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Celery: what is the reason to have acks_late=true without setting task_reject_on_worker_lost=true

After playing with some "defect" scenarios with celery (Redis being a broker for whatever it worth) we came to understanding that there is effectively no sense in setting acks_late=true without simultaneous setting of task_reject_on_worker_lost=true because the task won't be rescheduled (again, in our tests) -- task stays in the "unacked" category forever.
At the same time everybody says that acks_late will make the task being subject for rescheduling on the same / another worker, so the question is: when does it happen?
The official docs say that
Note that the worker will acknowledge the message if the child process
executing the task is terminated (either by the task calling
sys.exit(), or by signal) even when acks_late is enabled. This
behavior is intentional as…
We don’t want to rerun tasks that forces the kernel to send a SIGSEGV (segmentation fault) or similar signals to the process.
We assume that a system administrator deliberately killing the task does not want it to automatically restart.
A task that allocates too much memory is in danger of triggering the kernel OOM killer, the same may happen again.
A task that always fails when redelivered may cause a high-frequency message loop taking down the system.
If you really want a task to be redelivered in these scenarios you
should consider enabling the task_reject_on_worker_lost setting.
What are possible examples of "something went wrong" that don't fall into the "worker terminated deliberately or due to a signal caught" category?

Reboot, power outage, hardware failure. n.b., all of your examples assume that the prefetch multiplier is 1.

Note that there is a difference between the celery worker process, to the child processes actually executing the tasks.
By default, when you create a celery worker, it will create one "parent" process and x number of child processes which executes the tasks, where x is the number of CPUs you have (you can read more about this in the docs, and how to configure it)
I have tested all the different scenarios, these are my conclusions:
acks_late is about what happens when the worker dies. task_reject_on_worker_lost is about the actual process executing the task.
For example, if I have a k8s pod running celery process: if I send sigkill (cold shutdown) to the pod, having acks_late as true will make sure that the task will be picked up by a different worker.
But, if I kill somehow the child process executing the task (go inside the pod and kill the child process for example, or if the process exits by itself somehow), the task will not be picked up even if acks_late is true.
If you set task_reject_on_worker_lost to true, the task will be picked up again.
hope that clarifies everything

What are the consequences of disabling gossip, mingle and heartbeat for celery workers?

What are the implications of disabling gossip, mingle, and heartbeat on my celery workers?
In order to reduce the number of messages sent to CloudAMQP to stay within the free plan, I decided to follow these recommendations. I therefore used the options --without-gossip --without-mingle --without-heartbeat. Since then, I have been using these options by default for all my celery projects but I am not sure if there are any side-effects I am not aware of.
Please note:
we now moved to a Redis broker and do not have that much limitations on the number of messages sent to the broker
we have several instances running multiple celery workers with multiple queues

This is the base documentation which doesn't give us much info
heartbeat
Is related to communication between the worker and the broker (in your case the broker is CloudAMQP).
See explanation
With the --without-heartbeat the worker won't send heartbeat events
mingle
It only asks for "logical clocks" and "revoked tasks" from other workers on startup.
Taken from whatsnew-3.1
The worker will now attempt to synchronize with other workers in the same cluster.
Synchronized data currently includes revoked tasks and logical clock.
This only happens at startup and causes a one second startup delay to collect broadcast responses from other workers.
You can disable this bootstep using the --without-mingle argument.
Also see docs
gossip
Workers send events to all other workers and this is currently used for "clock synchronization", but it's also possible to write your own handlers on events, such as on_node_join, See docs
Taken from whatsnew-3.1
Workers are now passively subscribing to worker related events like heartbeats.
This means that a worker knows what other workers are doing and can detect if they go offline. Currently this is only used for clock synchronization, but there are many possibilities for future additions and you can write extensions that take advantage of this already.
Some ideas include consensus protocols, reroute task to best worker (based on resource usage or data locality) or restarting workers when they crash.
We believe that although this is a small addition, it opens amazing possibilities.
You can disable this bootstep using the --without-gossip argument.

Celery workers started up with the --without-mingle option, as #ofirule mentioned above, will not receive synchronization data from other workers, particularly revoked tasks. So if you revoke a task, all workers currently running will receive that broadcast and store it in memory so that when one of them eventually picks up the task from the queue, it will not execute it:
https://docs.celeryproject.org/en/stable/userguide/workers.html#persistent-revokes
But if a new worker starts up before that task has been dequeued by a worker that received the broadcast, it doesn't know to revoke the task. If it eventually picks up the task, then the task is executed. You will see this behavior if you're running in an environment where you are dynamically scaling in and out celery workers constantly.

