Source code for distributed.recreate_exceptions

from __future__ import print_function, division, absolute_import

import logging
from tornado import gen
from .client import futures_of, wait
from .utils import sync, tokey
from .utils_comm import pack_data
from .worker import _deserialize

logger = logging.getLogger(__name__)


class ReplayExceptionScheduler(object):
    """ A plugin for the scheduler to recreate exceptions locally

    This adds the following routes to the scheduler

    *  cause_of_failure
    """

    def __init__(self, scheduler):
        self.scheduler = scheduler
        self.scheduler.handlers['cause_of_failure'] = self.cause_of_failure
        self.scheduler.extensions['exceptions'] = self

    def cause_of_failure(self, *args, **kwargs):
        """
        Return details of first failed task required by set of keys

        Parameters
        ----------
        keys: list of keys known to the scheduler

        Returns
        -------
        Dictionary with:
        cause: the key that failed
        task: the definition of that key
        deps: keys that the task depends on
        """

        keys = kwargs.pop('keys', [])
        for key in keys:
            if isinstance(key, list):
                key = tuple(key)  # ensure not a list from msgpack
            key = tokey(key)
            ts = self.scheduler.tasks.get(key)
            if ts is not None and ts.exception_blame is not None:
                cause = ts.exception_blame
                # NOTE: cannot serialize sets
                return {'deps': [dts.key for dts in cause.dependencies],
                        'cause': cause.key,
                        'task': cause.run_spec}


[docs]class ReplayExceptionClient(object): """ A plugin for the client allowing replay of remote exceptions locally Adds the following methods (and their async variants)to the given client: - ``recreate_error_locally``: main user method - ``get_futures_error``: gets the task, its details and dependencies, responsible for failure of the given future. """ def __init__(self, client): self.client = client self.client.extensions['exceptions'] = self # monkey patch self.client.recreate_error_locally = self.recreate_error_locally self.client._recreate_error_locally = self._recreate_error_locally self.client._get_futures_error = self._get_futures_error self.client.get_futures_error = self.get_futures_error @property def scheduler(self): return self.client.scheduler @gen.coroutine def _get_futures_error(self, future): # only get errors for futures that errored. futures = [f for f in futures_of(future) if f.status == 'error'] if not futures: raise ValueError("No errored futures passed") out = yield self.scheduler.cause_of_failure( keys=[f.key for f in futures]) deps, task = out['deps'], out['task'] if isinstance(task, dict): function, args, kwargs = _deserialize(**task) raise gen.Return((function, args, kwargs, deps)) else: function, args, kwargs = _deserialize(task=task) raise gen.Return((function, args, kwargs, deps))
[docs] def get_futures_error(self, future): """ Ask the scheduler details of the sub-task of the given failed future When a future evaluates to a status of "error", i.e., an exception was raised in a task within its graph, we an get information from the scheduler. This function gets the details of the specific task that raised the exception and led to the error, but does not fetch data from the cluster or execute the function. Parameters ---------- future : future that failed, having ``status=="error"``, typically after an attempt to ``gather()`` shows a stack-stace. Returns ------- Tuple: - the function that raised an exception - argument list (a tuple), may include values and keys - keyword arguments (a dictionary), may include values and keys - list of keys that the function requires to be fetched to run See Also -------- ReplayExceptionClient.recreate_error_locally """ return self.client.sync(self._get_futures_error, future)
@gen.coroutine def _recreate_error_locally(self, future): yield wait(future) out = yield self._get_futures_error(future) function, args, kwargs, deps = out futures = self.client._graph_to_futures({}, deps) data = yield self.client._gather(futures) args = pack_data(args, data) kwargs = pack_data(kwargs, data) raise gen.Return((function, args, kwargs))
[docs] def recreate_error_locally(self, future): """ For a failed calculation, perform the blamed task locally for debugging. This operation should be performed after a future (result of ``gather``, ``compute``, etc) comes back with a status of "error", if the stack- trace is not informative enough to diagnose the problem. The specific task (part of the graph pointing to the future) responsible for the error will be fetched from the scheduler, together with the values of its inputs. The function will then be executed, so that ``pdb`` can be used for debugging. Examples -------- >>> future = c.submit(div, 1, 0) # doctest: +SKIP >>> future.status # doctest: +SKIP 'error' >>> c.recreate_error_locally(future) # doctest: +SKIP ZeroDivisionError: division by zero If you're in IPython you might take this opportunity to use pdb >>> %pdb # doctest: +SKIP Automatic pdb calling has been turned ON >>> c.recreate_error_locally(future) # doctest: +SKIP ZeroDivisionError: division by zero 1 def div(x, y): ----> 2 return x / y ipdb> Parameters ---------- future : future or collection that failed The same thing as was given to ``gather``, but came back with an exception/stack-trace. Can also be a (persisted) dask collection containing any errored futures. Returns ------- Nothing; the function runs and should raise an exception, allowing the debugger to run. """ func, args, kwargs = sync(self.client.loop, self._recreate_error_locally, future) func(*args, **kwargs)