| # |
| # 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. |
| # |
| |
| """This module defines Providers usable from yaml, which is a specification |
| for where to find and how to invoke services that vend implementations of |
| various PTransforms.""" |
| |
| import collections |
| import hashlib |
| import inspect |
| import json |
| import logging |
| import os |
| import re |
| import shutil |
| import subprocess |
| import sys |
| import urllib.parse |
| from typing import Any |
| from typing import Callable |
| from typing import Dict |
| from typing import Iterable |
| from typing import Mapping |
| from typing import Optional |
| |
| import docstring_parser |
| import yaml |
| from yaml.loader import SafeLoader |
| |
| import apache_beam as beam |
| import apache_beam.dataframe.io |
| import apache_beam.io |
| import apache_beam.transforms.util |
| from apache_beam.portability.api import schema_pb2 |
| from apache_beam.runners import pipeline_context |
| from apache_beam.testing.util import assert_that |
| from apache_beam.testing.util import equal_to |
| from apache_beam.transforms import external |
| from apache_beam.transforms import window |
| from apache_beam.transforms.fully_qualified_named_transform import FullyQualifiedNamedTransform |
| from apache_beam.typehints import schemas |
| from apache_beam.typehints import trivial_inference |
| from apache_beam.typehints.schemas import named_tuple_to_schema |
| from apache_beam.typehints.schemas import typing_to_runner_api |
| from apache_beam.utils import python_callable |
| from apache_beam.utils import subprocess_server |
| from apache_beam.version import __version__ as beam_version |
| |
| |
| class Provider: |
| """Maps transform types names and args to concrete PTransform instances.""" |
| def available(self) -> bool: |
| """Returns whether this provider is available to use in this environment.""" |
| raise NotImplementedError(type(self)) |
| |
| def cache_artifacts(self) -> Optional[Iterable[str]]: |
| raise NotImplementedError(type(self)) |
| |
| def provided_transforms(self) -> Iterable[str]: |
| """Returns a list of transform type names this provider can handle.""" |
| raise NotImplementedError(type(self)) |
| |
| def config_schema(self, type): |
| return None |
| |
| def description(self, type): |
| return None |
| |
| def requires_inputs(self, typ: str, args: Mapping[str, Any]) -> bool: |
| """Returns whether this transform requires inputs. |
| |
| Specifically, if this returns True and inputs are not provided than an error |
| will be thrown. |
| |
| This is best-effort, primarily for better and earlier error messages. |
| """ |
| return not typ.startswith('Read') |
| |
| def create_transform( |
| self, |
| typ: str, |
| args: Mapping[str, Any], |
| yaml_create_transform: Callable[ |
| [Mapping[str, Any], Iterable[beam.PCollection]], beam.PTransform] |
| ) -> beam.PTransform: |
| """Creates a PTransform instance for the given transform type and arguments. |
| """ |
| raise NotImplementedError(type(self)) |
| |
| def underlying_provider(self): |
| """If this provider is simply a proxy to another provider, return the |
| provider that should actually be used for affinity checking. |
| """ |
| return self |
| |
| def affinity(self, other: "Provider"): |
| """Returns a value approximating how good it would be for this provider |
| to be used immediately following a transform from the other provider |
| (e.g. to encourage fusion). |
| """ |
| # TODO(yaml): This is a very rough heuristic. Consider doing better. |
| # E.g. we could look at the the expected environments themselves. |
| # Possibly, we could provide multiple expansions and have the runner itself |
| # choose the actual implementation based on fusion (and other) criteria. |
| a = self.underlying_provider() |
| b = other.underlying_provider() |
| return a._affinity(b) + b._affinity(a) |
| |
| def _affinity(self, other: "Provider"): |
| if self is other or self == other: |
| return 100 |
| elif type(self) == type(other): |
| return 10 |
| else: |
| return 0 |
| |
| |
| def as_provider(name, provider_or_constructor): |
| if isinstance(provider_or_constructor, Provider): |
| return provider_or_constructor |
| else: |
| return InlineProvider({name: provider_or_constructor}) |
| |
| |
| def as_provider_list(name, lst): |
| if not isinstance(lst, list): |
| return as_provider_list(name, [lst]) |
| return [as_provider(name, x) for x in lst] |
| |
| |
| class ExternalProvider(Provider): |
| """A Provider implemented via the cross language transform service.""" |
| _provider_types: Dict[str, Callable[..., Provider]] = {} |
| |
| def __init__(self, urns, service): |
| self._urns = urns |
| self._service = service |
| self._schema_transforms = None |
| |
