Source code for apache_beam.transforms.external_transform_provider
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import logging
import re
from collections import namedtuple
from typing import Dict
from typing import List
from typing import Tuple
from apache_beam.transforms import PTransform
from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external import SchemaAwareExternalTransform
from apache_beam.transforms.external import SchemaTransformsConfig
from apache_beam.typehints.schemas import named_tuple_to_schema
from apache_beam.typehints.schemas import typing_from_runner_api
__all__ = ['ExternalTransform', 'ExternalTransformProvider']
def snake_case_to_upper_camel_case(string):
"""Convert snake_case to UpperCamelCase"""
components = string.split('_')
output = ''.join(n.capitalize() for n in components)
return output
def snake_case_to_lower_camel_case(string):
"""Convert snake_case to lowerCamelCase"""
if len(string) <= 1:
return string.lower()
upper = snake_case_to_upper_camel_case(string)
return upper[0].lower() + upper[1:]
def camel_case_to_snake_case(string):
"""Convert camelCase to snake_case"""
arr = []
word = []
for i, n in enumerate(string):
# If seeing an upper letter after a lower letter, we just witnessed a word
# If seeing an upper letter and the next letter is lower, we may have just
# witnessed an all caps word
if n.isupper() and ((i > 0 and string[i - 1].islower()) or
(i + 1 < len(string) and string[i + 1].islower())):
arr.append(''.join(word))
word = [n.lower()]
else:
word.append(n.lower())
arr.append(''.join(word))
return '_'.join(arr).strip('_')
# Information regarding a Wrapper parameter.
ParamInfo = namedtuple('ParamInfo', ['type', 'description', 'original_name'])
def get_config_with_descriptions(
schematransform: SchemaTransformsConfig) -> Dict[str, ParamInfo]:
# Prepare a configuration schema that includes types and descriptions
schema = named_tuple_to_schema(schematransform.configuration_schema)
descriptions = schematransform.configuration_schema._field_descriptions
fields_with_descriptions = {}
for field in schema.fields:
fields_with_descriptions[camel_case_to_snake_case(field.name)] = ParamInfo(
typing_from_runner_api(field.type),
descriptions[field.name],
field.name)
return fields_with_descriptions
[docs]class ExternalTransform(PTransform):
"""Template for a wrapper class of an external SchemaTransform
This is a superclass for dynamically generated SchemaTransform wrappers and
is not meant to be manually instantiated.
Experimental; no backwards compatibility guarantees."""
# These attributes need to be set when
# creating an ExternalTransform type
default_expansion_service = None
description: str = ""
identifier: str = ""
configuration_schema: Dict[str, ParamInfo] = {}
def __init__(self, expansion_service=None, **kwargs):
self._kwargs = kwargs
self._expansion_service = \
expansion_service or self.default_expansion_service
[docs] def expand(self, input):
camel_case_kwargs = {
snake_case_to_lower_camel_case(k): v
for k, v in self._kwargs.items()
}
external_schematransform = SchemaAwareExternalTransform(
identifier=self.identifier,
expansion_service=self._expansion_service,
rearrange_based_on_discovery=True,
**camel_case_kwargs)
return input | external_schematransform
STANDARD_URN_PATTERN = r"^beam:schematransform:org.apache.beam:([\w-]+):(\w+)$"
def infer_name_from_identifier(identifier: str, pattern: str):
"""Infer a class name from an identifier, adhering to the input pattern"""
match = re.match(pattern, identifier)
if not match:
return None
groups = match.groups()
components = [snake_case_to_upper_camel_case(n) for n in groups]
# Special handling for standard SchemaTransform identifiers:
# We don't include the version number if it's the first version
if (pattern == STANDARD_URN_PATTERN and components[1].lower() == 'v1'):
return components[0]
else:
return ''.join(components)
[docs]class ExternalTransformProvider:
"""Dynamically discovers Schema-aware external transforms from a given list
of expansion services and provides them as ready PTransforms.
A :class:`ExternalTransform` subclass is generated for each external
transform, and is named based on what can be inferred from the URN
(see :param urn_pattern).
