Software fails, Hardware fails, network connections fail, user code fails. This document describes how dask.distributed responds in the face of these failures and other known bugs.

User code failures

When a function raises an error that error is kept and transmitted to the client on request. Any attempt to gather that result or any dependent result will raise that exception.

>>> def div(a, b):
...     return a / b

>>> x = client.submit(div, 1, 0)
>>> x.result()
ZeroDivisionError: division by zero

>>> y = client.submit(add, x, 10)
>>> y.result()  # same error as above
ZeroDivisionError: division by zero

This does not affect the smooth operation of the scheduler or worker in any way.

Closed Network Connections

If the connection to a remote worker unexpectedly closes and the local process appropriately raises an IOError then the scheduler will reroute all pending computations to other workers.

If the lost worker was the only worker to hold vital results necessary for future computations then those results will be recomputed by surviving workers. The scheduler maintains a full history of how each result was produced and so is able to reproduce those same computations on other workers.

This has some fail cases.

  1. If results depend on impure functions then you may get a different (although still entirely accurate) result

  2. If the worker failed due to a bad function, for example a function that causes a segmentation fault, then that bad function will repeatedly be called on other workers. This function will be marked as “bad” after it kills a fixed number of workers (defaults to three).

  3. Data sent out directly to the workers via a call to scatter() (instead of being created from a Dask task graph via other Dask functions) is not kept in the scheduler, as it is often quite large, and so the loss of this data is irreparable. You may wish to call replicate() on the data with a suitable replication factor to ensure that it remains long-lived or else back the data off on some resilient store, like a file system.

Hardware Failures

It is not clear under which circumstances the local process will know that the remote worker has closed the connection. If the socket does not close cleanly then the system will wait for a timeout, roughly three seconds, before marking the worker as failed and resuming smooth operation.

Scheduler Failure

The process containing the scheduler might die. There is currently no persistence mechanism to record and recover the scheduler state.

The workers and clients will all reconnect to the scheduler after it comes back online but records of ongoing computations will be lost.

Restart and Nanny Processes

The client provides a mechanism to restart all of the workers in the cluster. This is convenient if, during the course of experimentation, you find your workers in an inconvenient state that makes them unresponsive. The Client.restart method kills all workers, flushes all scheduler state, and then brings all workers back online, resulting in a clean cluster. This requires the nanny process (which is started by default).