my dagster webserver takes almost 10 mins to start on kubernetes I have large code locations could that be a cause? how do I improve he situation

It's possible that large code locations could contribute to longer startup times for your Dagster webserver on Kubernetes. When the webserver starts, it communicates with user code deployments via gRPC to fetch information needed to populate the UI. If there is a significant amount of user code to load, this could indeed impact the startup time. Here are a few steps you can take to improve the situation: 1. Optimize Code Locations: If your code locations are large, consider breaking them down into smaller, more manageable pieces. This can help reduce the amount of time it takes to load each code location. 2. Horizontal Scaling: The Dagster webserver can be horizontally scaled by setting the

dagsterWebserver.replicaCount

field in

values.yaml

. This allows you to distribute the load across multiple instances of the webserver. 3. Check for Errors: After updating code locations, if there is an error with any code location, an error is surfaced within the Dagster UI. Make sure there are no underlying errors that could be causing delays. 4. Use Ephemeral Compute: The documentation mentions that the webserver launches runs via the

K8sRunLauncher

, which creates a new Kubernetes job per run. The run worker uses ephemeral compute and completes once the run is finished. Ensuring that your runs are using ephemeral compute can help manage resources more effectively. 5. Review Resource Allocation: Ensure that your Kubernetes cluster has sufficient resources allocated to the Dagster webserver. If the resources are constrained, this could lead to longer startup times. 6. Analyze Startup Process: You may want to analyze the startup process to identify any bottlenecks. Tools like py-spy can be used to profile your code and find out where the process is spending the most time during startup. 7. Review Kubernetes Configuration: Check your Kubernetes configuration for any potential issues that could be causing delays. This includes looking at pod resource requests and limits, as well as any startup probes or liveness/readiness checks that might be misconfigured. If after taking these steps you still experience long startup times, it may be beneficial to look into more detailed logs or profiling to pinpoint the exact cause of the delay. Remember that the webserver frequently checks whether the user code deployment has been updated and fetches new information, so any changes you make may not be immediately reflected until the next check.