#
# 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.
#
import collections
import functools
import json
import logging
import os
import pprint
import re
import uuid
from typing import Any
from typing import Iterable
from typing import List
from typing import Mapping
from typing import Set
import yaml
from yaml.loader import SafeLoader
import apache_beam as beam
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.transforms.fully_qualified_named_transform import FullyQualifiedNamedTransform
from apache_beam.yaml import yaml_provider
from apache_beam.yaml.yaml_combine import normalize_combine
from apache_beam.yaml.yaml_mapping import normalize_mapping
__all__ = ["YamlTransform"]
_LOGGER = logging.getLogger(__name__)
yaml_provider.fix_pycallable()
try:
import jsonschema
except ImportError:
jsonschema = None
@functools.lru_cache
def pipeline_schema(strictness):
with open(os.path.join(os.path.dirname(__file__),
'pipeline.schema.yaml')) as yaml_file:
pipeline_schema = yaml.safe_load(yaml_file)
if strictness == 'per_transform':
transform_schemas_path = os.path.join(
os.path.dirname(__file__), 'transforms.schema.yaml')
if not os.path.exists(transform_schemas_path):
raise RuntimeError(
"Please run "
"python -m apache_beam.yaml.generate_yaml_docs "
f"--schema_file='{transform_schemas_path}' "
"to run with transform-specific validation.")
with open(transform_schemas_path) as fin:
pipeline_schema['$defs']['transform']['allOf'].extend(yaml.safe_load(fin))
return pipeline_schema
def _closest_line(o, path):
best_line = SafeLineLoader.get_line(o)
for step in path:
o = o[step]
maybe_line = SafeLineLoader.get_line(o)
if maybe_line != 'unknown':
best_line = maybe_line
return best_line
def validate_against_schema(pipeline, strictness):
try:
jsonschema.validate(pipeline, pipeline_schema(strictness))
except jsonschema.ValidationError as exn:
exn.message += f" around line {_closest_line(pipeline, exn.path)}"
raise exn
def memoize_method(func):
def wrapper(self, *args):
if not hasattr(self, '_cache'):
self._cache = {}
key = func.__name__, args
if key not in self._cache:
self._cache[key] = func(self, *args)
return self._cache[key]
return wrapper
def only_element(xs):
x, = xs
return x
# These allow a user to explicitly pass no input to a transform (i.e. use it
# as a root transform) without an error even if the transform is not known to
# handle it.
def explicitly_empty():
return {'__explicitly_empty__': None}
def is_explicitly_empty(io):
return io == explicitly_empty()
def is_empty(io):
return not io or is_explicitly_empty(io)
def empty_if_explicitly_empty(io):
if is_explicitly_empty(io):
return {}
else:
return io
class SafeLineLoader(SafeLoader):
"""A yaml loader that attaches line information to mappings and strings."""
class TaggedString(str):
"""A string class to which we can attach metadata.
This is primarily used to trace a string's origin back to its place in a
yaml file.
"""
def __reduce__(self):
