apache_beam.io.jdbc module¶

PTransforms for supporting Jdbc in Python pipelines.

These transforms are currently supported by Beam portable Flink, Spark, and Dataflow v2 runners.

Setup

Transforms provided in this module are cross-language transforms implemented in the Beam Java SDK. During the pipeline construction, Python SDK will connect to a Java expansion service to expand these transforms. To facilitate this, a small amount of setup is needed before using these transforms in a Beam Python pipeline.

There are several ways to setup cross-language Jdbc transforms.

Option 1: use the default expansion service
Option 2: specify a custom expansion service

See below for details regarding each of these options.

Option 1: Use the default expansion service

This is the recommended and easiest setup option for using Python Jdbc transforms. This option is only available for Beam 2.24.0 and later.

This option requires following pre-requisites before running the Beam pipeline.

Install Java runtime in the computer from where the pipeline is constructed and make sure that ‘java’ command is available.

In this option, Python SDK will either download (for released Beam version) or build (when running from a Beam Git clone) a expansion service jar and use that to expand transforms. Currently Jdbc transforms use the ‘beam-sdks-java-io-expansion-service’ jar for this purpose.

Option 2: specify a custom expansion service

In this option, you startup your own expansion service and provide that as a parameter when using the transforms provided in this module.

This option requires following pre-requisites before running the Beam pipeline.

Startup your own expansion service.
Update your pipeline to provide the expansion service address when initiating Jdbc transforms provided in this module.

Flink Users can use the built-in Expansion Service of the Flink Runner’s Job Server. If you start Flink’s Job Server, the expansion service will be started on port 8097. For a different address, please set the expansion_service parameter.

More information

For more information regarding cross-language transforms see: - https://beam.apache.org/roadmap/portability/

For more information specific to Flink runner see: - https://beam.apache.org/documentation/runners/flink/

class apache_beam.io.jdbc.WriteToJdbc(table_name, driver_class_name, jdbc_url, username, password, statement=None, connection_properties=None, connection_init_sqls=None, expansion_service=None)[source]¶

Bases: apache_beam.transforms.external.ExternalTransform

A PTransform which writes Rows to the specified database via JDBC.

This transform receives Rows defined as NamedTuple type and registered in the coders registry, e.g.:

ExampleRow = typing.NamedTuple('ExampleRow',
                               [('id', int), ('name', unicode)])
coders.registry.register_coder(ExampleRow, coders.RowCoder)

with TestPipeline() as p:
  _ = (
      p
      | beam.Create([ExampleRow(1, 'abc')])
          .with_output_types(ExampleRow)
      | 'Write to jdbc' >> WriteToJdbc(
          table_name='jdbc_external_test_write'
          driver_class_name='org.postgresql.Driver',
          jdbc_url='jdbc:postgresql://localhost:5432/example',
          username='postgres',
          password='postgres',
      ))

table_name is a required paramater, and by default, the write_statement is generated from it.

The generated write_statement can be overridden by passing in a write_statment.

Experimental; no backwards compatibility guarantees.

Initializes a write operation to Jdbc.

Parameters:

driver_class_name – name of the jdbc driver class
jdbc_url – full jdbc url to the database.
username – database username
password – database password
statement – sql statement to be executed
connection_properties – properties of the jdbc connection passed as string with format [propertyName=property;]*
connection_init_sqls – required only for MySql and MariaDB. passed as list of strings
expansion_service – The address (host:port) of the ExpansionService.

URN = 'beam:external:java:schemaio:jdbc:write:v1'¶

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]¶

default_label()¶

default_type_hints()¶

display_data()¶

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

Return type: Dict[str, Any]

expand(pvalueish)¶

classmethod from_runner_api(proto, context)¶

classmethod get_local_namespace()¶

get_type_hints()¶

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs)¶

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)¶

label¶

classmethod outer_namespace(namespace)¶

pipeline = None¶

classmethod register_urn(urn, parameter_type, constructor=None)¶

runner_api_requires_keyed_input()¶

side_inputs = ()¶

to_runner_api(context, has_parts=False, **extra_kwargs)¶

to_runner_api_parameter(unused_context)¶

to_runner_api_pickled(unused_context)¶

to_runner_api_transform(context, full_label)¶

type_check_inputs(pvalueish)¶

type_check_inputs_or_outputs(pvalueish, input_or_output)¶

type_check_outputs(pvalueish)¶

with_input_types(input_type_hint)¶

Annotates the input type of a PTransform with a type-hint.

Parameters:	input_type_hint (type) – An instance of an allowed built-in type, a custom class, or an instance of a `TypeConstraint`.
Raises:	`TypeError` – If input_type_hint is not a valid type-hint. See `apache_beam.typehints.typehints.validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

with_output_types(type_hint)¶

Annotates the output type of a PTransform with a type-hint.

Parameters:	type_hint (type) – An instance of an allowed built-in type, a custom class, or a `TypeConstraint`.
Raises:	`TypeError` – If type_hint is not a valid type-hint. See `validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

class apache_beam.io.jdbc.ReadFromJdbc(table_name, driver_class_name, jdbc_url, username, password, query=None, output_parallelization=None, fetch_size=None, connection_properties=None, connection_init_sqls=None, expansion_service=None)[source]¶