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https://issues.apache.org/jira/browse/BEAM-3342?focusedWorklogId=317708&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-317708
]
ASF GitHub Bot logged work on BEAM-3342:
----------------------------------------
Author: ASF GitHub Bot
Created on: 24/Sep/19 17:46
Start Date: 24/Sep/19 17:46
Worklog Time Spent: 10m
Work Description: drubinstein commented on pull request #8457:
[BEAM-3342] Create a Cloud Bigtable IO connector for Python
URL: https://github.com/apache/beam/pull/8457#discussion_r327749283
##########
File path: sdks/python/apache_beam/io/gcp/bigtableio.py
##########
@@ -122,22 +126,145 @@ class WriteToBigTable(beam.PTransform):
A PTransform that write a list of `DirectRow` into the Bigtable Table
"""
- def __init__(self, project_id=None, instance_id=None,
- table_id=None):
+ def __init__(self, project_id=None, instance_id=None, table_id=None):
""" The PTransform to access the Bigtable Write connector
Args:
project_id(str): GCP Project of to write the Rows
instance_id(str): GCP Instance to write the Rows
table_id(str): GCP Table to write the `DirectRows`
"""
super(WriteToBigTable, self).__init__()
- self.beam_options = {'project_id': project_id,
- 'instance_id': instance_id,
- 'table_id': table_id}
+ self._beam_options = {'project_id': project_id,
+ 'instance_id': instance_id,
+ 'table_id': table_id}
def expand(self, pvalue):
- beam_options = self.beam_options
+ beam_options = self._beam_options
return (pvalue
| beam.ParDo(_BigTableWriteFn(beam_options['project_id'],
beam_options['instance_id'],
beam_options['table_id'])))
+
+
+class _BigtableReadFn(beam.DoFn):
+ """ Creates the connector that can read rows for Beam pipeline
+
+ Args:
+ project_id(str): GCP Project ID
+ instance_id(str): GCP Instance ID
+ table_id(str): GCP Table ID
+
+ """
+
+ def __init__(self, project_id, instance_id, table_id, filter_=b''):
+ """ Constructor of the Read connector of Bigtable
+
+ Args:
+ project_id: [str] GCP Project of to write the Rows
+ instance_id: [str] GCP Instance to write the Rows
+ table_id: [str] GCP Table to write the `DirectRows`
+ filter_: [RowFilter] Filter to apply to columns in a row.
+ """
+ super(self.__class__, self).__init__()
+ self._initialize({'project_id': project_id,
+ 'instance_id': instance_id,
+ 'table_id': table_id,
+ 'filter_': filter_})
+
+ def __getstate__(self):
+ return self._beam_options
+
+ def __setstate__(self, options):
+ self._initialize(options)
+
+ def _initialize(self, options):
+ self._beam_options = options
+ self.table = None
+ self.sample_row_keys = None
+ self.row_count = Metrics.counter(self.__class__.__name__, 'Rows read')
+
+ def start_bundle(self):
+ if self.table is None:
+ self.table = Client(project=self._beam_options['project_id'])\
+ .instance(self._beam_options['instance_id'])\
+ .table(self._beam_options['table_id'])
+
+ def process(self, element, **kwargs):
+ for row in self.table.read_rows(start_key=element.start_position,
+ end_key=element.end_position,
+ filter_=self._beam_options['filter_']):
+ self.row_count.inc()
+ yield row
+
+ def display_data(self):
+ return {'projectId': DisplayDataItem(self._beam_options['project_id'],
+ label='Bigtable Project Id'),
+ 'instanceId': DisplayDataItem(self._beam_options['instance_id'],
+ label='Bigtable Instance Id'),
+ 'tableId': DisplayDataItem(self._beam_options['table_id'],
+ label='Bigtable Table Id'),
+ 'filter_': DisplayDataItem(str(self._beam_options['filter_']),
+ label='Bigtable Filter')
+ }
+
+
+class ReadFromBigTable(beam.PTransform):
+ def __init__(self, project_id, instance_id, table_id, filter_=b''):
+ """ The PTransform to access the Bigtable Read connector
+
+ Args:
+ project_id: [str] GCP Project of to read the Rows
+ instance_id): [str] GCP Instance to read the Rows
+ table_id): [str] GCP Table to read the Rows
+ filter_: [RowFilter] Filter to apply to columns in a row.
+ """
+ super(self.__class__, self).__init__()
+ self._beam_options = {'project_id': project_id,
+ 'instance_id': instance_id,
+ 'table_id': table_id,
+ 'filter_': filter_}
+
+ def __getstate__(self):
+ return self._beam_options
+
+ def __setstate__(self, options):
+ self._beam_options = options
+
+ def expand(self, pbegin):
+ from apache_beam.transforms import util
+
+ beam_options = self._beam_options
+ table = Client(project=beam_options['project_id'])\
+ .instance(beam_options['instance_id'])\
+ .table(beam_options['table_id'])
+ sample_row_keys = list(table.sample_row_keys())
+
+ if len(sample_row_keys) > 1 and sample_row_keys[0].row_key != b'':
+ SampleRowKey = namedtuple("SampleRowKey", "row_key offset_bytes")
+ first_key = SampleRowKey(b'', 0)
+ sample_row_keys.insert(0, first_key)
+ sample_row_keys = list(sample_row_keys)
+
+ def split_source(unused_impulse):
+ bundles = []
+ for i in range(1, len(sample_row_keys)):
Review comment:
If `sample_row_keys()` on line 240 returns a list of length one, then line
250 (in `split_source`) will be skipped (as it is trying to iterate over
`range(1,1)` and shuffle will be called as `shuffle([])` (which my python shell
returns as `None`).
Maybe line 242 should read as `if len(sample_row_keys) >= 1 and
sample_row_keys[0].row_key != b''`
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Issue Time Tracking
-------------------
Worklog Id: (was: 317708)
Time Spent: 40h 20m (was: 40h 10m)
> Create a Cloud Bigtable IO connector for Python
> -----------------------------------------------
>
> Key: BEAM-3342
> URL: https://issues.apache.org/jira/browse/BEAM-3342
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Reporter: Solomon Duskis
> Assignee: Solomon Duskis
> Priority: Major
> Time Spent: 40h 20m
> Remaining Estimate: 0h
>
> I would like to create a Cloud Bigtable python connector.
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