| def provided_transforms(self): |
| return self._urns.keys() |
| |
| def schema_transforms(self): |
| if callable(self._service): |
| self._service = self._service() |
| if self._schema_transforms is None: |
| try: |
| self._schema_transforms = { |
| config.identifier: config |
| for config in external.SchemaAwareExternalTransform.discover( |
| self._service, ignore_errors=True) |
| } |
| except Exception: |
| # It's possible this service doesn't vend schema transforms. |
| self._schema_transforms = {} |
| return self._schema_transforms |
| |
| def config_schema(self, type): |
| if self._urns[type] in self.schema_transforms(): |
| return named_tuple_to_schema( |
| self.schema_transforms()[self._urns[type]].configuration_schema) |
| |
| def description(self, type): |
| if self._urns[type] in self.schema_transforms(): |
| return self.schema_transforms()[self._urns[type]].description |
| |
| def requires_inputs(self, typ, args): |
| if self._urns[typ] in self.schema_transforms(): |
| return bool(self.schema_transforms()[self._urns[typ]].inputs) |
| else: |
| return super().requires_inputs(typ, args) |
| |
| def create_transform(self, type, args, yaml_create_transform): |
| if callable(self._service): |
| self._service = self._service() |
| urn = self._urns[type] |
| if urn in self.schema_transforms(): |
| return external.SchemaAwareExternalTransform( |
| urn, self._service, rearrange_based_on_discovery=True, **args) |
| else: |
| return type >> self.create_external_transform(urn, args) |
| |
| def create_external_transform(self, urn, args): |
| return external.ExternalTransform( |
| urn, |
| external.ImplicitSchemaPayloadBuilder(args).payload(), |
| self._service) |
| |
| @classmethod |
| def provider_from_spec(cls, spec): |
| from apache_beam.yaml.yaml_transform import SafeLineLoader |
| for required in ('type', 'transforms'): |
| if required not in spec: |
| raise ValueError( |
| f'Missing {required} in provider ' |
| f'at line {SafeLineLoader.get_line(spec)}') |
| urns = SafeLineLoader.strip_metadata(spec['transforms']) |
| type = spec['type'] |
| config = SafeLineLoader.strip_metadata(spec.get('config', {})) |
| extra_params = set(SafeLineLoader.strip_metadata(spec).keys()) - { |
| 'transforms', 'type', 'config' |
| } |
| if extra_params: |
| raise ValueError( |
| f'Unexpected parameters in provider of type {type} ' |
| f'at line {SafeLineLoader.get_line(spec)}: {extra_params}') |
| if config.get('version', None) == 'BEAM_VERSION': |
| config['version'] = beam_version |
| if type in cls._provider_types: |
| try: |
| return cls._provider_types[type](urns, **config) |
| except Exception as exn: |
| raise ValueError( |
| f'Unable to instantiate provider of type {type} ' |
| f'at line {SafeLineLoader.get_line(spec)}: {exn}') from exn |
| else: |
| raise NotImplementedError( |
| f'Unknown provider type: {type} ' |
| f'at line {SafeLineLoader.get_line(spec)}.') |
| |
| @classmethod |
| def register_provider_type(cls, type_name): |
| def apply(constructor): |
| cls._provider_types[type_name] = constructor |
| return constructor |
| |
| return apply |
| |
| |
| @ExternalProvider.register_provider_type('javaJar') |
| def java_jar(urns, jar: str): |
| if not os.path.exists(jar): |
| parsed = urllib.parse.urlparse(jar) |
| if not parsed.scheme or not parsed.netloc: |
| raise ValueError(f'Invalid path or url: {jar}') |
| return ExternalJavaProvider(urns, lambda: jar) |
| |
| |
| @ExternalProvider.register_provider_type('mavenJar') |
| def maven_jar( |
| urns, |
| *, |
| artifact_id, |
| group_id, |
| version, |
| repository=subprocess_server.JavaJarServer.MAVEN_CENTRAL_REPOSITORY, |
| classifier=None, |
| appendix=None): |
| return ExternalJavaProvider( |
| urns, |
| lambda: subprocess_server.JavaJarServer.path_to_maven_jar( |
| artifact_id=artifact_id, |
| group_id=group_id, |
| version=version, |
| repository=repository, |
| classifier=classifier, |
| appendix=appendix)) |
| |
| |
| @ExternalProvider.register_provider_type('beamJar') |
| def beam_jar( |
| urns, |
| *, |
| gradle_target, |
| appendix=None, |
| version=beam_version, |
| artifact_id=None): |
| return ExternalJavaProvider( |
| urns, |
| lambda: subprocess_server.JavaJarServer.path_to_beam_jar( |
| gradle_target=gradle_target, version=version, artifact_id=artifact_id) |
| ) |
| |
| |
| @ExternalProvider.register_provider_type('docker') |
| def docker(urns, **config): |
| raise NotImplementedError() |
| |
| |
| @ExternalProvider.register_provider_type('remote') |
| class RemoteProvider(ExternalProvider): |
| _is_available = None |
| |
| def __init__(self, urns, address: str): |
| super().__init__(urns, service=address) |
| |
| def available(self): |
| if self._is_available is None: |
| try: |
| with external.ExternalTransform.service(self._service) as service: |