I wanted to know if the --without-heartbeat flag would impact the worker's ability to detect broker disconnect and attempts to reconnect. The documentation referenced above only opaquely refers to these heartbeats acting at the application layer rather than TCP/IP layer. Ok--what I really want to know is does eliminating these messages affect my worker's ability to function--specifically to detect broker disconnect and then to try to reconnect appropriately?
I ran a few quick tests myself and found that with the --without-heartbeat flag passed, workers still detect broker disconnect very quickly (initiated by me shutting down the RabbitMQ instance), and they attempt to reconnect to the broker and do so successfully when I restart the RabbitMQ instance. So my basic testing suggests the heartbeats are not necessary for basic health checks and functionality. What's the point of them anyways? It's unclear to me, but they don't appear to have impact on worker functionality.

Does restarting celery cause duplicate tasks?

I have an email task in celery that has an eta of 10 days from now(). However, I'm finding that some people are getting 5-6 duplicate emails at a time. I've come across this problem before with BROKER_TRANSPORT_OPTIONS set too low. Now I have this in my settings file:
BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 2592000} #30 days
So that shouldn't be a problem any more. I'm just wondering if there is anything else that can cause it. i.e. restarting celery. Celery gets restarted every time I deploy new code and that can happen 5 or more times a week so it's the only thing I can think of.
Any ideas?
Thanks.

Task duplicating is possible if worker/beat processes had not stopped correctly. How do you restart celery workers/beat? Check server for zombie celery worker and beat processes. Try to stop all celery processes, check no processes of celery exist and start it again. After all check that ps ax | grep celery shows fresh workers and only one beat.

Tasks won't restart in case of incorrect worker stop if you set CELERY_ACKS_LATE = False. In this case the task marked as acknowledged immediately after consuming. See docs.
Also make sure that your tasks have no retry enabled. If any exception happens inside task - they might retry with the same input arguments.
Another possible case - your tasks are written wrong and each run selects the same recipients set.

Persistent Long Running Tasks in Celery

I'm working on a Python based system, to enqueue long running tasks to workers.
The tasks originate from an outside service that generate a "token", but once they're created based on that token, they should run continuously, and stopped only when explicitly removed by code.
The task starts a WebSocket and loops on it. If the socket is closed, it reopens it. Basically, the task shouldn't reach conclusion.
My goals in architecting this solutions are:
When gracefully restarting a worker (for example to load new code), the task should be re-added to the queue, and picked up by some worker.
Same thing should happen when ungraceful shutdown happens.
2 workers shouldn't work on the same token.
Other processes may create more tasks that should be directed to the same worker that's handling a specific token. This will be resolved by sending those tasks to a queue named after the token, which the worker should start listening to after starting the token's task. I am listing this requirement as an explanation to why a task engine is even required here.
Independent servers, fast code reload, etc. - Minimal downtime per task.
All our server side is Python, and looks like Celery is the best platform for it.
Are we using the right technology here? Any other architectural choices we should consider?
Thanks for your help!

According to the docs
When shutdown is initiated the worker will finish all currently executing tasks before it actually terminates, so if these tasks are important you should wait for it to finish before doing anything drastic (like sending the KILL signal).
If the worker won’t shutdown after considerate time, for example because of tasks stuck in an infinite-loop, you can use the KILL signal to force terminate the worker, but be aware that currently executing tasks will be lost (unless the tasks have the acks_late option set).
You may get something like what you want by using retry or acks_late
Overall I reckon you'll need to implement some extra application-side job control, plus, maybe, a lock service.
But, yes, overall you can do this with celery. Whether there are better technologies... that's out of the scope of this site.

Understanding celery task prefetching

I just found out about the configuration option CELERYD_PREFETCH_MULTIPLIER (docs). The default is 4, but (I believe) I want the prefetching off or as low as possible. I set it to 1 now, which is close enough to what I'm looking for, but there's still some things I don't understand:
Why is this prefetching a good idea? I don't really see a reason for it, unless there's a lot of latency between the message queue and the workers (in my case, they are currently running on the same host and at worst might eventually run on different hosts in the same data center). The documentation only mentions the disadvantages, but fails to explain what the advantages are.
Many people seem to set this to 0, expecting to be able to turn off prefetching that way (a reasonable assumption in my opinion). However, 0 means unlimited prefetching. Why would anyone ever want unlimited prefetching, doesn't that entirely eliminate the concurrency/asynchronicity you introduced a task queue for in the first place?
Why can prefetching not be turned off? It might not be a good idea for performance to turn it off in most cases, but is there a technical reason for this not to be possible? Or is it just not implemented?
Sometimes, this option is connected to CELERY_ACKS_LATE. For example. Roger Hu writes «[…] often what [users] really want is to have a worker only reserve as many tasks as there are child processes. But this is not possible without enabling late acknowledgements […]» I don't understand how these two options are connected and why one is not possible without the other. Another mention of the connection can be found here. Can someone explain why the two options are connected?

