Python bigquery.DatasetReference方法代碼示例

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_list_rows_as_dataframe(client):
    # [START bigquery_list_rows_dataframe]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    project = "bigquery-public-data"
    dataset_id = "samples"

    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    table_ref = dataset_ref.table("shakespeare")
    table = client.get_table(table_ref)

    df = client.list_rows(table).to_dataframe()
    # [END bigquery_list_rows_dataframe]
    assert isinstance(df, pandas.DataFrame)
    assert len(list(df)) == len(table.schema)  # verify the number of columns
    assert len(df) == table.num_rows  # verify the number of rows 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def tearDown(self):
        def _still_in_use(bad_request):
            return any(
                error["reason"] == "resourceInUse" for error in bad_request._errors
            )

        retry_in_use = RetryErrors(BadRequest, error_predicate=_still_in_use)
        retry_storage_errors_conflict = RetryErrors(
            (Conflict, TooManyRequests, InternalServerError, ServiceUnavailable)
        )
        for doomed in self.to_delete:
            if isinstance(doomed, storage.Bucket):
                retry_storage_errors_conflict(doomed.delete)(force=True)
            elif isinstance(doomed, (Dataset, bigquery.DatasetReference)):
                retry_in_use(Config.CLIENT.delete_dataset)(doomed, delete_contents=True)
            elif isinstance(doomed, (Table, bigquery.TableReference)):
                retry_in_use(Config.CLIENT.delete_table)(doomed)
            else:
                doomed.delete() 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_get_table_w_public_dataset(self):
        public = "bigquery-public-data"
        dataset_id = "samples"
        table_id = "shakespeare"
        table_ref = DatasetReference(public, dataset_id).table(table_id)

        # Get table with reference.
        table = Config.CLIENT.get_table(table_ref)
        self.assertEqual(table.table_id, table_id)
        self.assertEqual(table.dataset_id, dataset_id)
        self.assertEqual(table.project, public)
        schema_names = [field.name for field in table.schema]
        self.assertEqual(schema_names, ["word", "word_count", "corpus", "corpus_date"])

        # Get table with string.
        table = Config.CLIENT.get_table("{}.{}.{}".format(public, dataset_id, table_id))
        self.assertEqual(table.table_id, table_id)
        self.assertEqual(table.dataset_id, dataset_id)
        self.assertEqual(table.project, public) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_extract_table(self):
        local_id = unique_resource_id()
        bucket_name = "bq_extract_test" + local_id
        source_blob_name = "person_ages.csv"
        dataset_id = _make_dataset_id("load_gcs_then_extract")
        table_id = "test_table"
        project = Config.CLIENT.project
        dataset_ref = bigquery.DatasetReference(project, dataset_id)
        table_ref = dataset_ref.table(table_id)
        table = Table(table_ref)
        self.to_delete.insert(0, table)
        bucket = self._create_bucket(bucket_name)
        self._load_table_for_extract_table(bucket, source_blob_name, table_ref, ROWS)
        destination_blob_name = "person_ages_out.csv"
        destination = bucket.blob(destination_blob_name)
        destination_uri = "gs://{}/person_ages_out.csv".format(bucket_name)

        job = Config.CLIENT.extract_table(table_ref, destination_uri)
        job.result(timeout=100)

        self.to_delete.insert(0, destination)
        got_bytes = retry_storage_errors(destination.download_as_string)()
        got = got_bytes.decode("utf-8")
        self.assertIn("Bharney Rhubble", got) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_copy_table(self):
        # If we create a new table to copy from, the test won't work
        # because the new rows will be stored in the streaming buffer,
        # and copy jobs don't read the streaming buffer.
        # We could wait for the streaming buffer to empty, but that could
        # take minutes. Instead we copy a small public table.
        source_dataset = DatasetReference("bigquery-public-data", "samples")
        source_ref = source_dataset.table("shakespeare")
        dest_dataset = self.temp_dataset(_make_dataset_id("copy_table"))
        dest_ref = dest_dataset.table("destination_table")
        job_config = bigquery.CopyJobConfig()
        job = Config.CLIENT.copy_table(source_ref, dest_ref, job_config=job_config)
        job.result()

        dest_table = Config.CLIENT.get_table(dest_ref)
        self.to_delete.insert(0, dest_table)
        # Just check that we got some rows.
        got_rows = self._fetch_single_page(dest_table)
        self.assertTrue(len(got_rows) > 0) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def execute_transformation_query(bq_client):
    """Executes transformation query to a new destination table.
    Args:
        bq_client: Object representing a reference to a BigQuery Client
    """
    dataset_ref = bq_client.get_dataset(bigquery.DatasetReference(
        project=config.config_vars['billing_project_id'],
        dataset_id=config.config_vars['output_dataset_id']))
    table_ref = dataset_ref.table(config.config_vars['output_table_name'])
    job_config = bigquery.QueryJobConfig()
    job_config.destination = table_ref
    job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
    job_config.time_partitioning = bigquery.TimePartitioning(
        field='usage_start_time',
        expiration_ms=None)
    sql = file_to_string(config.config_vars['sql_file_path'])
    sql = sql.format(**config.config_vars)
    logging.info('Attempting query on all dates...')
    # Execute Query
    query_job = bq_client.query(
        sql,
        job_config=job_config)