# Pickle as an ordinary string.
return str, (str(self), )
def construct_scalar(self, node):
value = super().construct_scalar(node)
if isinstance(value, str):
value = SafeLineLoader.TaggedString(value)
value._line_ = node.start_mark.line + 1
return value
def construct_mapping(self, node, deep=False):
mapping = super().construct_mapping(node, deep=deep)
mapping['__line__'] = node.start_mark.line + 1
mapping['__uuid__'] = self.create_uuid()
return mapping
@classmethod
def create_uuid(cls):
return str(uuid.uuid4())
@classmethod
def strip_metadata(cls, spec, tagged_str=True):
if isinstance(spec, Mapping):
return {
key: cls.strip_metadata(value, tagged_str)
for key,
value in spec.items() if key not in ('__line__', '__uuid__')
}
elif isinstance(spec, Iterable) and not isinstance(spec, (str, bytes)):
return [cls.strip_metadata(value, tagged_str) for value in spec]
elif isinstance(spec, SafeLineLoader.TaggedString) and tagged_str:
return str(spec)
else:
return spec
@staticmethod
def get_line(obj):
if isinstance(obj, dict):
return obj.get('__line__', 'unknown')
else:
return getattr(obj, '_line_', 'unknown')
class LightweightScope(object):
def __init__(self, transforms):
self._transforms = transforms
self._transforms_by_uuid = {t['__uuid__']: t for t in self._transforms}
self._uuid_by_name = collections.defaultdict(set)
for spec in self._transforms:
if 'name' in spec:
self._uuid_by_name[spec['name']].add(spec['__uuid__'])
if 'type' in spec:
self._uuid_by_name[spec['type']].add(spec['__uuid__'])
def get_transform_id_and_output_name(self, name):
if '.' in name:
transform_name, output = name.rsplit('.', 1)
else:
transform_name, output = name, None
return self.get_transform_id(transform_name), output
def get_transform_id(self, transform_name):
if transform_name in self._transforms_by_uuid:
return transform_name
else:
candidates = self._uuid_by_name[transform_name]
if not candidates:
raise ValueError(
f'Unknown transform at line '
f'{SafeLineLoader.get_line(transform_name)}: {transform_name}')
elif len(candidates) > 1:
raise ValueError(
f'Ambiguous transform at line '
f'{SafeLineLoader.get_line(transform_name)}: {transform_name}')
else:
return only_element(candidates)
class Scope(LightweightScope):
"""To look up PCollections (typically outputs of prior transforms) by name."""
def __init__(
self,
root,
inputs: Mapping[str, Any],
transforms: Iterable[dict],
providers: Mapping[str, Iterable[yaml_provider.Provider]],
input_providers: Iterable[yaml_provider.Provider]):
super().__init__(transforms)
self.root = root
self._inputs = inputs
self.providers = providers
self._seen_names: Set[str] = set()
self.input_providers = input_providers
self._all_followers = None
def followers(self, transform_name):
if self._all_followers is None:
self._all_followers = collections.defaultdict(list)
# TODO(yaml): Also trace through outputs and composites.
for transform in self._transforms:
if transform['type'] != 'composite':
for input in empty_if_explicitly_empty(transform['input']).values():
if input not in self._inputs:
transform_id, _ = self.get_transform_id_and_output_name(input)
self._all_followers[transform_id].append(transform['__uuid__'])
return self._all_followers[self.get_transform_id(transform_name)]
def compute_all(self):
for transform_id in self._transforms_by_uuid.keys():
self.compute_outputs(transform_id)
def get_pcollection(self, name):
if name in self._inputs:
return self._inputs[name]
elif '.' in name:
transform, output = name.rsplit('.', 1)
outputs = self.get_outputs(transform)
if output in outputs:
return outputs[output]
elif len(outputs) == 1 and outputs[next(iter(outputs))].tag == output:
return outputs[next(iter(outputs))]
else:
raise ValueError(
f'Unknown output {repr(output)} '
f'at line {SafeLineLoader.get_line(name)}: '
f'{transform} only has outputs {list(outputs.keys())}')
else:
outputs = self.get_outputs(name)
if len(outputs) == 1:
return only_element(outputs.values())
else:
error_output = self._transforms_by_uuid[self.get_transform_id(
name)]['config'].get('error_handling', {}).get('output')
if error_output and error_output in outputs and len(outputs) == 2:
return next(
output for tag, output in outputs.items() if tag != error_output)
raise ValueError(
f'Ambiguous output at line {SafeLineLoader.get_line(name)}: '
f'{name} has outputs {list(outputs.keys())}')
def get_outputs(self, transform_name):
return self.compute_outputs(self.get_transform_id(transform_name))
@memoize_method
def compute_outputs(self, transform_id):
return expand_transform(self._transforms_by_uuid[transform_id], self)
def best_provider(
self, t, input_providers: yaml_provider.Iterable[yaml_provider.Provider]):
if isinstance(t, dict):
spec = t
else:
spec = self._transforms_by_uuid[self.get_transform_id(t)]
possible_providers = [
p for p in self.providers[spec['type']] if p.available()
]
if not possible_providers:
raise ValueError(
'No available provider for type %r at %s' %
(spec['type'], identify_object(spec)))
# From here on, we have the invariant that possible_providers is not empty.
# Only one possible provider, no need to rank further.
if len(possible_providers) == 1:
return possible_providers[0]
def best_matches(
possible_providers: Iterable[yaml_provider.Provider],
adjacent_provider_options: Iterable[Iterable[yaml_provider.Provider]]
) -> List[yaml_provider.Provider]:
"""Given a set of possible providers, and a set of providers for each
adjacent transform, returns the top possible providers as ranked by
affinity to the adjacent transforms' providers.