These classes are generated when :class:`ExternalTransformProvider` is
initialized. We need to give it one or more expansion service addresses that
are already up and running:
>>> provider = ExternalTransformProvider(["localhost:12345",
... "localhost:12121"])
We can also give it the gradle target of a standard Beam expansion service:
>>> provider = ExternalTransform(BeamJarExpansionService(
... "sdks:java:io:google-cloud-platform:expansion-service:shadowJar"))
Let's take a look at the output of :func:`get_available()` to know the
available transforms in the expansion service(s) we provided:
>>> provider.get_available()
[('JdbcWrite', 'beam:schematransform:org.apache.beam:jdbc_write:v1'),
('BigtableRead', 'beam:schematransform:org.apache.beam:bigtable_read:v1'),
...]
Then retrieve a transform by :func:`get()`, :func:`get_urn()`, or by directly
accessing it as an attribute of :class:`ExternalTransformProvider`.
All of the following commands do the same thing:
>>> provider.get('BigqueryStorageRead')
>>> provider.get_urn(
... 'beam:schematransform:org.apache.beam:bigquery_storage_read:v1')
>>> provider.BigqueryStorageRead
To know more about the usage of a given transform, take a look at the
`description` attribute. This returns some documentation IF the underlying
SchemaTransform provides any.
>>> provider.BigqueryStorageRead.description
Similarly, the `configuration_schema` attribute returns information about the
parameters, including their names, types, and any documentation that the
underlying SchemaTransform may provide:
>>> provider.BigqueryStorageRead.configuration_schema
{'query': ParamInfo(type=typing.Optional[str], description='The SQL query to
be executed to read from the BigQuery table.', original_name='query'),
'row_restriction': ParamInfo(type=typing.Optional[str]...}
The retrieved external transform can be used as a normal PTransform like so::
with Pipeline() as p:
_ = (p
| 'Read from BigQuery` >> provider.BigqueryStorageRead(
query=query,
row_restriction=restriction)
| 'Some processing' >> beam.Map(...))
Experimental; no backwards compatibility guarantees.
"""
def __init__(self, expansion_services, urn_pattern=STANDARD_URN_PATTERN):
f"""Initialize an ExternalTransformProvider
:param expansion_services:
A list of expansion services to discover transforms from.
Supported forms:
* a string representing the expansion service address
* a :attr:`BeamJarExpansionService` pointing to a gradle target
:param urn_pattern:
The regular expression used to match valid transforms. In addition to
validating, the captured groups are used to infer a name for each class.
By default, the following pattern is used: [{STANDARD_URN_PATTERN}]
"""
self._urn_pattern = urn_pattern
self._transforms: Dict[str, type(ExternalTransform)] = {}
self._name_to_urn: Dict[str, str] = {}
if isinstance(expansion_services, set):
expansion_services = list(expansion_services)
if not isinstance(expansion_services, list):
expansion_services = [expansion_services]
self.expansion_services = expansion_services
self._create_wrappers()
def _create_wrappers(self):
# multiple services can overlap and include the same URNs. If this happens,
# we prioritize by the order of services in the list
identifiers = set()
for service in self.expansion_services:
target = service
if isinstance(service, BeamJarExpansionService):
target = service.gradle_target
try:
schematransform_configs = SchemaAwareExternalTransform.discover(service)
except Exception as e:
logging.exception(
"Encountered an error while discovering expansion service %s:\n%s",
target,
e)
continue
skipped_urns = []
for config in schematransform_configs:
identifier = config.identifier
if identifier not in identifiers:
identifiers.add(identifier)
name = infer_name_from_identifier(identifier, self._urn_pattern)
if name is None:
skipped_urns.append(identifier)
continue
self._transforms[identifier] = type(
name, (ExternalTransform, ),
dict(
identifier=identifier,
default_expansion_service=service,
schematransform=config,
description=config.description,
configuration_schema=get_config_with_descriptions(config)))
self._name_to_urn[name] = identifier
if skipped_urns:
logging.info(
"Skipped URN(s) in %s that don't follow the pattern [%s]: %s",
target,
self._urn_pattern,
skipped_urns)
for transform in self._transforms.values():
setattr(self, transform.__name__, transform)
[docs] def get_available(self) -> List[Tuple[str, str]]:
"""Get a list of available ExternalTransform names and identifiers"""
return list(self._name_to_urn.items())
[docs] def get(self, name) -> ExternalTransform:
"""Get an ExternalTransform by its inferred class name"""
return self._transforms[self._name_to_urn[name]]
[docs] def get_urn(self, identifier) -> ExternalTransform:
"""Get an ExternalTransform by its SchemaTransform identifier"""
return self._transforms[identifier]