| #!/usr/bin/env python |
| # -*- coding: utf-8 -*- |
| # |
| # 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. |
| # |
| |
| """Unit tests for BigQuery sources and sinks.""" |
| from __future__ import absolute_import |
| |
| import base64 |
| import logging |
| import random |
| import time |
| import unittest |
| from decimal import Decimal |
| |
| from future.utils import iteritems |
| from nose.plugins.attrib import attr |
| |
| import apache_beam as beam |
| from apache_beam.io.gcp.bigquery_tools import BigQueryWrapper |
| from apache_beam.io.gcp.internal.clients import bigquery |
| from apache_beam.testing.test_pipeline import TestPipeline |
| from apache_beam.testing.util import assert_that |
| from apache_beam.testing.util import equal_to |
| |
| # Protect against environments where bigquery library is not available. |
| # pylint: disable=wrong-import-order, wrong-import-position |
| try: |
| from apitools.base.py.exceptions import HttpError |
| except ImportError: |
| HttpError = None |
| # pylint: enable=wrong-import-order, wrong-import-position |
| |
| |
| _LOGGER = logging.getLogger(__name__) |
| |
| |
| class BigQueryReadIntegrationTests(unittest.TestCase): |
| BIG_QUERY_DATASET_ID = 'python_read_table_' |
| |
| def setUp(self): |
| self.test_pipeline = TestPipeline(is_integration_test=True) |
| self.runner_name = type(self.test_pipeline.runner).__name__ |
| self.project = self.test_pipeline.get_option('project') |
| |
| self.bigquery_client = BigQueryWrapper() |
| self.dataset_id = '%s%s%d' % (self.BIG_QUERY_DATASET_ID, |
| str(int(time.time())), |
| random.randint(0, 10000)) |
| self.bigquery_client.get_or_create_dataset(self.project, self.dataset_id) |
| _LOGGER.info("Created dataset %s in project %s", |
| self.dataset_id, self.project) |
| |
| def tearDown(self): |
| request = bigquery.BigqueryDatasetsDeleteRequest( |
| projectId=self.project, datasetId=self.dataset_id, |
| deleteContents=True) |
| try: |
| _LOGGER.info("Deleting dataset %s in project %s", |
| self.dataset_id, self.project) |
| self.bigquery_client.client.datasets.Delete(request) |
| except HttpError: |
| _LOGGER.debug('Failed to clean up dataset %s in project %s', |
| self.dataset_id, self.project) |
| |
| def create_table(self, tablename): |
| table_schema = bigquery.TableSchema() |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'number' |
| table_field.type = 'INTEGER' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'str' |
| table_field.type = 'STRING' |
| table_schema.fields.append(table_field) |
| table = bigquery.Table( |
| tableReference=bigquery.TableReference( |
| projectId=self.project, |
| datasetId=self.dataset_id, |
| tableId=tablename), |
| schema=table_schema) |
| request = bigquery.BigqueryTablesInsertRequest( |
| projectId=self.project, datasetId=self.dataset_id, table=table) |
| self.bigquery_client.client.tables.Insert(request) |
| table_data = [ |
| {'number': 1, 'str': 'abc'}, |
| {'number': 2, 'str': 'def'}, |
| {'number': 3, 'str': u'你好'}, |
| {'number': 4, 'str': u'привет'} |
| ] |
| self.bigquery_client.insert_rows( |
| self.project, self.dataset_id, tablename, table_data) |
| |
| def create_table_new_types(self, table_name): |
| table_schema = bigquery.TableSchema() |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'float' |
| table_field.type = 'FLOAT' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'numeric' |
| table_field.type = 'NUMERIC' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'bytes' |
| table_field.type = 'BYTES' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'date' |
| table_field.type = 'DATE' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'time' |
| table_field.type = 'TIME' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'datetime' |
| table_field.type = 'DATETIME' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'timestamp' |
| table_field.type = 'TIMESTAMP' |
| table_schema.fields.append(table_field) |
| table_field = bigquery.TableFieldSchema() |
| table_field.name = 'geo' |
| table_field.type = 'GEOGRAPHY' |
| table_schema.fields.append(table_field) |
| table = bigquery.Table( |
| tableReference=bigquery.TableReference( |
| projectId=self.project, |
| datasetId=self.dataset_id, |
| tableId=table_name), |
| schema=table_schema) |
| request = bigquery.BigqueryTablesInsertRequest( |
| projectId=self.project, datasetId=self.dataset_id, table=table) |
| self.bigquery_client.client.tables.Insert(request) |
| row_data = { |
| 'float': 0.33, 'numeric': Decimal('10'), 'bytes': |
| base64.b64encode(b'\xab\xac').decode('utf-8'), 'date': '3000-12-31', |
| 'time': '23:59:59', 'datetime': '2018-12-31T12:44:31', |
| 'timestamp': '2018-12-31 12:44:31.744957 UTC', 'geo': 'POINT(30 10)' |
| } |
| |
| table_data = [row_data] |
| # add rows with only one key value pair and None values for all other keys |
| for key, value in iteritems(row_data): |
| table_data.append({key: value}) |
| |
| self.bigquery_client.insert_rows( |
| self.project, self.dataset_id, table_name, table_data) |
| |
| @attr('IT') |
| def test_big_query_read(self): |
| table_name = 'python_write_table' |
| self.create_table(table_name) |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| args = self.test_pipeline.get_full_options_as_args() |
| |
| with beam.Pipeline(argv=args) as p: |
| result = (p | 'read' >> beam.io.Read(beam.io.BigQuerySource( |
| query='SELECT number, str FROM `%s`' % table_id, |
| use_standard_sql=True))) |
| assert_that(result, equal_to([{'number': 1, 'str': 'abc'}, |
| {'number': 2, 'str': 'def'}, |
| {'number': 3, 'str': u'你好'}, |
| {'number': 4, 'str': u'привет'}])) |
| |
| @attr('IT') |
| def test_big_query_read_new_types(self): |
| table_name = 'python_new_types' |
| self.create_table_new_types(table_name) |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| args = self.test_pipeline.get_full_options_as_args() |
| |
| expected_row = { |
| 'float': 0.33, 'numeric': Decimal('10'), 'bytes': |
| base64.b64encode(b'\xab\xac'), 'date': '3000-12-31', |
| 'time': '23:59:59', 'datetime': '2018-12-31T12:44:31', |
| 'timestamp': '2018-12-31 12:44:31.744957 UTC', 'geo': 'POINT(30 10)' |
| } |
| |
| expected_data = [expected_row] |
| |
| # add rows with only one key value pair and None values for all other keys |
| for key, value in iteritems(expected_row): |
| row = {k: None for k in expected_row} |
| row[key] = value |
| expected_data.append(row) |
| |
| with beam.Pipeline(argv=args) as p: |
| result = (p | 'read' >> beam.io.Read(beam.io.BigQuerySource( |
| query='SELECT float, numeric, bytes, date, time, datetime,' |
| 'timestamp, geo FROM `%s`' % table_id, |
| use_standard_sql=True))) |
| assert_that(result, equal_to(expected_data)) |
| |
| |
| if __name__ == '__main__': |
| logging.getLogger().setLevel(logging.INFO) |
| unittest.main() |
