satybald commented on a change in pull request #15185:
URL: https://github.com/apache/beam/pull/15185#discussion_r680257018
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File path: sdks/python/apache_beam/io/gcp/bigquery.py
##########
@@ -883,6 +892,222 @@ def _export_files(self, bq):
return table.schema, metadata_list
+class _CustomBigQueryStorageSourceBase(BoundedSource):
+ """A base class for BoundedSource implementations which read from BigQuery
+ using the BigQuery Storage API.
+
+ Args:
+ table (str, TableReference): The ID of the table. The ID must contain only
+ letters ``a-z``, ``A-Z``, numbers ``0-9``, or underscores ``_`` If
+ **dataset** argument is :data:`None` then the table argument must
+ contain the entire table reference specified as:
+ ``'PROJECT:DATASET.TABLE'`` or must specify a TableReference.
+ dataset (str): The ID of the dataset containing this table or
+ :data:`None` if the table argument specifies a TableReference.
+ project (str): The ID of the project containing this table or
+ :data:`None` if the table argument specifies a TableReference.
+ selected_fields (List[str]): Names of the fields in the table that should
be
+ read. If empty, all fields will be read. If the specified field is a
+ nested field, all the sub-fields in the field will be selected. The
output
+ field order is unrelated to the order of fields in selected_fields.
+ row_restriction (str): SQL text filtering statement, similar to a WHERE
+ clause in a query. Aggregates are not supported.Restricted to a maximum
+ length for 1 MB.
+ """
+
+ # The maximum number of streams which will be requested when creating a read
+ # session, regardless of the desired bundle size.
+ MAX_SPLIT_COUNT = 10000
+ # The minimum number of streams which will be requested when creating a read
+ # session, regardless of the desired bundle size. Note that the server may
+ # still choose to return fewer than ten streams based on the layout of the
+ # table.
+ MIN_SPLIT_COUNT = 10
+
+ def __init__(
+ self,
+ table: Union[str, TableReference],
+ dataset: str = None,
+ project: str = None,
+ selected_fields: List[str] = None,
+ row_restriction: str = None,
+ pipeline_options: GoogleCloudOptions = None):
+
+ self.table_reference = bigquery_tools.parse_table_reference(
+ table, dataset, project)
+ self.project = self.table_reference.projectId
+ self.dataset = self.table_reference.datasetId
+ self.table = self.table_reference.tableId
+ self.selected_fields = selected_fields
+ self.row_restriction = row_restriction
+ self.pipeline_options = pipeline_options
+ self.split_result = None
+
+ def _get_parent_project(self):
+ """Returns the project that will be billed."""
+ project = self.pipeline_options.view_as(GoogleCloudOptions).project
+ if isinstance(project, vp.ValueProvider):
+ project = project.get()
+ if not project:
+ project = self.project
+ return project
+
+ def _get_table_size(self, table, dataset, project):
+ if project is None:
+ project = self._get_parent_project()
+
+ bq = bigquery_tools.BigQueryWrapper()
+ table = bq.get_table(project, dataset, table)
+ return table.numBytes
+
+ def display_data(self):
+ return {
+ 'project': str(self.project),
+ 'dataset': str(self.dataset),
+ 'table': str(self.table),
+ 'selected_fields': str(self.selected_fields),
+ 'row_restriction': str(self.row_restriction)
+ }
+
+ def estimate_size(self):
+ # The size of stream source cannot be estimate due to server-side liquid
+ # sharding
+ return None
+
+ def split(self, desired_bundle_size, start_position=None,
stop_position=None):
+ requested_session = bq_storage.types.ReadSession()
+ requested_session.table = 'projects/{}/datasets/{}/tables/{}'.format(
+ self.project, self.dataset, self.table)
+ requested_session.data_format = bq_storage.types.DataFormat.AVRO
+ if self.selected_fields is not None:
+ requested_session.read_options.selected_fields = self.selected_fields
+ if self.row_restriction is not None:
+ requested_session.read_options.row_restriction = self.row_restriction
+
+ storage_client = bq_storage.BigQueryReadClient()
+ stream_count = 0
+ if (desired_bundle_size > 0):
+ stream_count = min(
Review comment:
thank you @vachan-shetty for outsiders like me, can you provide more
context why this min/max logic is needed?
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