I have a Dagster deployed in my own Kubernetes cluster. In my job, there are more than 10 minutes delay between two steps. Let's say an event STEP_SUCCESS is emitted for the first step at 1:00 nothing happens afterwards and the event STEP_WORKER_STARTING for the next step is only emitted at 1:25. What can cause this delay and how to fix ?

Delays between steps in a Dagster job running on a Kubernetes cluster can be caused by various factors. Here are some potential causes and troubleshooting steps based on the documentation: 1. Resource Initialization: If there is a resource that takes a long time to initialize, it could cause delays. Check if there is any resource that is particularly slow to start up. 2. Subprocess Creation: If each subprocess is taking a long time to start, this could also introduce delays. This might be related to the Kubernetes infrastructure, such as the time it takes to provision pods or containers. 3. Kubernetes Cluster Resources: Insufficient CPU or memory in your Kubernetes nodes can cause scheduling delays. If the cluster is trying to scale up to accommodate the job but lacks resources, it could lead to the observed behavior. 4. Dagster Daemon: Ensure that the Dagster daemon is running correctly. The daemon is responsible for managing the lifecycle of runs, including the transition between steps. To troubleshoot and potentially fix the issue, consider the following steps: - Check Resource Initialization: Review the logs for the job to see if there is a significant gap between

STEP_WORKER_STARTED

and

RESOURCE_INIT_STARTED

. This could indicate a slow resource initialization. - Monitor Kubernetes Resources: Use

kubectl describe

to check the status of the pods and nodes. Look for events that indicate insufficient resources or failed scheduling attempts. - Review Dagster Daemon Status: In the Dagster UI, navigate to Deployment > Daemons and verify that the daemon is running and healthy. - Check for Errors: Review the logs for any errors that might indicate why the steps are delayed. This could include errors during resource initialization or issues with subprocess creation. - Adjust Kubernetes Requests: If the issue is related to insufficient resources, consider increasing the CPU and memory requests for the run worker pods. - Run Monitoring: Utilize Dagster's run monitoring features to detect and handle cases where the run worker crashes or gets stuck. If after these checks and adjustments the issue persists, it may be necessary to delve deeper into the specific configuration of your Dagster deployment and Kubernetes cluster. You might also want to check for any recent changes in your infrastructure or Dagster configuration that could have introduced the delay. For more detailed guidance and potential solutions, you can refer to the Dagster documentation and consider reaching out to the community or support channels if the problem continues to be challenging to diagnose and resolve.