| service.ready(1) |
| self._is_available = True |
| except Exception: |
| self._is_available = False |
| return self._is_available |
| |
| def cache_artifacts(self): |
| pass |
| |
| |
| class ExternalJavaProvider(ExternalProvider): |
| def __init__(self, urns, jar_provider): |
| super().__init__( |
| urns, lambda: external.JavaJarExpansionService(jar_provider())) |
| self._jar_provider = jar_provider |
| |
| def available(self): |
| # pylint: disable=subprocess-run-check |
| return subprocess.run(['which', 'java'], |
| capture_output=True).returncode == 0 |
| |
| def cache_artifacts(self): |
| return [self._jar_provider()] |
| |
| |
| @ExternalProvider.register_provider_type('python') |
| def python(urns, packages=()): |
| if packages: |
| return ExternalPythonProvider(urns, packages) |
| else: |
| return InlineProvider({ |
| name: |
| python_callable.PythonCallableWithSource.load_from_source(constructor) |
| for (name, constructor) in urns.items() |
| }) |
| |
| |
| @ExternalProvider.register_provider_type('pythonPackage') |
| class ExternalPythonProvider(ExternalProvider): |
| def __init__(self, urns, packages): |
| super().__init__(urns, PypiExpansionService(packages)) |
| |
| def available(self): |
| return True # If we're running this script, we have Python installed. |
| |
| def cache_artifacts(self): |
| return [self._service._venv()] |
| |
| def create_external_transform(self, urn, args): |
| # Python transforms are "registered" by fully qualified name. |
| if not re.match(r'^[\w.]*$', urn): |
| # Treat it as source. |
| args = {'source': urn, **args} |
| urn = '__constructor__' |
| return external.ExternalTransform( |
| "beam:transforms:python:fully_qualified_named", |
| external.ImplicitSchemaPayloadBuilder({ |
| 'constructor': urn, |
| 'kwargs': args, |
| }).payload(), |
| self._service) |
| |
| def _affinity(self, other: "Provider"): |
| if isinstance(other, InlineProvider): |
| return 50 |
| else: |
| return super()._affinity(other) |
| |
| |
| # This is needed because type inference can't handle *args, **kwargs forwarding. |
| # TODO(BEAM-24755): Add support for type inference of through kwargs calls. |
| def fix_pycallable(): |
| from apache_beam.transforms.ptransform import label_from_callable |
| |
| def default_label(self): |
| src = self._source.strip() |
| last_line = src.split('\n')[-1] |
| if last_line[0] != ' ' and len(last_line) < 72: |
| return last_line |
| return label_from_callable(self._callable) |
| |
| def _argspec_fn(self): |
| return self._callable |
| |
| python_callable.PythonCallableWithSource.default_label = default_label |
| python_callable.PythonCallableWithSource._argspec_fn = property(_argspec_fn) |
| |
| original_infer_return_type = trivial_inference.infer_return_type |
| |
| def infer_return_type(fn, *args, **kwargs): |
| if isinstance(fn, python_callable.PythonCallableWithSource): |
| fn = fn._callable |
| return original_infer_return_type(fn, *args, **kwargs) |
| |
| trivial_inference.infer_return_type = infer_return_type |
| |
| original_fn_takes_side_inputs = ( |
| apache_beam.transforms.util.fn_takes_side_inputs) |
| |
| def fn_takes_side_inputs(fn): |
| if isinstance(fn, python_callable.PythonCallableWithSource): |
| fn = fn._callable |
| return original_fn_takes_side_inputs(fn) |
| |
| apache_beam.transforms.util.fn_takes_side_inputs = fn_takes_side_inputs |
| |
| |
| class InlineProvider(Provider): |
| def __init__(self, transform_factories, no_input_transforms=()): |
| self._transform_factories = transform_factories |
| self._no_input_transforms = set(no_input_transforms) |
| |
| def available(self): |
| return True |
| |
| def cache_artifacts(self): |
| pass |
| |
| def provided_transforms(self): |
| return self._transform_factories.keys() |
| |
| def config_schema(self, typ): |
| factory = self._transform_factories[typ] |
| if isinstance(factory, type) and issubclass(factory, beam.PTransform): |
| # https://bugs.python.org/issue40897 |
| params = dict(inspect.signature(factory.__init__).parameters) |
| if 'self' in params: |
| del params['self'] |
| else: |
| params = inspect.signature(factory).parameters |
| |
| def type_of(p): |
| t = p.annotation |
| if t == p.empty: |
| return Any |
| else: |
| return t |
| |
| docs = { |
| param.arg_name: param.description |
| for param in self.get_docs(typ).params |
| } |
| |
| names_and_types = [ |
| (name, typing_to_runner_api(type_of(p))) for name, p in params.items() |
| ] |
| return schema_pb2.Schema( |
| fields=[ |
| schema_pb2.Field(name=name, type=type, description=docs.get(name)) |
| for (name, type) in names_and_types |
| ]) |
| |
| def description(self, typ): |
| def empty_if_none(s): |
| return s or '' |
| |
| docs = self.get_docs(typ) |
| return ( |
