the queue considers the job abandoned while the Python worker is still working on it. I have also considered whether multiple worker processes might be able to obtain the same job if the job’s state is not changed atomically, but I do not want to assume that concurrency is the cause without understanding the correct locking or acknowledgement behavior.
Another part of the problem is deciding where the application should guarantee that a job can only be processed once. I understand that distributed background-processing systems can sometimes provide delivery guarantees that are different from exactly-once execution, but I am not sure how that should affect the design of the Python application itself. At the moment, the worker assumes that receiving a job means it is safe to execute the operation, but the operation is not completely harmless if it happens twice. I am therefore considering whether I should add an application-level job state, a processing lock, or an idempotency mechanism based on the job ID so that a second worker cannot repeat work that has already been completed. However, before changing the architecture, I would like to know whether there is a Python-specific or queue-worker pattern that I should be following to correctly handle acknowledgement, retries, worker crashes, and long-running jobs. I especially want to avoid adding a lock that could remain permanently active if a worker unexpectedly terminates while processing a job.
The issue is easier to notice when multiple workers are running because the application is designed to process several jobs concurrently. I have compared the logs from normal jobs with the logs from duplicated jobs and can see that the same identifier can sometimes appear in two worker execution paths. The workers are separate processes, so ordinary Python variables are not shared between them, which means I am trying to understand how the queue or shared job state determines whether a job is already being processed. I would like to know what information would be most useful for diagnosing this, such as worker process IDs, job timestamps, queue visibility or lease times, acknowledgement events, retry counts, or the exact sequence of state changes. I can add more detailed logging around the job lifecycle if necessary, but I would prefer to collect information that can actually distinguish between a queue redelivery, an application retry, a race between workers, and an accidental second submission.
Has anyone encountered a Python background-worker setup where a single submitted job is occasionally executed twice even though the application only creates one initial job entry? I would especially appreciate guidance on how to trace the complete lifecycle of one duplicated job and determine whether the second execution is caused by queue redelivery, acknowledgement timing, worker concurrency, a retry mechanism, or an application-level race condition. I can provide a simplified version of the worker code and the job-handling logic, along with sanitized logs showing the same job ID being processed by different workers. My main goal is to make each job safe from unintended duplicate execution while still allowing failed jobs to be retried when a worker genuinely crashes or cannot complete the work. I would like to understand the correct Python design for handling this situation rather than simply adding arbitrary delays or reducing the number of workers. Is there anyone who has faced this issue and how do you fixed it?