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"""Tools used tool work with Schema types in the context of BigQuery.
Classes, constants and functions in this file are experimental and have no
backwards compatibility guarantees.
NOTHING IN THIS FILE HAS BACKWARDS COMPATIBILITY GUARANTEES.
"""
from typing import Optional
from typing import Sequence
import numpy as np
import apache_beam as beam
from apache_beam.io.gcp.internal.clients import bigquery
from apache_beam.portability.api import schema_pb2
# BigQuery types as listed in
# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
# with aliases (RECORD, BOOLEAN, FLOAT, INTEGER) as defined in
# https://developers.google.com/resources/api-libraries/documentation/bigquery/v2/java/latest/com/google/api/services/bigquery/model/TableFieldSchema.html#setType-java.lang.String-
BIG_QUERY_TO_PYTHON_TYPES = {
"STRING": str,
"INTEGER": np.int64,
"FLOAT64": np.float64,
"BOOLEAN": bool,
"BYTES": bytes,
#TODO(https://github.com/apache/beam/issues/20810):
# Finish mappings for all BQ types
}
[docs]def generate_user_type_from_bq_schema(the_table_schema):
#type: (bigquery.TableSchema) -> type
"""Convert a schema of type TableSchema into a pcollection element.
Args:
the_table_schema: A BQ schema of type TableSchema
Returns:
type: type that can be used to work with pCollections.
"""
the_schema = beam.io.gcp.bigquery_tools.get_dict_table_schema(
the_table_schema)
if the_schema == {}:
raise ValueError("Encountered an empty schema")
dict_of_tuples = []
for i in range(len(the_schema['fields'])):
if the_schema['fields'][i]['type'] in BIG_QUERY_TO_PYTHON_TYPES:
typ = bq_field_to_type(
the_schema['fields'][i]['type'], the_schema['fields'][i]['mode'])
else:
raise ValueError(
f"Encountered "
f"an unsupported type: {the_schema['fields'][i]['type']!r}")
# TODO svetaksundhar@: Map remaining BQ types
dict_of_tuples.append((the_schema['fields'][i]['name'], typ))
sample_schema = beam.typehints.schemas.named_fields_to_schema(dict_of_tuples)
usertype = beam.typehints.schemas.named_tuple_from_schema(sample_schema)
return usertype
[docs]def bq_field_to_type(field, mode):
if mode == 'NULLABLE':
return Optional[BIG_QUERY_TO_PYTHON_TYPES[field]]
elif mode == 'REPEATED':
return Sequence[BIG_QUERY_TO_PYTHON_TYPES[field]]
elif mode is None or mode == '':
return BIG_QUERY_TO_PYTHON_TYPES[field]
else:
raise ValueError(f"Encountered an unsupported mode: {mode!r}")
[docs]def convert_to_usertype(table_schema):
usertype = beam.io.gcp.bigquery_schema_tools. \
generate_user_type_from_bq_schema(table_schema)
return beam.ParDo(
beam.io.gcp.bigquery_schema_tools.BeamSchemaConversionDoFn(usertype))
[docs]class BeamSchemaConversionDoFn(beam.DoFn):
# Converting a dictionary of tuples to a usertype.
def __init__(self, pcoll_val_ctor):
self._pcoll_val_ctor = pcoll_val_ctor
[docs] def process(self, dict_of_tuples):
yield self._pcoll_val_ctor(**dict_of_tuples)
[docs] def infer_output_type(self, input_type):
return self._pcoll_val_ctor
@classmethod
def _from_serialized_schema(cls, schema_str):
return cls(
beam.typehints.schemas.named_tuple_from_schema(
beam.utils.proto_utils.parse_Bytes(schema_str, schema_pb2.Schema)))
def __reduce__(self):
# when pickling, use bytes representation of the schema.
return (
self._from_serialized_schema,
(
beam.typehints.schemas.named_tuple_to_schema(
self._pcoll_val_ctor).SerializeToString(), ))