#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""DaskRunner, executing remote jobs on Dask.distributed.
The DaskRunner is a runner implementation that executes a graph of
transformations across processes and workers via Dask distributed's
scheduler.
"""
import argparse
import collections
import dataclasses
import typing as t
from apache_beam import pvalue
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.pipeline import AppliedPTransform
from apache_beam.pipeline import PipelineVisitor
from apache_beam.runners.dask.overrides import dask_overrides
from apache_beam.runners.dask.transform_evaluator import TRANSLATIONS
from apache_beam.runners.dask.transform_evaluator import DaskBagWindowedIterator
from apache_beam.runners.dask.transform_evaluator import Flatten
from apache_beam.runners.dask.transform_evaluator import NoOp
from apache_beam.runners.direct.direct_runner import BundleBasedDirectRunner
from apache_beam.runners.runner import PipelineResult
from apache_beam.runners.runner import PipelineState
from apache_beam.transforms.sideinputs import SideInputMap
from apache_beam.utils.interactive_utils import is_in_notebook
try:
# Added to try to prevent threading related issues, see
# https://github.com/pytest-dev/pytest/issues/3216#issuecomment-1502451456
import dask.distributed as ddist
except ImportError:
ddist = {}
[docs]
class DaskOptions(PipelineOptions):
@staticmethod
def _parse_timeout(candidate):
try:
return int(candidate)
except (TypeError, ValueError):
import dask
return dask.config.no_default
@staticmethod
def _extract_bag_kwargs(dask_options: t.Dict) -> t.Dict:
"""Parse keyword arguments for `dask.Bag`s; used in graph translation."""
out = {}
if npartitions := dask_options.pop('npartitions', None):
out['npartitions'] = npartitions
if partition_size := dask_options.pop('partition_size', None):
out['partition_size'] = partition_size
return out
@classmethod
def _add_argparse_args(cls, parser: argparse.ArgumentParser) -> None:
parser.add_argument(
'--dask_client_address',
dest='address',
type=str,
default=None,
help='Address of a dask Scheduler server. Will default to a '
'`dask.LocalCluster()`.')
parser.add_argument(
'--dask_connection_timeout',
dest='timeout',
type=DaskOptions._parse_timeout,
help='Timeout duration for initial connection to the scheduler.')
parser.add_argument(
'--dask_scheduler_file',
dest='scheduler_file',
type=str,
default=None,
help='Path to a file with scheduler information if available.')
# TODO(alxr): Add options for security.
parser.add_argument(
'--dask_client_name',
dest='name',
type=str,
default=None,
help='Gives the client a name that will be included in logs generated '
'on the scheduler for matters relating to this client.')
parser.add_argument(
'--dask_connection_limit',
dest='connection_limit',
type=int,
default=512,
help='The number of open comms to maintain at once in the connection '
'pool.')
partitions_parser = parser.add_mutually_exclusive_group()
partitions_parser.add_argument(
'--dask_npartitions',
dest='npartitions',
type=int,
default=None,
help='The desired number of `dask.Bag` partitions. When unspecified, '
'an educated guess is made.')
partitions_parser.add_argument(
'--dask_partition_size',
dest='partition_size',
type=int,
default=None,
help='The length of each `dask.Bag` partition. When unspecified, '
'an educated guess is made.')
[docs]
@dataclasses.dataclass
class DaskRunnerResult(PipelineResult):
client: ddist.Client
futures: t.Sequence[ddist.Future]
def __post_init__(self):
super().__init__(PipelineState.RUNNING)
[docs]
def wait_until_finish(self, duration=None) -> str:
try:
if duration is not None:
# Convert milliseconds to seconds
duration /= 1000
for _ in ddist.as_completed(self.futures,
timeout=duration,
with_results=True):
# without gathering results, worker errors are not raised on the client:
# https://distributed.dask.org/en/stable/resilience.html#user-code-failures
# so we want to gather results to raise errors client-side, but we do
# not actually need to use the results here, so we just pass. to gather,
# we use the iterative `as_completed(..., with_results=True)`, instead
# of aggregate `client.gather`, to minimize memory footprint of results.
pass
self._state = PipelineState.DONE
except: # pylint: disable=broad-except
self._state = PipelineState.FAILED
raise
return self._state
[docs]
def cancel(self) -> str:
self._state = PipelineState.CANCELLING
self.client.cancel(self.futures)
self._state = PipelineState.CANCELLED
return self._state
[docs]
def metrics(self):
# TODO(alxr): Collect and return metrics...
raise NotImplementedError('collecting metrics will come later!')
[docs]
class DaskRunner(BundleBasedDirectRunner):
"""Executes a pipeline on a Dask distributed client."""
[docs]
@staticmethod
def to_dask_bag_visitor(bag_kwargs=None) -> PipelineVisitor:
from dask import bag as db
if bag_kwargs is None:
bag_kwargs = {}
@dataclasses.dataclass
class DaskBagVisitor(PipelineVisitor):
bags: t.Dict[AppliedPTransform, db.Bag] = dataclasses.field(
default_factory=collections.OrderedDict)
def visit_transform(self, transform_node: AppliedPTransform) -> None:
op_class = TRANSLATIONS.get(transform_node.transform.__class__, NoOp)
op = op_class(transform_node, bag_kwargs=bag_kwargs)
op_kws = {"input_bag": None, "side_inputs": None}
inputs = list(transform_node.inputs)
if inputs:
bag_inputs = []
for input_value in inputs:
if isinstance(input_value, pvalue.PBegin):
bag_inputs.append(None)
prev_op = input_value.producer
if prev_op in self.bags:
bag_inputs.append(self.bags[prev_op])
# Input to `Flatten` could be of length 1, e.g. a single-element
# tuple: `(pcoll, ) | beam.Flatten()`. If so, we still pass it as
# an iterable, because `Flatten.apply` always takes an iterable.
if len(bag_inputs) == 1 and not isinstance(op, Flatten):
op_kws["input_bag"] = bag_inputs[0]
else:
op_kws["input_bag"] = bag_inputs
side_inputs = list(transform_node.side_inputs)
if side_inputs:
bag_side_inputs = []
for si in side_inputs:
si_asbag = self.bags.get(si.pvalue.producer)
bag_side_inputs.append(
SideInputMap(
type(si),
si._view_options(),
DaskBagWindowedIterator(si_asbag, si._window_mapping_fn)))
op_kws["side_inputs"] = bag_side_inputs
self.bags[transform_node] = op.apply(**op_kws)
return DaskBagVisitor()
[docs]
@staticmethod
def is_fnapi_compatible():
return False
[docs]
def run_pipeline(self, pipeline, options):
import dask
# TODO(alxmrs): Create interactive notebook support.
if is_in_notebook():
raise NotImplementedError('interactive support will come later!')
try:
import dask.distributed as ddist
except ImportError:
raise ImportError(
'DaskRunner is not available. Please install apache_beam[dask].')
dask_options = options.view_as(DaskOptions).get_all_options(
drop_default=True)
bag_kwargs = DaskOptions._extract_bag_kwargs(dask_options)
client = ddist.Client(**dask_options)
pipeline.replace_all(dask_overrides())
dask_visitor = self.to_dask_bag_visitor(bag_kwargs)
pipeline.visit(dask_visitor)
# The dictionary in this visitor keeps a mapping of every Beam
# PTransform to the equivalent Bag operation. This is highly
# redundant. Thus, we can get away with computing just the last
# value, which should be connected to the full Bag Task Graph.
opt_graph = dask.optimize(list(dask_visitor.bags.values())[-1])
futures = client.compute(opt_graph)
return DaskRunnerResult(client, futures)