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
<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):
super(SqlTransform, self).__init__(
self.URN,
NamedTupleBasedPayloadBuilder(
SqlTransformSchema(query=query, dialect=dialect)),
BeamJarExpansionService(
':sdks:java:extensions:sql:expansion-service:shadowJar'))