sql.py

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"""Package for SqlTransform and related classes."""

# pytype: skip-file

importtyping

fromapache_beam.transforms.externalimportBeamJarExpansionService
fromapache_beam.transforms.externalimportExternalTransform
fromapache_beam.transforms.externalimportNamedTupleBasedPayloadBuilder

__all__= ['SqlTransform']

SqlTransformSchema=typing.NamedTuple(
'SqlTransformSchema',
 [('query', str), ('dialect', typing.Optional[str]),
 ('ddl', typing.Optional[str])])


classSqlTransform(ExternalTransform):
"""A transform that can translate a SQL query into PTransforms.

 Input PCollections must have a schema. Currently, there are two ways to define
 a schema for a PCollection:

 1) Register a `typing.NamedTuple` type to use RowCoder, and specify it as the
 output type. For example::

 Purchase = typing.NamedTuple('Purchase',
 [('item_name', unicode), ('price', float)])
 coders.registry.register_coder(Purchase, coders.RowCoder)
 with Pipeline() as p:
 purchases = (p | beam.io...
 | beam.Map(..).with_output_types(Purchase))

 2) Produce `beam.Row` instances. Note this option will fail if Beam is unable
 to infer data types for any of the fields. For example::

 with Pipeline() as p:
 purchases = (p | beam.io...
 | beam.Map(lambda x: beam.Row(item_name=unicode(..),
 price=float(..))))

 Similarly, the output of SqlTransform is a PCollection with a schema.
 The columns produced by the query can be accessed as attributes. For example::

 purchases | SqlTransform(\"\"\"
 SELECT item_name, COUNT(*) AS `count`
 FROM PCOLLECTION GROUP BY item_name\"\"\")
 | beam.Map(lambda row: "We've sold %d %ss!" % (row.count,
 row.item_name))

 Additional examples can be found in
 `apache_beam.examples.wordcount_xlang_sql`, `apache_beam.examples.sql_taxi`,
 and `apache_beam.transforms.sql_test`.

 For more details about Beam SQL in general, see the `Java transform
 <https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/extensions/sql/SqlTransform.html>`_,
 and the `documentation
 <https://beam.apache.org/documentation/dsls/sql/overview/>`_.
 """
URN='beam:external:java:sql:v1'

def__init__(self, query, dialect=None, ddl=None, expansion_service=None):
"""
 Creates a SqlTransform which will be expanded to Java's SqlTransform.
 (See class docs).
 :param query: The SQL query.
 :param dialect: (optional) The dialect, e.g. use 'zetasql' for ZetaSQL.
 :param ddl: (optional) The DDL statement.
 :param expansion_service: (optional) The URL of the expansion service to use
 """
expansion_service=expansion_serviceorBeamJarExpansionService(
':sdks:java:extensions:sql:expansion-service:shadowJar')
super().__init__(
self.URN,
NamedTupleBasedPayloadBuilder(
SqlTransformSchema(query=query, dialect=dialect, ddl=ddl)),
expansion_service=expansion_service)