"""
providers_by_score = collections.defaultdict(list)
for p in possible_providers:
# The sum of the affinity of the best provider
# for each adjacent transform.
providers_by_score[sum(
max(p.affinity(ap) for ap in apo)
for apo in adjacent_provider_options)].append(p)
return providers_by_score[max(providers_by_score.keys())]
# If there are any inputs, prefer to match them.
if input_providers:
possible_providers = best_matches(
possible_providers, [[p] for p in input_providers])
# Without __uuid__ we can't find downstream operations.
if '__uuid__' not in spec:
return possible_providers[0]
# Match against downstream transforms, continuing until there is no tie
# or we run out of downstream transforms.
if len(possible_providers) > 1:
adjacent_transforms = list(self.followers(spec['__uuid__']))
while adjacent_transforms:
# This is a list of all possible providers for each adjacent transform.
adjacent_provider_options = [[
p for p in self.providers[self._transforms_by_uuid[t]['type']]
if p.available()
] for t in adjacent_transforms]
if any(not apo for apo in adjacent_provider_options):
# One of the transforms had no available providers.
# We will throw an error later, doesn't matter what we return.
break
# Filter down the set of possible providers to the best ones.
possible_providers = best_matches(
possible_providers, adjacent_provider_options)
# If we are down to one option, no need to go further.
if len(possible_providers) == 1:
break
# Go downstream one more step.
adjacent_transforms = sum(
[list(self.followers(t)) for t in adjacent_transforms], [])
return possible_providers[0]
# A method on scope as providers may be scoped...
def create_ptransform(self, spec, input_pcolls):
if 'type' not in spec:
raise ValueError(f'Missing transform type: {identify_object(spec)}')
if spec['type'] not in self.providers:
raise ValueError(
'Unknown transform type %r at %s' %
(spec['type'], identify_object(spec)))
# TODO(yaml): Perhaps we can do better than a greedy choice here.
# TODO(yaml): Figure out why this is needed.
providers_by_input = {k: v for k, v in self.input_providers.items()}
input_providers = [
providers_by_input[pcoll] for pcoll in input_pcolls
if pcoll in providers_by_input
]
provider = self.best_provider(spec, input_providers)
config = SafeLineLoader.strip_metadata(spec.get('config', {}))
if not isinstance(config, dict):
raise ValueError(
'Config for transform at %s must be a mapping.' %
identify_object(spec))
if (not input_pcolls and not is_explicitly_empty(spec.get('input', {})) and
provider.requires_inputs(spec['type'], config)):
raise ValueError(
f'Missing inputs for transform at {identify_object(spec)}')
try:
# pylint: disable=undefined-loop-variable
ptransform = provider.create_transform(
spec['type'], config, self.create_ptransform)
# TODO(robertwb): Should we have a better API for adding annotations
# than this?
annotations = dict(
yaml_type=spec['type'],
yaml_args=json.dumps(config),
yaml_provider=json.dumps(provider.to_json()),
**ptransform.annotations())
ptransform.annotations = lambda: annotations
original_expand = ptransform.expand
def recording_expand(pvalue):
result = original_expand(pvalue)
def record_providers(pvalueish):
if isinstance(pvalueish, (tuple, list)):
for p in pvalueish:
record_providers(p)
elif isinstance(pvalueish, dict):
for p in pvalueish.values():
record_providers(p)
elif isinstance(pvalueish, beam.PCollection):
if pvalueish not in self.input_providers:
self.input_providers[pvalueish] = provider
record_providers(result)
return result
ptransform.expand = recording_expand
return ptransform
except Exception as exn:
if isinstance(exn, TypeError):