Prefetching can improve the performance. Workers don't need to wait for the next message from a broker to process. Communicating with a broker once and processing a lot of messages gives a performance gain. Getting a message from a broker (even from a local one) is expensive compared to the local memory access. Workers are also allowed to acknowledge messages in batches
Prefetching set to zero means "no specific limit" rather than unlimited
Setting prefetching to 1 is documented to be equivalent to turning it off, but this may not always be the case (see https://stackoverflow.com/a/33357180/71522)
Prefetching allows to ack messages in batches. CELERY_ACKS_LATE=True prevents acknowledging messages when they reach to a worker

Old question, but still adding my answer in case it helps someone. My understanding from some initial testing was same as that in David Wolever's answer. I just tested this more in celery 3.1.19 and -Ofair does work. Just that it is not meant to disable prefetch at the worker node level. That will continue to happen. Using -Ofair has a different effect which is at the pool worker level. In summary, to disable prefetch completely, do this:
Set CELERYD_PREFETCH_MULTIPLIER = 1
Set CELERY_ACKS_LATE = True at a global level or task level
Use -Ofair while starting the workers
If you set concurrency to 1, then step 3 is not needed. If you want a
higher concurrency, then step 3 is essential to avoid tasks getting
backed up in a node that could be run long running tasks.
Adding some more details:
I found that the worker node will always prefetch by default. You can only control how many tasks it prefetches by using CELERYD_PREFETCH_MULTIPLIER. If set to 1, it will only prefetch as many tasks as the number of pool workers (concurrency) in the node. So if you had concurrency = n, the max tasks prefetched by the node will be n.
Without the -Ofair option, what happened for me was that if one of the pool worker processes was executing a long running task, the other workers in the node would also stop processing the tasks already prefetched by the node. By using -Ofair, that changed. Even though one of the workers in the node was executing a long running tasks, others would not stop processing and would continue to process the tasks prefetched by the node. So I see two levels of prefetching. One at the worker node level. The other at the individual worker level. Using -Ofair for me seemed to disable it at the worker level.
How is ACKS_LATE related? ACKS_LATE = True means that the task will be acknowledged only when the task succeeds. If not, I suppose it would happen when it is received by a worker. In case of prefetch, the task is first received by the worker (confirmed from logs) but will be executed later. I just realized that prefetched messages show up under "unacknowledged messages" in rabbitmq. So I'm not sure if setting it to True is absolutely needed. We anyway had our tasks set that way (late ack) for other reasons.

Just a warning: as of my testing with the redis broker + Celery 3.1.15, all of the advice I've read pertaining to CELERYD_PREFETCH_MULTIPLIER = 1 disabling prefetching is demonstrably false.
To demonstrate this:
Set CELERYD_PREFETCH_MULTIPLIER = 1
Queue up 5 tasks that will each take a few seconds (ex, time.sleep(5))
Start watching the length of the task queue in Redis: watch redis-cli -c llen default
Start celery worker -c 1
Notice that the queue length in Redis will immediately drop from 5 to 3
CELERYD_PREFETCH_MULTIPLIER = 1 does not prevent prefetching, it simply limits the prefetching to 1 task per queue.
-Ofair, despite what the documentation says, also does not prevent prefetching.
Short of modifying the source code, I haven't found any method for entirely disabling prefetching.

I cannot comment on David Wolever's answers, since my stackcred isn't high enough. So, I've framed my comment as an answer since I'd like to share my experience with Celery 3.1.18 and a Mongodb broker. I managed to stop prefetching with the following:
add CELERYD_PREFETCH_MULTIPLIER = 1 to the celery config
add CELERY_ACKS_LATE = True to the celery config
Start celery worker with options: --concurrency=1 -Ofair
Leaving CELERY_ACKS_LATE to the default, the worker still prefetches. Just like the OP I don't fully grasp the link between prefetching and late acks. I understand what David says "CELERY_ACKS_LATE=True prevents acknowledging messages when they reach to a worker", but I fail to understand why late acks would be incompatible with prefetch. In theory a prefetch would still allow to ack late right - even if not coded as such in celery ?

I experienced something a little bit different with SQS as broker.
The setup was:
CELERYD_PREFETCH_MULTIPLIER = 1
ACKS_ON_FAILURE_OR_TIMEOUT=False
CELERY_ACKS_LATE = True
CONCURRENCY=1
After task fail (exception raised), the worker became unavailable since the message was not acked, both local and remote queue.
The solution that made the workers continue consuming work was setting
CELERYD_PREFETCH_MULTIPLIER = 0
I can only speculate that acks_late was not taken in consideration when writing the SQS transport