chamikaramj commented on a change in pull request #8457: [BEAM-3342] Create a
Cloud Bigtable IO connector for Python
URL: https://github.com/apache/beam/pull/8457#discussion_r396691673
##########
File path: sdks/python/apache_beam/io/gcp/bigtableio.py
##########
@@ -141,3 +144,123 @@ def expand(self, pvalue):
| beam.ParDo(_BigTableWriteFn(beam_options['project_id'],
beam_options['instance_id'],
beam_options['table_id'])))
+
+
+class _BigtableReadFn(beam.DoFn):
+ def __init__(self, project_id, instance_id, table_id, filter_=None):
+ """ A DoFn to parallelize reading from a Bigtable table
+
+ :type project_id: str
+ :param project_id: The ID of the project used for Bigtable access
+
+ :type instance_id: str
+ :param instance_id: The ID of the instance that owns the table.
+
+ :type table_id: str
+ :param table_id: The ID of the table.
+
+ :type filter_: :class:`.RowFilter`
+ :param filter_: (Optional) The filter to apply to the contents of the
+ specified row(s). If unset, reads every column in
+ each row.
+ """
+ super(self.__class__, self).__init__()
+ self._initialize({'project_id': project_id,
+ 'instance_id': instance_id,
+ 'table_id': table_id,
+ 'filter_': filter_})
+
+ def _initialize(self, options):
+ """ The defaults initializer, to assist with pickling
+
+ :return: None
+ """
+ self._options = options
+ self._table = None
+ self._counter = Metrics.counter(self.__class__, 'Rows Read')
+
+ def __getstate__(self):
+ return self._options
+
+ def __setstate__(self, options):
+ self._initialize(options)
+
+ def start_bundle(self):
+ # from google.cloud.bigtable import Client
+ if self._table is None:
+ # noinspection PyAttributeOutsideInit
+ self._table = Client(project=self._options['project_id'])\
+ .instance(self._options['instance_id'])\
+ .table(self._options['table_id'])
+
+ def process(self, source_bundle):
+ _start_key = source_bundle.start_position
+ _end_key = source_bundle.stop_position
+ for row in self._table.read_rows(_start_key, _end_key):
+ self._counter.inc()
+ yield row
+
+ def display_data(self):
+ return {'projectId': DisplayDataItem(self._options['project_id'],
+ label='Bigtable Project Id'),
+ 'instanceId': DisplayDataItem(self._options['instance_id'],
+ label='Bigtable Instance Id'),
+ 'tableId': DisplayDataItem(self._options['table_id'],
+ label='Bigtable Table Id')}
+
+
+class ReadFromBigtable(beam.PTransform):
+ def __init__(self, project_id, instance_id, table_id, filter_=None):
+ """ A PTransform wrapper for parallel reading rows from s Bigtable table.
+
+ :type project_id: str
+ :param project_id: The ID of the project used for Bigtable access
+
+ :type instance_id: str
+ :param instance_id: The ID of the instance that owns the table.
+
+ :type table_id: str
+ :param table_id: The ID of the table.
+
+ :type filter_: :class:`.RowFilter`
+ :param filter_: (Optional) The filter to apply to the contents of the
+ specified row(s). If unset, reads every column in
+ each row. If noe is provided, all rows are read by default.
+ """
+ super(self.__class__, self).__init__()
+ self._options = {'project_id': project_id,
+ 'instance_id': instance_id,
+ 'table_id': table_id,
+ 'filter_': filter_}
+
+ def __getstate__(self):
+ return self._options
+
+ def __setstate__(self, options):
+ self._options = options
+
+ def expand(self, pbegin):
+ table = Client(project=self._options['project_id'], admin=True) \
+ .instance(instance_id=self._options['instance_id']) \
+ .table(table_id=self._options['table_id'])
+
+ keys = list(table.sample_row_keys())
+
+ SampleRowKey = namedtuple("SampleRowKey", "row_key offset_bytes")
+ keys.insert(0, SampleRowKey(b'', 0))
+
+ def chunks():
+ for i in range(1, len(keys)):
Review comment:
Do we expect to always get at least two keys here ? If so add an assertion
before this statement so that we don't fall through trivially.
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