Hey, it seems that the issue is related to the driver undergoing a memory bottleneck, which causes it to crash with an out of memory (OOM) condition and gets restarted or becomes unresponsive due to frequent full garbage collection. The reason for the memory bottleneck can be, among others that 1) The driver instance type is not optimal for the load executed on the driver, 2) there are memory-intensive operations executed on the driver, or many notebooks or jobs are running in parallel on the same cluster. The solution varies from case to case, but the easiest way to resolve the issue in the absence of specific details is to increase the driver's memory. Other points to consider include avoiding memory-intensive operations like collect() operator, which brings a large amount of data to the driver, conversion of a large DataFrame to Pandas, and running batch jobs on a shared interactive cluster. It is also recommended to distribute the workloads into different clusters. (https://kb.databricks.com/jobs/driver-unavailable)

For more information on troubleshooting unresponsive Python notebooks or canceled commands, please refer to the Troubleshooting unresponsive Python notebooks or canceled commands article in the Databricks documentation.

PS: Check #DAIS2023 talks as well