apache_beam.transforms.external_transform_provider module

class apache_beam.transforms.external_transform_provider.ExternalTransform(expansion_service=None, **kwargs)[source]

Bases: apache_beam.transforms.ptransform.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.

default_expansion_service = None
description = ''
identifier = ''
configuration_schema = {}
class apache_beam.transforms.external_transform_provider.ExternalTransformProvider(expansion_services, urn_pattern='^beam:schematransform:org.apache.beam:([\w-]+):(\w+)$')[source]

Bases: object

Dynamically discovers Schema-aware external transforms from a given list of expansion services and provides them as ready PTransforms.

A ExternalTransform subclass is generated for each external transform, and is named based on what can be inferred from the URN (see the urn_pattern parameter).

These classes are generated when 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 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 get(), get_urn(), or by directly accessing it as an attribute of 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(
    | 'Some processing' >> beam.Map(...))

Experimental; no backwards compatibility guarantees.

get_available() → List[Tuple[str, str]][source]

Get a list of available ExternalTransform names and identifiers

get_all() → Dict[str, apache_beam.transforms.external_transform_provider.ExternalTransform][source]

Get all ExternalTransform

get(name) → apache_beam.transforms.external_transform_provider.ExternalTransform[source]

Get an ExternalTransform by its inferred class name

get_urn(identifier) → apache_beam.transforms.external_transform_provider.ExternalTransform[source]

Get an ExternalTransform by its SchemaTransform identifier