# Create a slightly more generic error message for argument errors.
msg = str(exn).replace('positional', '').replace('keyword', '')
msg = re.sub(r'\S+lambda\S+', '', msg)
msg = re.sub(' +', ' ', msg).strip()
else:
msg = str(exn)
raise ValueError(
f'Invalid transform specification at {identify_object(spec)}: {msg}'
) from exn
def unique_name(self, spec, ptransform, strictness=0):
if 'name' in spec:
name = spec['name']
strictness += 1
elif 'ExternalTransform' not in ptransform.label:
# The label may have interesting information.
name = ptransform.label
else:
name = spec['type']
if name in self._seen_names:
if strictness >= 2:
raise ValueError(f'Duplicate name at {identify_object(spec)}: {name}')
else:
name = f'{name}@{SafeLineLoader.get_line(spec)}'
self._seen_names.add(name)
return name
def expand_transform(spec, scope):
if 'type' not in spec:
raise TypeError(
f'Missing type parameter for transform at {identify_object(spec)}')
type = spec['type']
if type == 'composite':
return expand_composite_transform(spec, scope)
else:
return expand_leaf_transform(spec, scope)
def expand_leaf_transform(spec, scope):
spec = normalize_inputs_outputs(spec)
inputs_dict = {
key: scope.get_pcollection(value)
for (key, value) in empty_if_explicitly_empty(spec['input']).items()
}
input_type = spec.get('input_type', 'default')
if input_type == 'list':
inputs = tuple(inputs_dict.values())
elif input_type == 'map':
inputs = inputs_dict
else:
if len(inputs_dict) == 0:
inputs = scope.root
elif len(inputs_dict) == 1:
inputs = next(iter(inputs_dict.values()))
else:
inputs = inputs_dict
_LOGGER.info("Expanding %s ", identify_object(spec))
ptransform = scope.create_ptransform(spec, inputs_dict.values())
try:
# TODO: Move validation to construction?
with FullyQualifiedNamedTransform.with_filter('*'):
outputs = inputs | scope.unique_name(spec, ptransform) >> ptransform
except Exception as exn:
raise ValueError(
f"Error apply transform {identify_object(spec)}: {exn}") from exn
if isinstance(outputs, dict):
# TODO: Handle (or at least reject) nested case.
return outputs
elif isinstance(outputs, (tuple, list)):
return {f'out{ix}': pcoll for (ix, pcoll) in enumerate(outputs)}
elif isinstance(outputs, beam.PCollection):
return {'out': outputs}
elif outputs is None:
return {}
else:
raise ValueError(
f'Transform {identify_object(spec)} returned an unexpected type '
f'{type(outputs)}')
def expand_composite_transform(spec, scope):
spec = normalize_inputs_outputs(normalize_source_sink(spec))
inner_scope = Scope(
scope.root,
{
key: scope.get_pcollection(value)
for (key, value) in empty_if_explicitly_empty(spec['input']).items()
},
spec['transforms'],
yaml_provider.merge_providers(
yaml_provider.parse_providers(spec.get('providers', [])),
scope.providers),
scope.input_providers)
class CompositePTransform(beam.PTransform):
@staticmethod
def expand(inputs):
inner_scope.compute_all()
return {
key: inner_scope.get_pcollection(value)
for (key, value) in spec['output'].items()
}
if 'name' not in spec:
spec['name'] = 'Composite'
if spec['name'] is None: # top-level pipeline, don't nest
return CompositePTransform.expand(None)
else:
_LOGGER.info("Expanding %s ", identify_object(spec))
return ({
key: scope.get_pcollection(value)
for (key, value) in empty_if_explicitly_empty(spec['input']).items()
} or scope.root) | scope.unique_name(spec, None) >> CompositePTransform()
def expand_chain_transform(spec, scope):
return expand_composite_transform(chain_as_composite(spec), scope)
def chain_as_composite(spec):
def is_not_output_of_last_transform(new_transforms, value):
return (
('name' in new_transforms[-1] and
value != new_transforms[-1]['name']) or
('type' in new_transforms[-1] and value != new_transforms[-1]['type']))
# A chain is simply a composite transform where all inputs and outputs
# are implicit.
spec = normalize_source_sink(spec)
if 'transforms' not in spec:
raise TypeError(
f"Chain at {identify_object(spec)} missing transforms property.")
