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"""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/
"""
# pytype: skip-file
import typing
from apache_beam.coders import RowCoder
from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external import ExternalTransform
from apache_beam.transforms.external import NamedTupleBasedPayloadBuilder
from apache_beam.typehints.schemas import typing_to_runner_api
__all__ = [
'WriteToJdbc',
'ReadFromJdbc',
]
def default_io_expansion_service():
return BeamJarExpansionService(
':sdks:java:extensions:schemaio-expansion-service:shadowJar')
JdbcConfigSchema = typing.NamedTuple(
'JdbcConfigSchema',
[('location', str), ('config', bytes)],
)
Config = typing.NamedTuple(
'Config',
[
('driver_class_name', str),
('jdbc_url', str),
('username', str),
('password', str),
('connection_properties', typing.Optional[str]),
('connection_init_sqls', typing.Optional[typing.List[str]]),
('write_statement', typing.Optional[str]),
('read_query', typing.Optional[str]),
('fetch_size', typing.Optional[int]),
('output_parallelization', typing.Optional[bool]),
],
)
[docs]class WriteToJdbc(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.
"""
URN = 'beam:external:java:schemaio:jdbc:write:v1'
def __init__(
self,
table_name,
driver_class_name,
jdbc_url,
username,
password,
statement=None,
connection_properties=None,
connection_init_sqls=None,
expansion_service=None,
):
"""
Initializes a write operation to Jdbc.
:param driver_class_name: name of the jdbc driver class
:param jdbc_url: full jdbc url to the database.
:param username: database username
:param password: database password
:param statement: sql statement to be executed
:param connection_properties: properties of the jdbc connection
passed as string with format
[propertyName=property;]*
:param connection_init_sqls: required only for MySql and MariaDB.
passed as list of strings
:param expansion_service: The address (host:port) of the ExpansionService.
"""
super(WriteToJdbc, self).__init__(
self.URN,
NamedTupleBasedPayloadBuilder(
JdbcConfigSchema(
location=table_name,
config=RowCoder(
typing_to_runner_api(Config).row_type.schema).encode(
Config(
driver_class_name=driver_class_name,
jdbc_url=jdbc_url,
username=username,
password=password,
connection_properties=connection_properties,
connection_init_sqls=connection_init_sqls,
write_statement=statement,
read_query=None,
fetch_size=None,
output_parallelization=None,
))),
),
expansion_service or default_io_expansion_service(),
)
[docs]class ReadFromJdbc(ExternalTransform):
"""A PTransform which reads Rows from the specified database via JDBC.
This transform delivers Rows defined as NamedTuple 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:
result = (
p
| 'Read from jdbc' >> ReadFromJdbc(
table_name='jdbc_external_test_read'
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 read_query is
generated from it.
The generated read_query can be overridden by passing in a read_query.
Experimental; no backwards compatibility guarantees.
"""
URN = 'beam:external:java:schemaio:jdbc:read:v1'
def __init__(
self,
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,
):
"""
Initializes a read operation from Jdbc.
:param driver_class_name: name of the jdbc driver class
:param jdbc_url: full jdbc url to the database.
:param username: database username
:param password: database password
:param query: sql query to be executed
:param output_parallelization: is output parallelization on
:param fetch_size: how many rows to fetch
:param connection_properties: properties of the jdbc connection
passed as string with format
[propertyName=property;]*
:param connection_init_sqls: required only for MySql and MariaDB.
passed as list of strings
:param expansion_service: The address (host:port) of the ExpansionService.
"""
super(ReadFromJdbc, self).__init__(
self.URN,
NamedTupleBasedPayloadBuilder(
JdbcConfigSchema(
location=table_name,
config=RowCoder(
typing_to_runner_api(Config).row_type.schema).encode(
Config(
driver_class_name=driver_class_name,
jdbc_url=jdbc_url,
username=username,
password=password,
connection_properties=connection_properties,
connection_init_sqls=connection_init_sqls,
write_statement=None,
read_query=query,
fetch_size=fetch_size,
output_parallelization=output_parallelization,
))),
),
expansion_service or default_io_expansion_service(),
)