| #!/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.""" |
| # pytype: skip-file |
| |
| from __future__ import absolute_import |
| |
| import base64 |
| import datetime |
| import logging |
| import random |
| import time |
| import unittest |
| from decimal import Decimal |
| |
| import hamcrest as hc |
| import pytz |
| 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.io.gcp.tests.bigquery_matcher import BigqueryFullResultMatcher |
| from apache_beam.testing.test_pipeline import TestPipeline |
| |
| # 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 BigQueryWriteIntegrationTests(unittest.TestCase): |
| BIG_QUERY_DATASET_ID = 'python_write_to_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, table_name): |
| table_schema = bigquery.TableSchema() |
| 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 = 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) |
| |
| @attr('IT') |
| def test_big_query_write(self): |
| table_name = 'python_write_table' |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| input_data = [ |
| {'number': 1, 'str': 'abc'}, |
| {'number': 2, 'str': 'def'}, |
| {'number': 3, 'str': u'你好'}, |
| {'number': 4, 'str': u'привет'}, |
| ] |
| table_schema = {"fields": [ |
| {"name": "number", "type": "INTEGER"}, |
| {"name": "str", "type": "STRING"}]} |
| |
| pipeline_verifiers = [ |
| BigqueryFullResultMatcher( |
| project=self.project, |
| query="SELECT number, str FROM %s" % table_id, |
| data=[(1, 'abc',), (2, 'def',), (3, u'你好',), (4, u'привет',)])] |
| |
| args = self.test_pipeline.get_full_options_as_args( |
| on_success_matcher=hc.all_of(*pipeline_verifiers)) |
| |
| with beam.Pipeline(argv=args) as p: |
| # pylint: disable=expression-not-assigned |
| (p | 'create' >> beam.Create(input_data) |
| | 'write' >> beam.io.WriteToBigQuery( |
| table_id, |
| schema=table_schema, |
| create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_EMPTY)) |
| |
| @attr('IT') |
| def test_big_query_write_schema_autodetect(self): |
| if self.runner_name == 'TestDataflowRunner': |
| self.skipTest('DataflowRunner does not support schema autodetection') |
| |
| table_name = 'python_write_table' |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| input_data = [ |
| {'number': 1, 'str': 'abc'}, |
| {'number': 2, 'str': 'def'}, |
| ] |
| |
| pipeline_verifiers = [ |
| BigqueryFullResultMatcher( |
| project=self.project, |
| query="SELECT number, str FROM %s" % table_id, |
| data=[(1, 'abc',), (2, 'def',)])] |
| |
| args = self.test_pipeline.get_full_options_as_args( |
| on_success_matcher=hc.all_of(*pipeline_verifiers), |
| experiments='use_beam_bq_sink') |
| |
| with beam.Pipeline(argv=args) as p: |
| # pylint: disable=expression-not-assigned |
| (p | 'create' >> beam.Create(input_data) |
| | 'write' >> beam.io.WriteToBigQuery( |
| table_id, |
| method=beam.io.WriteToBigQuery.Method.FILE_LOADS, |
| schema=beam.io.gcp.bigquery.SCHEMA_AUTODETECT, |
| create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_EMPTY)) |
| |
| @attr('IT') |
| def test_big_query_write_new_types(self): |
| table_name = 'python_new_types_table' |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| 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)' |
| } |
| |
| input_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): |
| input_data.append({key: value}) |
| |
| table_schema = {"fields": [ |
| {"name": "float", "type": "FLOAT"}, |
| {"name": "numeric", "type": "NUMERIC"}, |
| {"name": "bytes", "type": "BYTES"}, |
| {"name": "date", "type": "DATE"}, |
| {"name": "time", "type": "TIME"}, |
| {"name": "datetime", "type": "DATETIME"}, |
| {"name": "timestamp", "type": "TIMESTAMP"}, |
| {"name": "geo", "type": "GEOGRAPHY"} |
| ]} |
| |
| expected_row = (0.33, Decimal('10'), b'\xab\xac', |
| datetime.date(3000, 12, 31), datetime.time(23, 59, 59), |
| datetime.datetime(2018, 12, 31, 12, 44, 31), |
| datetime.datetime(2018, 12, 31, 12, 44, 31, 744957, |
| tzinfo=pytz.utc), 'POINT(30 10)', |
| ) |
| |
| expected_data = [expected_row] |
| |
| # add rows with only one key value pair and None values for all other keys |
| for i, value in enumerate(expected_row): |
| row = [None]*len(expected_row) |
| row[i] = value |
| expected_data.append(tuple(row)) |
| |
| pipeline_verifiers = [ |
| BigqueryFullResultMatcher( |
| project=self.project, |
| query='SELECT float, numeric, bytes, date, time, datetime,' |
| 'timestamp, geo FROM %s' % table_id, |
| data=expected_data)] |
| |
| args = self.test_pipeline.get_full_options_as_args( |
| on_success_matcher=hc.all_of(*pipeline_verifiers)) |
| |
| with beam.Pipeline(argv=args) as p: |
| # pylint: disable=expression-not-assigned |
| (p | 'create' >> beam.Create(input_data) |
| | 'write' >> beam.io.WriteToBigQuery( |
| table_id, |
| schema=table_schema, |
| create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_EMPTY)) |
| |
| @attr('IT') |
| def test_big_query_write_without_schema(self): |
| table_name = 'python_no_schema_table' |
| self.create_table(table_name) |
| table_id = '{}.{}'.format(self.dataset_id, table_name) |
| |
| input_data = [ |
| {'bytes': b'xyw', 'date': '2011-01-01', 'time': '23:59:59.999999'}, |
| {'bytes': b'abc', 'date': '2000-01-01', 'time': '00:00:00'}, |
| {'bytes': b'\xe4\xbd\xa0\xe5\xa5\xbd', 'date': '3000-12-31', |
| 'time': '23:59:59'}, |
| {'bytes': b'\xab\xac\xad', 'date': '2000-01-01', 'time': '00:00:00'} |
| ] |
| # bigquery io expects bytes to be base64 encoded values |
| for row in input_data: |
| row['bytes'] = base64.b64encode(row['bytes']) |
| |
| pipeline_verifiers = [ |
| BigqueryFullResultMatcher( |
| project=self.project, |
| query="SELECT bytes, date, time FROM %s" % table_id, |
| data=[(b'xyw', datetime.date(2011, 1, 1), |
| datetime.time(23, 59, 59, 999999), ), |
| (b'abc', datetime.date(2000, 1, 1), |
| datetime.time(0, 0, 0), ), |
| (b'\xe4\xbd\xa0\xe5\xa5\xbd', datetime.date(3000, 12, 31), |
| datetime.time(23, 59, 59), ), |
| (b'\xab\xac\xad', datetime.date(2000, 1, 1), |
| datetime.time(0, 0, 0), )])] |
| |
| args = self.test_pipeline.get_full_options_as_args( |
| on_success_matcher=hc.all_of(*pipeline_verifiers)) |
| |
| with beam.Pipeline(argv=args) as p: |
| # pylint: disable=expression-not-assigned |
| (p | 'create' >> beam.Create(input_data) |
| | 'write' >> beam.io.WriteToBigQuery( |
| table_id, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND)) |
| |
| |
| if __name__ == '__main__': |
| logging.getLogger().setLevel(logging.INFO) |
| unittest.main() |