apache_beam.yaml.yaml_provider module
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.
- class apache_beam.yaml.yaml_provider.Provider[source]
Bases:
object
Maps transform types names and args to concrete PTransform instances.
- provided_transforms() Iterable[str] [source]
Returns a list of transform type names this provider can handle.
- requires_inputs(typ: str, args: Mapping[str, Any]) bool [source]
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.
- create_transform(typ: str, args: Mapping[str, Any], yaml_create_transform: Callable[[Mapping[str, Any], Iterable[PCollection]], PTransform]) PTransform [source]
Creates a PTransform instance for the given transform type and arguments.
- class apache_beam.yaml.yaml_provider.ExternalProvider(urns, service)[source]
Bases:
Provider
A Provider implemented via the cross language transform service.
- apache_beam.yaml.yaml_provider.maven_jar(urns, *, artifact_id, group_id, version, repository='https://repo.maven.apache.org/maven2', classifier=None, appendix=None)[source]
- apache_beam.yaml.yaml_provider.beam_jar(urns, *, gradle_target, appendix=None, version='2.61.0', artifact_id=None)[source]
- class apache_beam.yaml.yaml_provider.RemoteProvider(urns, address: str)[source]
Bases:
ExternalProvider
- class apache_beam.yaml.yaml_provider.ExternalJavaProvider(urns, jar_provider)[source]
Bases:
ExternalProvider
- class apache_beam.yaml.yaml_provider.ExternalPythonProvider(urns, packages)[source]
Bases:
ExternalProvider
- class apache_beam.yaml.yaml_provider.YamlProvider(transforms: Mapping[str, Mapping[str, Any]])[source]
Bases:
Provider
- create_transform(type: str, args: Mapping[str, Any], yaml_create_transform: Callable[[Mapping[str, Any], Iterable[PCollection]], PTransform]) PTransform [source]
- class apache_beam.yaml.yaml_provider.InlineProvider(transform_factories, no_input_transforms=())[source]
Bases:
Provider
- class apache_beam.yaml.yaml_provider.MetaInlineProvider(transform_factories, no_input_transforms=())[source]
Bases:
InlineProvider
- class apache_beam.yaml.yaml_provider.SqlBackedProvider(transforms: Mapping[str, Callable[[...], PTransform]], sql_provider: Provider | None = None)[source]
Bases:
Provider
- class apache_beam.yaml.yaml_provider.YamlProviders[source]
Bases:
object
- class AssertEqual(elements: Iterable[Any])[source]
Bases:
PTransform
Asserts that the input contains exactly the elements provided.
This is primarily used for testing; it will cause the entire pipeline to fail if the input to this transform is not exactly the set of elements given in the config parameter.
As with Create, YAML/JSON-style mappings are interpreted as Beam rows, e.g.:
type: AssertEqual input: SomeTransform config: elements: - {a: 0, b: "foo"} - {a: 1, b: "bar"}
would ensure that SomeTransform produced exactly two elements with values (a=0, b=”foo”) and (a=1, b=”bar”) respectively.
- Parameters:
elements – The set of elements that should belong to the PCollection. YAML/JSON-style mappings will be interpreted as Beam rows.
- static create(elements: Iterable[Any], reshuffle: bool | None = True)[source]
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]
- Parameters:
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.
- static fully_qualified_named_transform(constructor: str, args: Iterable[Any] | None = (), kwargs: Mapping[str, Any] | None = {})[source]
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/).
- Parameters:
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.
- class Flatten[source]
Bases:
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.
- class WindowInto(windowing)[source]
Bases:
PTransform
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.
- Parameters:
windowing – the type and parameters of the windowing to perform
- static log_for_testing(level: str | None = 'INFO', prefix: str | None = '')[source]
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.
- Parameters:
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
- class apache_beam.yaml.yaml_provider.TranslatingProvider(transforms: Mapping[str, Callable[[...], PTransform]], underlying_provider: Provider)[source]
Bases:
Provider
- apache_beam.yaml.yaml_provider.create_java_builtin_provider()[source]
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.
- class apache_beam.yaml.yaml_provider.PypiExpansionService(packages, base_python='/home/runner/work/beam/beam/beam/sdks/python/target/.tox/docs/bin/python')[source]
Bases:
object
Expands transforms by fully qualified name in a virtual environment with the given dependencies.
- VENV_CACHE = '/home/runner/.apache_beam/cache/venvs'
- class apache_beam.yaml.yaml_provider.RenamingProvider(transforms, mappings, underlying_provider, defaults=None)[source]
Bases:
Provider
- create_transform(typ: str, args: Mapping[str, Any], yaml_create_transform: Callable[[Mapping[str, Any], Iterable[PCollection]], PTransform]) PTransform [source]
Creates a PTransform instance for the given transform type and arguments.
- apache_beam.yaml.yaml_provider.flatten_included_provider_specs(provider_specs: Iterable[Mapping]) Iterator[Mapping] [source]
- apache_beam.yaml.yaml_provider.parse_providers(provider_specs: Iterable[Mapping]) Iterable[Provider] [source]