Source code for apache_beam.transforms.sql

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

# pytype: skip-file

from __future__ import absolute_import

import typing

from past.builtins import unicode

from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external import ExternalTransform
from apache_beam.transforms.external import NamedTupleBasedPayloadBuilder

__all__ = ['SqlTransform']

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

[docs]class SqlTransform(ExternalTransform): """A transform that can translate a SQL query into PTransforms. Input PCollections must have a schema. Currently, this means the PCollection *must* have a NamedTuple output type, and that type must be registered to use RowCoder. For example:: Purchase = typing.NamedTuple('Purchase', [('item_name', unicode), ('price', float)]) coders.registry.register_coder(Purchase, coders.RowCoder) Similarly, the output of SqlTransform is a PCollection with a generated NamedTuple type, and columns can be accessed as fields. 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`, and `apache_beam.transforms.sql_test`. For more details about Beam SQL in general see the `Java transform <>`_, and the `documentation <>`_. """ URN = 'beam:external:java:sql:v1' def __init__(self, query, dialect=None): super(SqlTransform, self).__init__( self.URN, NamedTupleBasedPayloadBuilder( SqlTransformSchema(query=query, dialect=dialect)), BeamJarExpansionService( ':sdks:java:extensions:sql:expansion-service:shadowJar'))