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https://issues.apache.org/jira/browse/BEAM-3342?focusedWorklogId=241437&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-241437
]
ASF GitHub Bot logged work on BEAM-3342:
----------------------------------------
Author: ASF GitHub Bot
Created on: 14/May/19 01:34
Start Date: 14/May/19 01:34
Worklog Time Spent: 10m
Work Description: mf2199 commented on pull request #8457: [BEAM-3342]
Initial version of Google Cloud Bigtable IO connector
URL: https://github.com/apache/beam/pull/8457#discussion_r283596297
##########
File path: sdks/python/apache_beam/io/gcp/bigtableio_it_test.py
##########
@@ -0,0 +1,178 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+""" Integration test for GCP Bigtable testing."""
+from __future__ import absolute_import
+
+import argparse
+import datetime
+import logging
+import random
+import string
+import time
+import unittest
+
+import apache_beam as beam
+from apache_beam.io import Read
+from apache_beam.metrics.metric import MetricsFilter
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.runners.runner import PipelineState
+from apache_beam.testing.util import assert_that, equal_to
+from apache_beam.transforms.combiners import Count
+
+try:
+ from google.cloud.bigtable import enums, row, column_family, Client
+ from gcp.bigtableio import BigtableSource, WriteToBigTable
+except ImportError:
+ Client = None
+
+
+class GenerateTestRows(beam.PTransform):
+ """ A PTransform to generate dummy rows to write to a Bigtable Table.
+
+ A PTransform that generates a list of `DirectRow` and writes it to a
Bigtable Table.
+ """
+ def __init__(self):
+ super(self.__class__, self).__init__()
+ self.beam_options = {'project_id': PROJECT_ID,
+ 'instance_id': INSTANCE_ID,
+ 'table_id': TABLE_ID}
+
+ def _generate(self):
+ for i in range(ROW_COUNT):
+ key = "key_%s" % ('{0:012}'.format(i))
+ test_row = row.DirectRow(row_key=key)
+ value = ''.join(random.choice(LETTERS_AND_DIGITS) for _ in
range(CELL_SIZE))
+ for j in range(COLUMN_COUNT):
+ test_row.set_cell(column_family_id=COLUMN_FAMILY_ID,
+ column=('field%s' % j).encode('utf-8'),
+ value=value,
+ timestamp=datetime.datetime.now())
+ yield test_row
+
+ def expand(self, pvalue):
+ return (pvalue
+ | beam.Create(self._generate())
+ | WriteToBigTable(project_id=self.beam_options['project_id'],
+ instance_id=self.beam_options['instance_id'],
+ table_id=self.beam_options['table_id']))
+
[email protected](Client is None, 'GCP Bigtable dependencies are not installed')
+class BigtableIOTest(unittest.TestCase):
+ """ Bigtable IO Connector Test
+
+ This tests the connector both ways, first writing rows to a new table, then
reading them and comparing the counters
+ """
+ def setUp(self):
+ self.result = None
+ self.table = Client(project=PROJECT_ID, admin=True)\
+ .instance(instance_id=INSTANCE_ID)\
+ .table(TABLE_ID)
+
+ if not self.table.exists():
+ column_families = {COLUMN_FAMILY_ID: column_family.MaxVersionsGCRule(2)}
+ self.table.create(column_families=column_families)
+ logging.info('Table {} has been created!'.format(TABLE_ID))
+
+ def test_bigtable_io(self):
+ print('Project ID: {}'.format(PROJECT_ID))
+ print('Instance ID:{}'.format(INSTANCE_ID))
+ print('Table ID: {}'.format(TABLE_ID))
+
+ pipeline_options = PipelineOptions(PIPELINE_PARAMETERS)
+ p = beam.Pipeline(options=pipeline_options)
+ _ = (p | 'Write Test Rows' >> GenerateTestRows())
+
+ self.result = p.run()
+ self.result.wait_until_finish()
+
+ assert self.result.state == PipelineState.DONE
+
+ if not hasattr(self.result, 'has_job') or self.result.has_job:
+ query_result =
self.result.metrics().query(MetricsFilter().with_name('Written Row'))
+ if query_result['counters']:
+ read_counter = query_result['counters'][0]
+ logging.info('Number of Rows written: %d', read_counter.committed)
+ assert read_counter.committed == ROW_COUNT
+
+ p = beam.Pipeline(options=pipeline_options)
+ count = (p
+ | 'Read from Bigtable' >>
Read(BigtableSource(project_id=PROJECT_ID,
+
instance_id=INSTANCE_ID,
+ table_id=TABLE_ID))
+ | 'Count Rows' >> Count.Globally())
+ self.result = p.run()
+ self.result.wait_until_finish()
+ assert_that(count, equal_to([ROW_COUNT]))
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--project', type=str)
+ parser.add_argument('--instance', type=str)
+ parser.add_argument('--table', type=str, default='test-table')
+ parser.add_argument('--region', type=str, default='us-central1')
+ parser.add_argument('--staging_location', type=str)
+ parser.add_argument('--temp_location', type=str)
+ parser.add_argument('--setup_file', type=str)
+ parser.add_argument('--extra_package', type=str)
+ parser.add_argument('--num_workers', type=int, default=300)
+ parser.add_argument('--autoscaling_algorithm', type=str, default='NONE')
+ parser.add_argument('--experiments', type=str, default='beam_fn_api')
+ parser.add_argument('--runner', type=str, default='dataflow')
+ parser.add_argument('--disk_size_gb', type=int, default=50)
Review comment:
According to Google online docs,
"The disk size, in gigabytes, to use on each remote Compute Engine worker
instance. The minimum disk size is 30 GB, to account for the worker boot image
and local logs. If your pipeline shuffles data, you should allocate more than
the minimum. | Set to 0 to use the default size defined in your GCP project."
Since 30GB is the minimum, no other default size is provided elsewhere in
the project, and the connector connector does use shuffling, a default valaue
of 50GB seems to be like a reasonable choice. The user can always override ti
by specifying this valaue explicitly in the command line arguments.
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Issue Time Tracking
-------------------
Worklog Id: (was: 241437)
Time Spent: 22h 20m (was: 22h 10m)
> Create a Cloud Bigtable Python connector
> ----------------------------------------
>
> 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: 22h 20m
> Remaining Estimate: 0h
>
> I would like to create a Cloud Bigtable python connector.
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