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"""Managed Transforms.

This module builds and instantiates turnkey transforms that can be managed by

the underlying runner. This means the runner can upgrade the transform to a

more optimal/updated version without requiring the user to do anything. It may

also replace the transform with something entirely different if it chooses to.

By default, however, the specified transform will remain unchanged.

Available transforms

====================

Please check the Managed IO configuration page:

https://beam.apache.org/documentation/io/managed-io/

Using Managed Transforms

========================

Managed turnkey transforms have a defined configuration and can be built using

an inline :class:`dict` like so::

results = p | beam.managed.Read(

beam.managed.ICEBERG,

config={"table": "foo",

"catalog_name": "bar",

"catalog_properties": {

"warehouse": "path/to/warehouse",

"catalog-impl": "org.apache.my.CatalogImpl"}})

A YAML configuration file can also be used to build a Managed transform. Say we

have the following `config.yaml` file::

topic: "foo"

bootstrap_servers: "localhost:1234"

format: "AVRO"

Simply provide the location to the file like so::

input_rows = p | beam.Create(...)

input_rows | beam.managed.Write(

beam.managed.KAFKA,

config_url="path/to/config.yaml")

**Note:** inputs and outputs need to be PCollection(s) of Beam

:py:class:`apache_beam.pvalue.Row` elements.

Runner specific features

========================

Google Cloud Dataflow supports additional management features for `managed`

including automatically upgrading transforms to the latest supported version.

For more details and examples, please see

Dataflow managed I/O https://cloud.google.com/dataflow/docs/guides/managed-io.

"""

from typing import Any

from typing import Optional

import yaml

from apache_beam.portability.common_urns import ManagedTransforms

from apache_beam.transforms.external import MANAGED_SCHEMA_TRANSFORM_IDENTIFIER

from apache_beam.transforms.external import MANAGED_TRANSFORM_URN_TO_JAR_TARGET_MAPPING

from apache_beam.transforms.external import BeamJarExpansionService

from apache_beam.transforms.external import SchemaAwareExternalTransform

from apache_beam.transforms.ptransform import PTransform

ICEBERG = "iceberg"

# TODO(https://github.com/apache/beam/issues/34212): keep ICEBERG_CDC private

# until we vet it with integration tests

_ICEBERG_CDC = "iceberg_cdc"

KAFKA = "kafka"

BIGQUERY = "bigquery"

POSTGRES = "postgres"

MYSQL = "mysql"

SQL_SERVER = "sqlserver"

__all__ = ["ICEBERG", "KAFKA", "BIGQUERY", "Read", "Write"]

class Read(PTransform):

"""Read using Managed Transforms"""

_READ_TRANSFORMS = {

ICEBERG: ManagedTransforms.Urns.ICEBERG_READ.urn,

_ICEBERG_CDC: ManagedTransforms.Urns.ICEBERG_CDC_READ.urn,

KAFKA: ManagedTransforms.Urns.KAFKA_READ.urn,

BIGQUERY: ManagedTransforms.Urns.BIGQUERY_READ.urn,

POSTGRES: ManagedTransforms.Urns.POSTGRES_READ.urn,

MYSQL: ManagedTransforms.Urns.MYSQL_READ.urn,

SQL_SERVER: ManagedTransforms.Urns.SQL_SERVER_READ.urn,

}

def __init__(

self,

source: str,

config: Optional[dict[str, Any]] = None,

config_url: Optional[str] = None,

expansion_service=None):

super().__init__()

self._source = source

identifier = self._READ_TRANSFORMS.get(source.lower())

if not identifier:

raise ValueError(

f"An unsupported source was specified: '{source}'. Please specify "

f"one of the following sources: {list(self._READ_TRANSFORMS.keys())}")

# Store parameters for deferred expansion service creation

self._identifier = identifier

self._provided_expansion_service = expansion_service

self._underlying_identifier = identifier

self._yaml_config = yaml.dump(config)

self._config_url = config_url

def expand(self, input):

# Create expansion service with access to pipeline options

expansion_service = _resolve_expansion_service(

self._source,

self._identifier,

self._provided_expansion_service,

pipeline_options=input.pipeline._options)

return input | SchemaAwareExternalTransform(

identifier=MANAGED_SCHEMA_TRANSFORM_IDENTIFIER,

expansion_service=expansion_service,

rearrange_based_on_discovery=True,

transform_identifier=self._underlying_identifier,

config=self._yaml_config,

config_url=self._config_url)

def default_label(self) -> str:

return "Managed Read(%s)" % self._source.upper()

class Write(PTransform):

"""Write using Managed Transforms"""

_WRITE_TRANSFORMS = {

ICEBERG: ManagedTransforms.Urns.ICEBERG_WRITE.urn,

KAFKA: ManagedTransforms.Urns.KAFKA_WRITE.urn,

BIGQUERY: ManagedTransforms.Urns.BIGQUERY_WRITE.urn,

POSTGRES: ManagedTransforms.Urns.POSTGRES_WRITE.urn,

MYSQL: ManagedTransforms.Urns.MYSQL_WRITE.urn,

SQL_SERVER: ManagedTransforms.Urns.SQL_SERVER_WRITE.urn

}

def __init__(

self,

sink: str,

config: Optional[dict[str, Any]] = None,

config_url: Optional[str] = None,

expansion_service=None):

super().__init__()

self._sink = sink

identifier = self._WRITE_TRANSFORMS.get(sink.lower())

if not identifier:

raise ValueError(

f"An unsupported sink was specified: '{sink}'. Please specify "

f"one of the following sinks: {list(self._WRITE_TRANSFORMS.keys())}")

# Store parameters for deferred expansion service creation

self._identifier = identifier

self._provided_expansion_service = expansion_service

self._underlying_identifier = identifier

self._yaml_config = yaml.dump(config)

self._config_url = config_url

def expand(self, input):

# Create expansion service with access to pipeline options

expansion_service = _resolve_expansion_service(

self._sink,

self._identifier,

self._provided_expansion_service,

pipeline_options=input.pipeline._options)

return input | SchemaAwareExternalTransform(

identifier=MANAGED_SCHEMA_TRANSFORM_IDENTIFIER,

expansion_service=expansion_service,

rearrange_based_on_discovery=True,

transform_identifier=self._underlying_identifier,

config=self._yaml_config,

config_url=self._config_url)

def default_label(self) -> str:

return "Managed Write(%s)" % self._sink.upper()

def _resolve_expansion_service(

transform_name: str,

identifier: str,

expansion_service,

pipeline_options=None):

if expansion_service:

return expansion_service

gradle_target = None

if identifier in MANAGED_TRANSFORM_URN_TO_JAR_TARGET_MAPPING:

gradle_target = MANAGED_TRANSFORM_URN_TO_JAR_TARGET_MAPPING.get(identifier)

if not gradle_target:

raise ValueError(

"No expansion service was specified and could not find a "

f"default expansion service for {transform_name}: '{identifier}'.")

# Extract maven_repository_url and user_agent from pipeline options if

# available

maven_repository_url = None

user_agent = None

if pipeline_options:

from apache_beam.options import pipeline_options as po

setup_options = pipeline_options.view_as(po.SetupOptions)

maven_repository_url = setup_options.maven_repository_url

user_agent = setup_options.user_agent

return BeamJarExpansionService(

gradle_target,

maven_repository_url=maven_repository_url,

user_agent=user_agent)