| empty_if_none(docs.short_description) + |
| ('\n\n' if docs.blank_after_short_description else '\n') + |
| empty_if_none(docs.long_description)).strip() or None |
| |
| def get_docs(self, typ): |
| docstring = self._transform_factories[typ].__doc__ or '' |
| # These "extra" docstring parameters are not relevant for YAML and mess |
| # up the parsing. |
| docstring = re.sub( |
| r'Pandas Parameters\s+-----.*', '', docstring, flags=re.S) |
| return docstring_parser.parse( |
| docstring, docstring_parser.DocstringStyle.GOOGLE) |
| |
| def create_transform(self, type, args, yaml_create_transform): |
| return self._transform_factories[type](**args) |
| |
| def to_json(self): |
| return {'type': "InlineProvider"} |
| |
| def requires_inputs(self, typ, args): |
| if typ in self._no_input_transforms: |
| return False |
| elif hasattr(self._transform_factories[typ], '_yaml_requires_inputs'): |
| return self._transform_factories[typ]._yaml_requires_inputs |
| else: |
| return super().requires_inputs(typ, args) |
| |
| |
| class MetaInlineProvider(InlineProvider): |
| def create_transform(self, type, args, yaml_create_transform): |
| return self._transform_factories[type](yaml_create_transform, **args) |
| |
| |
| class SqlBackedProvider(Provider): |
| def __init__( |
| self, |
| transforms: Mapping[str, Callable[..., beam.PTransform]], |
| sql_provider: Optional[Provider] = None): |
| self._transforms = transforms |
| if sql_provider is None: |
| sql_provider = beam_jar( |
| urns={'Sql': 'beam:external:java:sql:v1'}, |
| gradle_target='sdks:java:extensions:sql:expansion-service:shadowJar') |
| self._sql_provider = sql_provider |
| |
| def sql_provider(self): |
| return self._sql_provider |
| |
| def provided_transforms(self): |
| return self._transforms.keys() |
| |
| def available(self): |
| return self.sql_provider().available() |
| |
| def cache_artifacts(self): |
| return self.sql_provider().cache_artifacts() |
| |
| def underlying_provider(self): |
| return self.sql_provider() |
| |
| def to_json(self): |
| return {'type': "SqlBackedProvider"} |
| |
| def create_transform( |
| self, typ: str, args: Mapping[str, Any], |
| yaml_create_transform: Any) -> beam.PTransform: |
| return self._transforms[typ]( |
| lambda query: self.sql_provider().create_transform( |
| 'Sql', {'query': query}, yaml_create_transform), |
| **args) |
| |
| |
| PRIMITIVE_NAMES_TO_ATOMIC_TYPE = { |
| py_type.__name__: schema_type |
| for (py_type, schema_type) in schemas.PRIMITIVE_TO_ATOMIC_TYPE.items() |
| if py_type.__module__ != 'typing' |
| } |
| |
| |
| def element_to_rows(e): |
| if isinstance(e, dict): |
| return dicts_to_rows(e) |
| else: |
| return beam.Row(element=dicts_to_rows(e)) |
| |
| |
| def dicts_to_rows(o): |
| if isinstance(o, dict): |
| return beam.Row(**{k: dicts_to_rows(v) for k, v in o.items()}) |
| elif isinstance(o, list): |
| return [dicts_to_rows(e) for e in o] |
| else: |
| return o |
| |
| |
| class YamlProviders: |
| class AssertEqual(beam.PTransform): |
| def __init__(self, elements): |
| self._elements = elements |
| |
| def expand(self, pcoll): |
| return assert_that( |
| pcoll | beam.Map(lambda row: beam.Row(**row._asdict())), |
| equal_to(dicts_to_rows(self._elements))) |
| |
| @staticmethod |
| def create(elements: Iterable[Any], reshuffle: Optional[bool] = True): |
| """Creates a collection containing a specified set of elements. |
| |
| This transform always produces schema'd data. For example:: |
| |
| type: Create |
| config: |
| elements: [1, 2, 3] |
| |
| will result in an output with three elements with a schema of |
| Row(element=int) whereas YAML/JSON-style mappings will be interpreted |
| directly as Beam rows, e.g.:: |
| |
| type: Create |
| config: |
| elements: |
| - {first: 0, second: {str: "foo", values: [1, 2, 3]}} |
| - {first: 1, second: {str: "bar", values: [4, 5, 6]}} |
| |
| will result in a schema of the form (int, Row(string, List[int])). |
| |
| This can also be expressed as YAML:: |
| |
| type: Create |
| config: |
| elements: |
| - first: 0 |
| second: |
| str: "foo" |
| values: [1, 2, 3] |
| - first: 1 |
| second: |
| str: "bar" |
| values: [4, 5, 6] |
| |
| Args: |
| elements: The set of elements that should belong to the PCollection. |
| YAML/JSON-style mappings will be interpreted as Beam rows. |
| Primitives will be mapped to rows with a single "element" field. |
| reshuffle: (optional) Whether to introduce a reshuffle (to possibly |
| redistribute the work) if there is more than one element in the |
| collection. Defaults to True. |
| """ |
| return beam.Create([element_to_rows(e) for e in elements], |
| reshuffle=reshuffle is not False) |
| |
| # Or should this be posargs, args? |
| # pylint: disable=dangerous-default-value |
| @staticmethod |