has_explicit_outputs = 'output' in spec
composite_spec = normalize_inputs_outputs(tag_explicit_inputs(spec))
new_transforms = []
for ix, transform in enumerate(composite_spec['transforms']):
if any(io in transform for io in ('input', 'output')):
if (ix == 0 and 'input' in transform and 'output' not in transform and
is_explicitly_empty(transform['input'])):
# This is OK as source clause sets an explicitly empty input.
pass
else:
raise ValueError(
f'Transform {identify_object(transform)} is part of a chain, '
'must have implicit inputs and outputs.')
if ix == 0:
if is_explicitly_empty(transform.get('input', None)):
pass
elif is_explicitly_empty(composite_spec['input']):
transform['input'] = composite_spec['input']
elif is_empty(composite_spec['input']):
del composite_spec['input']
else:
transform['input'] = {
key: key
for key in composite_spec['input'].keys()
}
else:
transform['input'] = new_transforms[-1]['__uuid__']
new_transforms.append(transform)
new_transforms.extend(spec.get('extra_transforms', []))
composite_spec['transforms'] = new_transforms
last_transform = new_transforms[-1]['__uuid__']
if has_explicit_outputs:
for (key, value) in composite_spec['output'].items():
if is_not_output_of_last_transform(new_transforms, value):
raise ValueError(
f"Explicit output {identify_object(value)} of the chain transform"
f" is not an output of the last transform.")
composite_spec['output'] = {
key: f'{last_transform}.{value}'
for (key, value) in composite_spec['output'].items()
}
else:
composite_spec['output'] = last_transform
if 'name' not in composite_spec:
composite_spec['name'] = 'Chain'
composite_spec['type'] = 'composite'
return composite_spec
def preprocess_chain(spec):
if spec['type'] == 'chain':
return chain_as_composite(spec)
else:
return spec
def pipeline_as_composite(spec):
if isinstance(spec, list):
return {
'type': 'composite',
'name': None,
'transforms': spec,
'__line__': spec[0]['__line__'],
'__uuid__': SafeLineLoader.create_uuid(),
}
else:
return dict(spec, name=None, type=spec.get('type', 'composite'))
def normalize_source_sink(spec):
if 'source' not in spec and 'sink' not in spec:
return spec
spec = dict(spec)
spec['transforms'] = list(spec.get('transforms', []))
if 'source' in spec:
if 'input' not in spec['source']:
spec['source']['input'] = explicitly_empty()
spec['transforms'].insert(0, spec.pop('source'))
if 'sink' in spec:
spec['transforms'].append(spec.pop('sink'))
return spec
def preprocess_source_sink(spec):
if spec['type'] in ('chain', 'composite'):
return normalize_source_sink(spec)
else:
return spec
def tag_explicit_inputs(spec):
if 'input' in spec and not SafeLineLoader.strip_metadata(spec['input']):
return dict(spec, input=explicitly_empty())
else:
return spec
def normalize_inputs_outputs(spec):
spec = dict(spec)
def normalize_io(tag):
io = spec.get(tag, {})
if isinstance(io, (str, list)):
return {tag: io}
else:
return SafeLineLoader.strip_metadata(io, tagged_str=False)
return dict(spec, input=normalize_io('input'), output=normalize_io('output'))
def identify_object(spec):
line = SafeLineLoader.get_line(spec)
name = extract_name(spec)
if name:
return f'"{name}" at line {line}'
else:
return f'at line {line}'
def extract_name(spec):
if isinstance(spec, dict):
if 'name' in spec:
return spec['name']
elif 'id' in spec:
return spec['id']
elif 'type' in spec:
return spec['type']
elif len(spec) == 1:
return extract_name(next(iter(spec.values())))
else:
return ''
elif isinstance(spec, str):
return spec
else:
return ''
def push_windowing_to_roots(spec):
scope = LightweightScope(spec['transforms'])
consumed_outputs_by_transform = collections.defaultdict(set)
for transform in spec['transforms']:
for _, input_ref in empty_if_explicitly_empty(transform['input']).items():
try:
transform_id, output = scope.get_transform_id_and_output_name(input_ref)
consumed_outputs_by_transform[transform_id].add(output)
except ValueError:
