Databricks Community

zenwanderer · Saturday

Hi everyone, I’m facing an issue when running a notebook on a Databricks All-purpose cluster. 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.

I tried setting spark.databricks.execution.timeout, but it doesn’t seem to have any effect in my case.

What I need is a timeout mechanism that can cancel the currently running notebook cell, not just a Spark job timeout.

If anyone can share guidance or official documentation references, I’d really appreciate it. Thanks in advance!

balajij8 · Saturday

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

zenwanderer · Saturday

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

balajij8 · Saturday

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