Our backend uses the Python flask framework and temporal as an asynchronous task queue to handle our scheduled or asynchronous tasks. In our system, temporal is treated as a Celery-like component. We redeployed a server to run temporal workers. Then we encountered a memory leak on tenporal, which c…

We are not aware of any memory leaks, but memory usage can grow until limits are reached. Specifically, running activities use memory but the amount of running activities can be bounded by max_concurrent_activities. Also, workflows use memory in a cache for optimization and will fill the cache when …

I have a question. I feel that what we are facing now is this situation. If our worker activity is always running, and after the old worker ends, a new worker comes out, then there will be workflows using memory in a cache for optimization. In this case, our memory is gradually growing to 100%. Is t…

[image] We configured the values of “max_cached_workflows” and “max_concurrent_activities” in the old code, but the server’s memory continued to grow, which confused me, so I wondered if it was the cause of the memory leak problem.

[image] alchemya: after the old worker ends, a new worker comes out, then there will be workflows using memory in a cache for optimization. In this case, our memory is gradually growing to 100%. Is this the reason? Usually workers live for the life of the process, but regardless, workflow cac…

In the past few months, our server memory has continued to grow due to temporal‘s reasons. We have upgraded the memory of the cloud server from 8G to 16G, and now it is 64G. If we don’t upgrade the physical memory, the proportion of our temporal memory will continue to increase until it occupies fu…

As time goes by, our memory usage will eventually reach 100%, even if we use a server with 64G memory. This is a serious problem we are facing now.

This could also be due to activities not completing and continually increasing memory. Are you properly heartbeating in activities? Granted the number of activities will still be bound by max concurrent activities. We are aware of no memory leaks. If you are able to replicate and then continually r…

In our code, our activities will be automatically terminated after a certain duration. There is no historical residual activity that causes memory increase. I can guarantee that there is no situation where the activity is not completed and continues to occupy memory. So this is why I suspect there …

So this is where I’m confused now.

We now have a memory leak problem

Community Support

alchemya June 17, 2024, 10:47am 2

We now use the method of restarting Docker with a scheduled task to temporarily solve this problem, but the server memory still accounts for nearly 100%

Worker Memory usage

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We now have a memory leak problem

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