# Could be an input or an ambiguity we'll raise later.
pass
for transform in spec['transforms']:
if is_empty(transform['input']) and 'windowing' not in transform:
transform['windowing'] = spec['windowing']
transform['__consumed_outputs'] = consumed_outputs_by_transform[
transform['__uuid__']]
return spec
def preprocess_windowing(spec):
if spec['type'] == 'WindowInto':
# This is the transform where it is actually applied.
if 'windowing' in spec:
spec['config'] = spec.get('config', {})
spec['config']['windowing'] = spec.pop('windowing')
return spec
elif 'windowing' not in spec:
# Nothing to do.
return spec
if spec['type'] == 'composite':
# Apply the windowing to any reads, creates, etc. in this transform
# TODO(robertwb): Better handle the case where a read is followed by a
# setting of the timestamps. We should be careful of sliding windows
# in particular.
spec = push_windowing_to_roots(spec)
windowing = spec.pop('windowing')
if not is_empty(spec['input']):
# Apply the windowing to all inputs by wrapping it in a transform that
# first applies windowing and then applies the original transform.
original_inputs = spec['input']
windowing_transforms = [{
'type': 'WindowInto',
'name': f'WindowInto[{key}]',
'windowing': windowing,
'input': {
'input': key
},
'__line__': spec['__line__'],
'__uuid__': SafeLineLoader.create_uuid(),
} for key in original_inputs.keys()]
windowed_inputs = {
key: t['__uuid__']
for (key, t) in zip(original_inputs.keys(), windowing_transforms)
}
modified_spec = dict(
spec, input=windowed_inputs, __uuid__=SafeLineLoader.create_uuid())
return {
'type': 'composite',
'name': spec.get('name', None) or spec['type'],
'transforms': [modified_spec] + windowing_transforms,
'input': spec['input'],
'output': modified_spec['__uuid__'],
'__line__': spec['__line__'],
'__uuid__': spec['__uuid__'],
}
elif spec['type'] == 'composite':
# Pushing the windowing down was sufficient.
return spec
else:
# No inputs, apply the windowing to all outputs.
consumed_outputs = list(spec.pop('__consumed_outputs', {None}))
modified_spec = dict(spec, __uuid__=SafeLineLoader.create_uuid())
windowing_transforms = [{
'type': 'WindowInto',
'name': f'WindowInto[{out}]',
'windowing': windowing,
'input': {
'input': modified_spec['__uuid__'] + ('.' + out if out else '')
},
'__line__': spec['__line__'],
'__uuid__': SafeLineLoader.create_uuid(),
} for out in consumed_outputs]
if consumed_outputs == [None]:
windowed_outputs = only_element(windowing_transforms)['__uuid__']
else:
windowed_outputs = {
out: t['__uuid__']
for (out, t) in zip(consumed_outputs, windowing_transforms)
}
return {
'type': 'composite',
'name': spec.get('name', None) or spec['type'],
'transforms': [modified_spec] + windowing_transforms,
'output': windowed_outputs,
'__line__': spec['__line__'],
'__uuid__': spec['__uuid__'],
}
def preprocess_flattened_inputs(spec):
if spec['type'] != 'composite':
return spec
# Prefer to add the flattens as sibling operations rather than nesting
# to keep graph shape consistent when the number of inputs goes from
# one to multiple.
new_transforms = []
for t in spec['transforms']:
if t['type'] == 'Flatten':