| def fully_qualified_named_transform( |
| constructor: str, |
| args: Optional[Iterable[Any]] = (), |
| kwargs: Optional[Mapping[str, Any]] = {}): |
| """A Python PTransform identified by fully qualified name. |
| |
| This allows one to import, construct, and apply any Beam Python transform. |
| This can be useful for using transforms that have not yet been exposed |
| via a YAML interface. Note, however, that conversion may be required if this |
| transform does not accept or produce Beam Rows. |
| |
| For example:: |
| |
| type: PyTransform |
| config: |
| constructor: apache_beam.pkg.mod.SomeClass |
| args: [1, 'foo'] |
| kwargs: |
| baz: 3 |
| |
| can be used to access the transform |
| `apache_beam.pkg.mod.SomeClass(1, 'foo', baz=3)`. |
| |
| See also the documentation on |
| [Inlining |
| Python](https://beam.apache.org/documentation/sdks/yaml-inline-python/). |
| |
| Args: |
| constructor: Fully qualified name of a callable used to construct the |
| transform. Often this is a class such as |
| `apache_beam.pkg.mod.SomeClass` but it can also be a function or |
| any other callable that returns a PTransform. |
| args: A list of parameters to pass to the callable as positional |
| arguments. |
| kwargs: A list of parameters to pass to the callable as keyword |
| arguments. |
| """ |
| with FullyQualifiedNamedTransform.with_filter('*'): |
| return constructor >> FullyQualifiedNamedTransform( |
| constructor, args, kwargs) |
| |
| # This intermediate is needed because there is no way to specify a tuple of |
| # exactly zero or one PCollection in yaml (as they would be interpreted as |
| # PBegin and the PCollection itself respectively). |
| class Flatten(beam.PTransform): |
| """Flattens multiple PCollections into a single PCollection. |
| |
| The elements of the resulting PCollection will be the (disjoint) union of |
| all the elements of all the inputs. |
| |
| Note that in YAML transforms can always take a list of inputs which will |
| be implicitly flattened. |
| """ |
| def __init__(self): |
| # Suppress the "label" argument from the superclass for better docs. |
| # pylint: disable=useless-parent-delegation |
| super().__init__() |
| |
| def expand(self, pcolls): |
| if isinstance(pcolls, beam.PCollection): |
| pipeline_arg = {} |
| pcolls = (pcolls, ) |
| elif isinstance(pcolls, dict): |
| pipeline_arg = {} |
| pcolls = tuple(pcolls.values()) |
| else: |
| pipeline_arg = {'pipeline': pcolls.pipeline} |
| pcolls = () |
| return pcolls | beam.Flatten(**pipeline_arg) |
| |
| class WindowInto(beam.PTransform): |
| # pylint: disable=line-too-long |
| |
| """A window transform assigning windows to each element of a PCollection. |
| |
| The assigned windows will affect all downstream aggregating operations, |
| which will aggregate by window as well as by key. |
| |
| See [the Beam documentation on windowing](https://beam.apache.org/documentation/programming-guide/#windowing) |
| for more details. |
| |
| Sizes, offsets, periods and gaps (where applicable) must be defined using |
| a time unit suffix 'ms', 's', 'm', 'h' or 'd' for milliseconds, seconds, |
| minutes, hours or days, respectively. If a time unit is not specified, it |
| will default to 's'. |
| |
| For example:: |
| |
| windowing: |
| type: fixed |
| size: 30s |
| |
| Note that any Yaml transform can have a |
| [windowing parameter](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/README.md#windowing), |
| which is applied to its inputs (if any) or outputs (if there are no inputs) |
| which means that explicit WindowInto operations are not typically needed. |
| |
| Args: |
| windowing: the type and parameters of the windowing to perform |
| """ |
| def __init__(self, windowing): |
| self._window_transform = self._parse_window_spec(windowing) |
| |
| def expand(self, pcoll): |
| return pcoll | self._window_transform |
| |
| @staticmethod |
| def _parse_duration(value, name): |
| time_units = { |
| 'ms': 0.001, 's': 1, 'm': 60, 'h': 60 * 60, 'd': 60 * 60 * 12 |
| } |
| value, suffix = re.match(r'^(.*?)([^\d]*)$', str(value)).groups() |
| # Default to seconds if time unit suffix is not defined |
| if not suffix: |
| suffix = 's' |
| if not value: |
| raise ValueError( |
| f"Invalid windowing {name} value " |
| f"'{suffix if not value else value}'. " |
| f"Must provide numeric value.") |
| if suffix not in time_units: |
| raise ValueError(( |
| "Invalid windowing {} time unit '{}'. " + |
| "Valid time units are {}.").format( |
| name, |
| suffix, |
| ', '.join("'{}'".format(k) for k in time_units.keys()))) |
| return float(value) * time_units[suffix] |
| |
| @staticmethod |
| def _parse_window_spec(spec): |
| spec = dict(spec) |
| window_type = spec.pop('type') |
| # TODO: These are in seconds, perhaps parse duration strings meaningfully? |