    query_job.result()  # Waits for the query to finish
    logging.info('Transformation query complete. All partitions are updated.') 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def __init__(self):
    if constants.ON_LOCAL:
      return
    self._client = bigquery.Client()
    self._dataset_ref = bigquery.DatasetReference(
        self._client.project, constants.BIGQUERY_DATASET_NAME) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def setUp(self):
    super(BigQueryClientTest, self).setUp()
    bq_patcher = mock.patch.object(gcloud_bq, 'Client', autospec=True)
    self.addCleanup(bq_patcher.stop)
    self.bq_mock = bq_patcher.start()
    self.dataset_ref = mock.Mock(spec=gcloud_bq.DatasetReference)
    self.table = mock.Mock(spec=gcloud_bq.Table)
    self.table.schema = []
    self.dataset_ref.table.return_value = self.table
    with mock.patch.object(
        bigquery.BigQueryClient, '__init__', return_value=None):
      self.client = bigquery.BigQueryClient()
      self.client._client = self.bq_mock()
      self.client._dataset_ref = self.dataset_ref
      self.client._client.insert_rows.return_value = None
      self.client._client.get_table.return_value = self.table
    self.nested_schema = [
        gcloud_bq.SchemaField('nested_string_attribute', 'STRING', 'NULLABLE')]
    self.entity_schema = [
        gcloud_bq.SchemaField('string_attribute', 'STRING', 'NULLABLE'),
        gcloud_bq.SchemaField('integer_attribute', 'INTEGER', 'NULLABLE'),
        gcloud_bq.SchemaField('boolean_attribute', 'BOOLEAN', 'NULLABLE'),
        gcloud_bq.SchemaField(
            'nested_attribute', 'RECORD', 'NULLABLE', fields=self.nested_schema)
    ]

    test_device = device_model.Device(
        serial_number='abc123', chrome_device_id='123123')
    test_device.put()
    test_row = bigquery_row_model.BigQueryRow.add(
        test_device, datetime.datetime.utcnow(),
        loanertest.USER_EMAIL, 'Enroll', 'This is a test')
    self.test_row_dict = test_row.to_json_dict()
    self.test_table = [(self.test_row_dict['ndb_key'],
                        self.test_row_dict['timestamp'],
                        self.test_row_dict['actor'],
                        self.test_row_dict['method'],
                        self.test_row_dict['summary'],
                        self.test_row_dict['entity'])] 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def temp_dataset():
    from google.cloud import bigquery

    client = bigquery.Client()
    dataset_id = "temp_dataset_{}".format(int(time.time() * 1000))
    dataset_ref = bigquery.DatasetReference(client.project, dataset_id)
    dataset = client.create_dataset(bigquery.Dataset(dataset_ref))
    yield dataset
    client.delete_dataset(dataset, delete_contents=True) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_query_succeed(self, mock_client,
        mock_kfp_context, mock_dump_json, mock_display):
        mock_kfp_context().__enter__().context_id.return_value = 'ctx1'
        mock_client().get_job.side_effect = exceptions.NotFound('not found')
        mock_dataset = bigquery.DatasetReference('project-1', 'dataset-1')
        mock_client().dataset.return_value = mock_dataset
        mock_client().get_dataset.side_effect = exceptions.NotFound('not found')
        mock_response = {
            'configuration': {
                'query': {
                    'query': 'SELECT * FROM table_1'
                }
            }
        }
        mock_client().query.return_value.to_api_repr.return_value = mock_response

        result = query('SELECT * FROM table_1', 'project-1', 'dataset-1', 
            output_gcs_path='gs://output/path')

        self.assertEqual(mock_response, result)
        mock_client().create_dataset.assert_called()
        expected_job_config = bigquery.QueryJobConfig()
        expected_job_config.create_disposition = bigquery.job.CreateDisposition.CREATE_IF_NEEDED
        expected_job_config.write_disposition = bigquery.job.WriteDisposition.WRITE_TRUNCATE
        expected_job_config.destination = mock_dataset.table('query_ctx1')
        mock_client().query.assert_called_with('SELECT * FROM table_1',mock.ANY,
            job_id = 'query_ctx1')
        actual_job_config = mock_client().query.call_args_list[0][0][1]
        self.assertDictEqual(
            expected_job_config.to_api_repr(),
            actual_job_config.to_api_repr()
        )
        mock_client().extract_table.assert_called_with(
            mock_dataset.table('query_ctx1'),
            'gs://output/path') 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_query_no_output_path(self, mock_client,
        mock_kfp_context, mock_dump_json, mock_display):
        mock_kfp_context().__enter__().context_id.return_value = 'ctx1'
        mock_client().get_job.side_effect = exceptions.NotFound('not found')
        mock_dataset = bigquery.DatasetReference('project-1', 'dataset-1')
        mock_client().dataset.return_value = mock_dataset
        mock_client().get_dataset.return_value = bigquery.Dataset(mock_dataset)
        mock_response = {
            'configuration': {
                'query': {
                    'query': 'SELECT * FROM table_1'
                }
            }
        }
        mock_client().query.return_value.to_api_repr.return_value = mock_response