# Don't flatten before explicit flatten.
# But we do have to expand list inputs into singleton inputs.
def all_inputs(t):
for key, values in t.get('input', {}).items():
if isinstance(values, list):
for ix, values in enumerate(values):
yield f'{key}{ix}', values
else:
yield key, values
inputs_dict = {}
for key, value in all_inputs(t):
while key in inputs_dict:
key += '_'
inputs_dict[key] = value
t = dict(t, input=inputs_dict)
else:
replaced_inputs = {}
for key, values in t.get('input', {}).items():
if isinstance(values, list):
flatten_id = SafeLineLoader.create_uuid()
new_transforms.append({
'type': 'Flatten',
'name': '%s-Flatten[%s]' % (t.get('name', t['type']), key),
'input': {
f'input{ix}': value
for (ix, value) in enumerate(values)
},
'__line__': spec['__line__'],
'__uuid__': flatten_id,
})
replaced_inputs[key] = flatten_id
if replaced_inputs:
t = dict(t, input={**t['input'], **replaced_inputs})
new_transforms.append(t)
return dict(spec, transforms=new_transforms)
def ensure_transforms_have_types(spec):
if 'type' not in spec:
raise ValueError(f'Missing type specification in {identify_object(spec)}')
return spec
def ensure_errors_consumed(spec):
if spec['type'] == 'composite':
scope = LightweightScope(spec['transforms'])
to_handle = {}
consumed = set(
scope.get_transform_id_and_output_name(output)
for output in spec['output'].values())
for t in spec['transforms']:
config = t.get('config', t)
if 'error_handling' in config:
if 'output' not in config['error_handling']:
raise ValueError(
f'Missing output in error_handling of {identify_object(t)}')
to_handle[t['__uuid__'], config['error_handling']['output']] = t
for _, input in empty_if_explicitly_empty(t['input']).items():
if input not in spec['input']:
consumed.add(scope.get_transform_id_and_output_name(input))
for error_pcoll, t in to_handle.items():
if error_pcoll not in consumed:
raise ValueError(f'Unconsumed error output for {identify_object(t)}.')
return spec
def lift_config(spec):
if 'config' not in spec:
common_params = 'name', 'type', 'input', 'output', 'transforms'
return {
'config': {k: v
for (k, v) in spec.items() if k not in common_params},
**{
k: v
for (k, v) in spec.items() #
if k in common_params or k in ('__line__', '__uuid__')
}
}
else:
return spec
def ensure_config(spec):
if 'config' not in spec:
spec['config'] = {}
return spec
def preprocess(spec, verbose=False, known_transforms=None):
if verbose:
pprint.pprint(spec)
def apply(phase, spec):
spec = phase(spec)
if spec['type'] in {'composite', 'chain'} and 'transforms' in spec:
spec = dict(
spec, transforms=[apply(phase, t) for t in spec['transforms']])
return spec
if known_transforms:
known_transforms = set(known_transforms).union(['chain', 'composite'])
def ensure_transforms_have_providers(spec):
if known_transforms:
if spec['type'] not in known_transforms:
raise ValueError(
'Unknown type or missing provider '
f'for type {spec["type"]} for {identify_object(spec)}')
return spec
def preprocess_langauges(spec):
if spec['type'] in ('Filter', 'MapToFields', 'Combine', 'AssignTimestamps'):
language = spec.get('config', {}).get('language', 'generic')
new_type = spec['type'] + '-' + language
if known_transforms and new_type not in known_transforms:
if language == 'generic':
raise ValueError(f'Missing language for {identify_object(spec)}')
else:
raise ValueError(
f'Unknown language {language} for {identify_object(spec)}')
return dict(spec, type=new_type, name=spec.get('name', spec['type']))
else:
return spec
for phase in [
ensure_transforms_have_types,
normalize_mapping,
normalize_combine,
preprocess_langauges,
ensure_transforms_have_providers,
preprocess_source_sink,
preprocess_chain,
tag_explicit_inputs,
normalize_inputs_outputs,
preprocess_flattened_inputs,
ensure_errors_consumed,
preprocess_windowing,
# TODO(robertwb): Consider enabling this by default, or as an option.
# lift_config,
ensure_config,
]:
spec = apply(phase, spec)
if verbose:
print('=' * 20, phase, '=' * 20)
pprint.pprint(spec)
return spec
def expand_pipeline(
pipeline,
pipeline_spec,
providers=None,
validate_schema='generic' if jsonschema is not None else None):
if isinstance(pipeline_spec, str):
pipeline_spec = yaml.load(pipeline_spec, Loader=SafeLineLoader)
# TODO(robertwb): It's unclear whether this gives as good of errors, but
# this could certainly be handy as a first pass when Beam is not available.
if validate_schema and validate_schema != 'none':
validate_against_schema(pipeline_spec, validate_schema)
# Calling expand directly to avoid outer layer of nesting.
return YamlTransform(
pipeline_as_composite(pipeline_spec['pipeline']),
{
**yaml_provider.parse_providers(pipeline_spec.get('providers', [])),
**{
key: yaml_provider.as_provider_list(key, value)
for (key, value) in (providers or {}).items()
}
}).expand(beam.pvalue.PBegin(pipeline))