| if window_type == 'global': |
| window_fn = window.GlobalWindows() |
| elif window_type == 'fixed': |
| window_fn = window.FixedWindows( |
| YamlProviders.WindowInto._parse_duration(spec.pop('size'), 'size'), |
| YamlProviders.WindowInto._parse_duration( |
| spec.pop('offset', 0), 'offset')) |
| elif window_type == 'sliding': |
| window_fn = window.SlidingWindows( |
| YamlProviders.WindowInto._parse_duration(spec.pop('size'), 'size'), |
| YamlProviders.WindowInto._parse_duration( |
| spec.pop('period'), 'period'), |
| YamlProviders.WindowInto._parse_duration( |
| spec.pop('offset', 0), 'offset')) |
| elif window_type == 'sessions': |
| window_fn = window.Sessions( |
| YamlProviders.WindowInto._parse_duration(spec.pop('gap'), 'gap')) |
| else: |
| raise ValueError(f'Unknown window type {window_type}') |
| if spec: |
| raise ValueError(f'Unknown parameters {spec.keys()}') |
| # TODO: Triggering, etc. |
| return beam.WindowInto(window_fn) |
| |
| @staticmethod |
| def log_for_testing( |
| level: Optional[str] = 'INFO', prefix: Optional[str] = ''): |
| """Logs each element of its input PCollection. |
| |
| The output of this transform is a copy of its input for ease of use in |
| chain-style pipelines. |
| |
| Args: |
| level: one of ERROR, INFO, or DEBUG, mapped to a corresponding |
| language-specific logging level |
| prefix: an optional identifier that will get prepended to the element |
| being logged |
| """ |
| # Keeping this simple to be language agnostic. |
| # The intent is not to develop a logging library (and users can always do) |
| # their own mappings to get fancier output. |
| log_levels = { |
| 'ERROR': logging.error, |
| 'INFO': logging.info, |
| 'DEBUG': logging.debug, |
| } |
| if level not in log_levels: |
| raise ValueError( |
| f'Unknown log level {level} not in {list(log_levels.keys())}') |
| logger = log_levels[level] |
| |
| def to_loggable_json_recursive(o): |
| if isinstance(o, (str, bytes)): |
| return o |
| elif callable(getattr(o, '_asdict', None)): |
| return to_loggable_json_recursive(o._asdict()) |
| elif isinstance(o, Mapping) and callable(getattr(o, 'items', None)): |
| return {str(k): to_loggable_json_recursive(v) for k, v in o.items()} |
| elif isinstance(o, Iterable): |
| return [to_loggable_json_recursive(x) for x in o] |
| else: |
| return o |
| |
| def log_and_return(x): |
| logger(prefix + json.dumps(to_loggable_json_recursive(x))) |
| return x |
| |
| return "LogForTesting" >> beam.Map(log_and_return) |
| |
| @staticmethod |
| def create_builtin_provider(): |
| return InlineProvider({ |
| 'AssertEqual': YamlProviders.AssertEqual, |
| 'Create': YamlProviders.create, |
| 'LogForTesting': YamlProviders.log_for_testing, |
| 'PyTransform': YamlProviders.fully_qualified_named_transform, |
| 'Flatten': YamlProviders.Flatten, |
| 'WindowInto': YamlProviders.WindowInto, |
| }, |
| no_input_transforms=('Create', )) |
| |
| |
| class TranslatingProvider(Provider): |
| def __init__( |
| self, |
| transforms: Mapping[str, Callable[..., beam.PTransform]], |
| underlying_provider: Provider): |
| self._transforms = transforms |
| self._underlying_provider = underlying_provider |
| |
| def provided_transforms(self): |
| return self._transforms.keys() |
| |
| def available(self): |
| return self._underlying_provider.available() |
| |
| def cache_artifacts(self): |
| return self._underlying_provider.cache_artifacts() |
| |
| def underlying_provider(self): |
| return self._underlying_provider |
| |
| def to_json(self): |
| return {'type': "TranslatingProvider"} |
| |
| def create_transform( |
| self, typ: str, config: Mapping[str, Any], |
| yaml_create_transform: Any) -> beam.PTransform: |
| return self._transforms[typ](self._underlying_provider, **config) |
| |
| |
| def create_java_builtin_provider(): |
| """Exposes built-in transforms from Java as well as Python to maximize |
| opportunities for fusion. |
| |
| This class holds those transforms that require pre-processing of the configs. |
| For those Java transforms that can consume the user-provided configs directly |
| (or only need a simple renaming of parameters) a direct or renaming provider |
| is the simpler choice. |
| """ |
| |
| # An alternative could be examining the capabilities of various environments |
| # during (or as a pre-processing phase before) fusion to align environments |
| # where possible. This would also require extra care in skipping these |
| # common transforms when doing the provider affinity analysis. |
| |
| def java_window_into(java_provider, windowing): |
| """Use the `windowing` WindowingStrategy and invokes the Java class. |
| |
| Though it would not be that difficult to implement this in Java as well, |
| we prefer to implement it exactly once for consistency (especially as |