        result = query('SELECT * FROM table_1', 'project-1', 'dataset-1', 'table-1')

        self.assertEqual(mock_response, result)
        mock_client().create_dataset.assert_not_called()
        mock_client().extract_table.assert_not_called()

        expected_job_config = bigquery.QueryJobConfig()
        expected_job_config.create_disposition = bigquery.job.CreateDisposition.CREATE_IF_NEEDED
        expected_job_config.write_disposition = bigquery.job.WriteDisposition.WRITE_TRUNCATE
        expected_job_config.destination = mock_dataset.table('table-1')
        mock_client().query.assert_called_with('SELECT * FROM table_1',mock.ANY,
            job_id = 'query_ctx1')
        actual_job_config = mock_client().query.call_args_list[0][0][1]
        self.assertDictEqual(
            expected_job_config.to_api_repr(),
            actual_job_config.to_api_repr()
        ) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def to_delete(client):
    doomed = []
    yield doomed
    for item in doomed:
        if isinstance(item, (bigquery.Dataset, bigquery.DatasetReference)):
            retry_429(client.delete_dataset)(item, delete_contents=True)
        elif isinstance(item, storage.Bucket):
            retry_storage_errors(item.delete)()
        else:
            retry_429(item.delete)() 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_create_table_nested_repeated_schema(client, to_delete):
    dataset_id = "create_table_nested_repeated_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    client.create_dataset(dataset)
    to_delete.append(dataset)

    # [START bigquery_nested_repeated_schema]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # project = client.project
    # dataset_ref = bigquery.DatasetReference(project, 'my_dataset')

    schema = [
        bigquery.SchemaField("id", "STRING", mode="NULLABLE"),
        bigquery.SchemaField("first_name", "STRING", mode="NULLABLE"),
        bigquery.SchemaField("last_name", "STRING", mode="NULLABLE"),
        bigquery.SchemaField("dob", "DATE", mode="NULLABLE"),
        bigquery.SchemaField(
            "addresses",
            "RECORD",
            mode="REPEATED",
            fields=[
                bigquery.SchemaField("status", "STRING", mode="NULLABLE"),
                bigquery.SchemaField("address", "STRING", mode="NULLABLE"),
                bigquery.SchemaField("city", "STRING", mode="NULLABLE"),
                bigquery.SchemaField("state", "STRING", mode="NULLABLE"),
                bigquery.SchemaField("zip", "STRING", mode="NULLABLE"),
                bigquery.SchemaField("numberOfYears", "STRING", mode="NULLABLE"),
            ],
        ),
    ]
    table_ref = dataset_ref.table("my_table")
    table = bigquery.Table(table_ref, schema=schema)
    table = client.create_table(table)  # API request

    print("Created table {}".format(table.full_table_id))
    # [END bigquery_nested_repeated_schema] 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_create_table_cmek(client, to_delete):
    dataset_id = "create_table_cmek_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    client.create_dataset(dataset)
    to_delete.append(dataset)

    # [START bigquery_create_table_cmek]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # dataset_id = 'my_dataset'

    table_ref = dataset.table("my_table")
    table = bigquery.Table(table_ref)

    # Set the encryption key to use for the table.
    # TODO: Replace this key with a key you have created in Cloud KMS.
    kms_key_name = "projects/{}/locations/{}/keyRings/{}/cryptoKeys/{}".format(
        "cloud-samples-tests", "us", "test", "test"
    )
    table.encryption_configuration = bigquery.EncryptionConfiguration(
        kms_key_name=kms_key_name
    )

    table = client.create_table(table)  # API request

    assert table.encryption_configuration.kms_key_name == kms_key_name
    # [END bigquery_create_table_cmek] 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import DatasetReference [as 別名]
def test_create_partitioned_table(client, to_delete):
    dataset_id = "create_table_partitioned_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = client.create_dataset(dataset_ref)
    to_delete.append(dataset)

    # [START bigquery_create_table_partitioned]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # project = client.project
    # dataset_ref = bigquery.DatasetReference(project, 'my_dataset')

    table_ref = dataset_ref.table("my_partitioned_table")
    schema = [
        bigquery.SchemaField("name", "STRING"),
        bigquery.SchemaField("post_abbr", "STRING"),
        bigquery.SchemaField("date", "DATE"),
    ]
    table = bigquery.Table(table_ref, schema=schema)
    table.time_partitioning = bigquery.TimePartitioning(
        type_=bigquery.TimePartitioningType.DAY,
        field="date",  # name of column to use for partitioning
        expiration_ms=7776000000,
    )  # 90 days

    table = client.create_table(table)

    print(
        "Created table {}, partitioned on column {}".format(
            table.table_id, table.time_partitioning.field
        )
    )
    # [END bigquery_create_table_partitioned]

    assert table.time_partitioning.type_ == "DAY"
    assert table.time_partitioning.field == "date"
    assert table.time_partitioning.expiration_ms == 7776000000