There are several ways in which state and other activities are logged throughout a Dask cluster.


The scheduler, workers, and client all log various administrative events using Python’s standard logging module. Both the logging level and logging handlers are customizable. See the Debugging docs for more information.

Task transition logs

The scheduler keeps track of all state transitions for each task. This gives insight into how tasks progressed through their computation and can be particularly valuable when debugging. To retrieve the transition logs for a given task, pass the task’s key to the Scheduler.story() method.

>>> f = client.submit(inc, 123)
>>> f
<Future: finished, type: builtins.int, key: inc-aad7bbea25dc61c8e53d929c7ec50bed>
>>> s.story(f.key)
[('inc-aad7bbea25dc61c8e53d929c7ec50bed', 'released', 'waiting', {'inc-aad7bbea25dc61c8e53d929c7ec50bed': 'processing'}, 1605143345.7283862),
 ('inc-aad7bbea25dc61c8e53d929c7ec50bed', 'waiting', 'processing', {}, 1605143345.7284858),
 ('inc-aad7bbea25dc61c8e53d929c7ec50bed', 'processing', 'memory', {}, 1605143345.731495)]

Structured logs

The scheduler, workers, and client all support logging structured events to a centralized ledger, which is indexed by topic. By default, Dask will log a few administrative events to this system (e.g. when workers enter and leave the cluster) but custom events can be logged using the Scheduler.log_event(), Worker.log_event(), or Client.log_event() methods.

For example, below we log start and stop times to the "runtimes" topic using the worker’s log_event method:

>>> def myfunc(x):
...     start = time()
...     ...
...     stop = time()
...     dask.distributed.get_worker().log_event("runtimes", {"start": start, "stop": stop})
>>> futures = client.map(myfunc, range(10))
>>> client.get_events("runtimes")
((1605207481.77175, {'start': 1605207481.769397, 'stop': 1605207481.769397}),
 (1605207481.772021, {'start': 1605207481.770036, 'stop': 1605207481.770037}),

Events for a given topic can be retrieved using the Client.get_events() method. In the above example, we retrieved the logged start and stop times with client.get_events("runtimes"). Note that Client.get_events returns a tuple for each logged event which contains the logged message along with a timestamp for when the event was logged.

When combined with scheduler and worker plugins, the structured events system can produce rich logging / diagnostic systems.