| it evolves). |
| """ |
| windowing_strategy = YamlProviders.WindowInto._parse_window_spec( |
| windowing).get_windowing(None) |
| # No context needs to be preserved for the basic WindowFns. |
| empty_context = pipeline_context.PipelineContext() |
| return java_provider.create_transform( |
| 'WindowIntoStrategy', |
| { |
| 'serialized_windowing_strategy': windowing_strategy.to_runner_api( |
| empty_context).SerializeToString() |
| }, |
| None) |
| |
| return TranslatingProvider( |
| transforms={'WindowInto': java_window_into}, |
| underlying_provider=beam_jar( |
| urns={ |
| 'WindowIntoStrategy': ( |
| 'beam:schematransform:' |
| 'org.apache.beam:yaml:window_into_strategy:v1') |
| }, |
| gradle_target= |
| 'sdks:java:extensions:schemaio-expansion-service:shadowJar')) |
| |
| |
| class PypiExpansionService: |
| """Expands transforms by fully qualified name in a virtual environment |
| with the given dependencies. |
| """ |
| VENV_CACHE = os.path.expanduser("~/.apache_beam/cache/venvs") |
| |
| def __init__(self, packages, base_python=sys.executable): |
| self._packages = packages |
| self._base_python = base_python |
| |
| @classmethod |
| def _key(cls, base_python, packages): |
| return json.dumps({ |
| 'binary': base_python, 'packages': sorted(packages) |
| }, |
| sort_keys=True) |
| |
| @classmethod |
| def _path(cls, base_python, packages): |
| return os.path.join( |
| cls.VENV_CACHE, |
| hashlib.sha256(cls._key(base_python, |
| packages).encode('utf-8')).hexdigest()) |
| |
| @classmethod |
| def _create_venv_from_scratch(cls, base_python, packages): |
| venv = cls._path(base_python, packages) |
| if not os.path.exists(venv): |
| try: |
| subprocess.run([base_python, '-m', 'venv', venv], check=True) |
| venv_python = os.path.join(venv, 'bin', 'python') |
| venv_pip = os.path.join(venv, 'bin', 'pip') |
| subprocess.run([venv_python, '-m', 'ensurepip'], check=True) |
| subprocess.run([venv_pip, 'install'] + packages, check=True) |
| with open(venv + '-requirements.txt', 'w') as fout: |
| fout.write('\n'.join(packages)) |
| except: # pylint: disable=bare-except |
| if os.path.exists(venv): |
| shutil.rmtree(venv, ignore_errors=True) |
| raise |
| return venv |
| |
| @classmethod |
| def _create_venv_from_clone(cls, base_python, packages): |
| venv = cls._path(base_python, packages) |
| if not os.path.exists(venv): |
| try: |
| clonable_venv = cls._create_venv_to_clone(base_python) |
| clonable_python = os.path.join(clonable_venv, 'bin', 'python') |
| subprocess.run( |
| [clonable_python, '-m', 'clonevirtualenv', clonable_venv, venv], |
| check=True) |
| venv_pip = os.path.join(venv, 'bin', 'pip') |
| subprocess.run([venv_pip, 'install'] + packages, check=True) |
| with open(venv + '-requirements.txt', 'w') as fout: |
| fout.write('\n'.join(packages)) |
| except: # pylint: disable=bare-except |
| if os.path.exists(venv): |
| shutil.rmtree(venv, ignore_errors=True) |
| raise |
| return venv |
| |
| @classmethod |
| def _create_venv_to_clone(cls, base_python): |
| if '.dev' in beam_version: |
| base_venv = os.path.dirname(os.path.dirname(base_python)) |
| print('Cloning dev environment from', base_venv) |
| return cls._create_venv_from_scratch( |
| base_python, |
| [ |
| 'apache_beam[dataframe,gcp,test,yaml]==' + beam_version, |
| 'virtualenv-clone' |
| ]) |
| |
| def _venv(self): |
| return self._create_venv_from_clone(self._base_python, self._packages) |
| |
| def __enter__(self): |
| venv = self._venv() |
| self._service_provider = subprocess_server.SubprocessServer( |
| external.ExpansionAndArtifactRetrievalStub, |
| [ |
| os.path.join(venv, 'bin', 'python'), |
| '-m', |
| 'apache_beam.runners.portability.expansion_service_main', |
| '--port', |
| '{{PORT}}', |
| '--fully_qualified_name_glob=*', |
| '--pickle_library=cloudpickle', |
| '--requirements_file=' + os.path.join(venv + '-requirements.txt') |
| ]) |
| self._service = self._service_provider.__enter__() |
| return self._service |
| |
| def __exit__(self, *args): |
| self._service_provider.__exit__(*args) |
| self._service = None |
| |
| |
| @ExternalProvider.register_provider_type('renaming') |
| class RenamingProvider(Provider): |
| def __init__(self, transforms, mappings, underlying_provider, defaults=None): |
| if isinstance(underlying_provider, dict): |
| underlying_provider = ExternalProvider.provider_from_spec( |
| underlying_provider) |
| self._transforms = transforms |
| self._underlying_provider = underlying_provider |
| for transform in transforms.keys(): |
| if transform not in mappings: |
| raise ValueError(f'Missing transform {transform} in mappings.') |
| self._mappings = self.expand_mappings(mappings) |
| self._defaults = defaults or {} |
| |
| @staticmethod |
| def expand_mappings(mappings): |
