@Marvin when working with prefect server. I am noticing that if a flow run with many tasks fails, my servers memory spikes and stays there. any idea what is causing this and how I can prevent this?

When a flow run with many tasks fails, causing a memory spike on your Prefect server, it may be related to how Prefect manages task concurrency and resource limits. Here are some points to consider: 1. Task Concurrency Limits: Prefect allows you to set concurrency limits on tasks using tags. This can help you manage the number of tasks that run simultaneously, which might help prevent memory spikes. * You can learn more from this guide: https://docs.prefect.io/latest/concepts/tasks/ 2. Managed Execution Pools: If you're using managed execution, there are memory and resource limits to be aware of: * Free tier accounts have a 2GB RAM limit. * More details here: https://docs.prefect.io/latest/guides/managed-execution/ Unfortunately, no specific GitHub issues related to memory spikes were found. To mitigate this issue: - Implement Task Concurrency Limits: Restrict the number of tasks running concurrently by configuring task tags. - Monitor Resource Usage: Ensure that your resources do not exceed the available memory. Feel free to reach out if you need further assistance!

what does cache_result_in_memory=false do? @Marvin

The

cache_result_in_memory=False

option in Prefect helps manage memory usage during flow execution. Here’s how it works: When you set

cache_result_in_memory=False

for a task or flow, Prefect will not keep the results of that task or flow in memory. This can be useful if: - You are returning a large amount of data, which can be costly to keep in memory for the entire flow run. - You want to reduce the overhead of memory usage during flow execution. Example Usage:

python
@flow(cache_result_in_memory=False)
def example_flow():
    return "large data set"

@task(cache_result_in_memory=False)
def example_task():
    return "another large data set"

Instead of keeping results in memory, the result will be persisted (stored) and retrieved from storage when required. For more details, check the documentation here: https://docs.prefect.io/latest/concepts/results/ It’s a handy feature when dealing with memory-intensive flows and prevents memory spikes like the one you described. Feel free to ask if you have more questions!

@Marvin my prefect on prem server is sitting at 12 GB of memory right now ith a running python task. that I think is tied to a failed flow that ran yesterday. my guess is prefect is not releasing this memory.. why?

Your issue could be related to a potential memory leak in Prefect, similar to what has been observed in case of using the

DaskExecutor

in a Kubernetes setup. Here's a breakdown of the observations and potential solutions: ### Observations: 1. Memory Leak: A GitHub issue reported a memory leak when using

DaskExecutor

, where the memory usage of the Prefect job kept increasing. 2. Persistent Memory: There's another related discussion on handling SIGTERM signals gracefully, which aims at better memory handling during shutdowns or failures. ### Relevant GitHub Issues: 1. Possible Memory Leak with DaskExecutor: - Issue: High and growing memory usage for Prefect jobs, specifically with

DaskExecutor

. - Reference: Possible memory leak when using DaskExecutor in Kubernetes 2. Handling SIGTERM Gracefully: - Issue: This improvement aims to help with managing resources better by handling SIGTERM signals gracefully, reducing memory residues after flows fail. - Reference: Handle SIGTERM received by server gracefully ### Solutions and Recommendations: 1. Avoid Persisting Large Data in Memory: - Use

cache_result_in_memory=False

to prevent large data from being cached in memory. 2. Increase Monitoring: - Monitor your server’s memory usage and identify which process is consuming excess resources. 3. Upgrade Prefect Version: - Make sure to use the latest version of Prefect to benefit from recent fixes and improvements. 4. Handle Memory Efficiently: - If using Kubernetes, ensure your deployments and jobs have appropriate resource limits and request settings. - Utilize Prefect task concurrency limits to control the number of active tasks and manage memory usage. 5. Consider DaskExecutor Configuration: - If you’re using DaskExecutor, fine-tune its configuration with appropriate

n_workers

and

memory_limit

. If these recommendations don’t resolve the issue, it might be worth opening a new issue on Prefect GitHub Issues to get more direct assistance from the maintainers. Feel free to ask if you need more assistance!