• You can use signal to do this if running in a notebook for code validation

#Add in notebook
import signal

class TimeoutException(Exception):
    """Raised when a cell is run for very long time"""

def timeout_handler(signum, frame):
    raise TimeoutException("Timed out!")

def set_cell_timeout(seconds):
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(seconds)

#Add in a notebook cell running notebook function
try:
    set_cell_timeout(30) # Set for 30 seconds
    #notebook function
finally:
    signal.alarm(0)

• You can use lakeflow job notifications with threshold to cancel jobs running too long. Avoid using signals in notebooks running via jobs

What I’m looking for is a workspace-level monitoring approach: detect any notebook execution where a cell (or the run) has been running longer than a threshold, and then cancel/terminate it automatically.

I’ve tried looking into audit tables, REST APIs, but it seems they don’t provide enough visibility at cell-level

For the issue - Some of my cells/pipelines run for a very long time, and I want to automatically cancel/kill them when they exceed a certain time limit.

• You can use job notifications with Metric threshold (Duration Warning for notifications & Duration Timeout for kill) to cancel jobs running too long (completion time more than Duration Timeout). More details here

@zenwanderer Have you looked into Query Watchdog?

For Classic All-Purpose clusters this might be your best bet.

https://docs.databricks.com/aws/en/compute/troubleshooting/query-watchdog