| if not isinstance(mappings, dict): |
| raise ValueError( |
| "RenamingProvider mappings must be dict of transform " |
| "mappings.") |
| for key, value in mappings.items(): |
| if isinstance(value, str): |
| if value not in mappings.keys(): |
| raise ValueError( |
| "RenamingProvider transform mappings must be dict or " |
| "specify transform that has mappings within same " |
| "provider.") |
| mappings[key] = mappings[value] |
| return mappings |
| |
| def available(self) -> bool: |
| return self._underlying_provider.available() |
| |
| def provided_transforms(self) -> Iterable[str]: |
| return self._transforms.keys() |
| |
| def config_schema(self, type): |
| underlying_schema = self._underlying_provider.config_schema( |
| self._transforms[type]) |
| if underlying_schema is None: |
| return None |
| defaults = self._defaults.get(type, {}) |
| underlying_schema_fields = {f.name: f for f in underlying_schema.fields} |
| missing = set(self._mappings[type].values()) - set( |
| underlying_schema_fields.keys()) |
| if missing: |
| if 'kwargs' in underlying_schema_fields.keys(): |
| # These are likely passed by keyword argument dict rather than missing. |
| for field_name in missing: |
| underlying_schema_fields[field_name] = schema_pb2.Field( |
| name=field_name, type=typing_to_runner_api(Any)) |
| else: |
| raise ValueError( |
| f"Mapping destinations {missing} for {type} are not in the " |
| f"underlying config schema {list(underlying_schema_fields.keys())}") |
| |
| def with_name( |
| original: schema_pb2.Field, new_name: str) -> schema_pb2.Field: |
| result = schema_pb2.Field() |
| result.CopyFrom(original) |
| result.name = new_name |
| return result |
| |
| return schema_pb2.Schema( |
| fields=[ |
| with_name(underlying_schema_fields[dest], src) |
| for (src, dest) in self._mappings[type].items() |
| if dest not in defaults |
| ]) |
| |
| def description(self, typ): |
| return self._underlying_provider.description(self._transforms[typ]) |
| |
| def requires_inputs(self, typ, args): |
| return self._underlying_provider.requires_inputs( |
| self._transforms[typ], args) |
| |
| def create_transform( |
| self, |
| typ: str, |
| args: Mapping[str, Any], |
| yaml_create_transform: Callable[ |
| [Mapping[str, Any], Iterable[beam.PCollection]], beam.PTransform] |
| ) -> beam.PTransform: |
| """Creates a PTransform instance for the given transform type and arguments. |
| """ |
| mappings = self._mappings[typ] |
| remapped_args = { |
| mappings.get(key, key): value |
| for key, value in args.items() |
| } |
| for key, value in self._defaults.get(typ, {}).items(): |
| if key not in remapped_args: |
| remapped_args[key] = value |
| return self._underlying_provider.create_transform( |
| self._transforms[typ], remapped_args, yaml_create_transform) |
| |
| def _affinity(self, other): |
| raise NotImplementedError( |
| 'Should not be calling _affinity directly on this provider.') |
| |
| def underlying_provider(self): |
| return self._underlying_provider.underlying_provider() |
| |
| def cache_artifacts(self): |
| self._underlying_provider.cache_artifacts() |
| |
| |
| def parse_providers(provider_specs): |
| providers = collections.defaultdict(list) |
| for provider_spec in provider_specs: |
| provider = ExternalProvider.provider_from_spec(provider_spec) |
| for transform_type in provider.provided_transforms(): |
| providers[transform_type].append(provider) |
| # TODO: Do this better. |
| provider.to_json = lambda result=provider_spec: result |
| return providers |
| |
| |
| def merge_providers(*provider_sets): |
| result = collections.defaultdict(list) |
| for provider_set in provider_sets: |
| if isinstance(provider_set, Provider): |
| provider = provider_set |
| provider_set = { |
| transform_type: [provider] |
| for transform_type in provider.provided_transforms() |
| } |
| elif isinstance(provider_set, list): |
| provider_set = merge_providers(*provider_set) |
| for transform_type, providers in provider_set.items(): |
| result[transform_type].extend(providers) |
| return result |
| |
| |
| def standard_providers(): |
| from apache_beam.yaml.yaml_combine import create_combine_providers |
| from apache_beam.yaml.yaml_mapping import create_mapping_providers |
| from apache_beam.yaml.yaml_join import create_join_providers |
| from apache_beam.yaml.yaml_io import io_providers |
| with open(os.path.join(os.path.dirname(__file__), |
| 'standard_providers.yaml')) as fin: |
| standard_providers = yaml.load(fin, Loader=SafeLoader) |
| |
| return merge_providers( |
| YamlProviders.create_builtin_provider(), |
| create_java_builtin_provider(), |
| create_mapping_providers(), |
| create_combine_providers(), |
| create_join_providers(), |
| io_providers(), |
| parse_providers(standard_providers)) |
