Python bigquery.SchemaField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 SchemaField [as 別名]
def test_table_to_dataframe(capsys, clients):
    from google.cloud import bigquery

    bqclient, bqstorageclient = clients

    # [START bigquerystorage_pandas_tutorial_all]
    # [START bigquerystorage_pandas_tutorial_read_table]
    # Download a table.
    table = bigquery.TableReference.from_string(
        "bigquery-public-data.utility_us.country_code_iso"
    )
    rows = bqclient.list_rows(
        table,
        selected_fields=[
            bigquery.SchemaField("country_name", "STRING"),
            bigquery.SchemaField("fips_code", "STRING"),
        ],
    )
    dataframe = rows.to_dataframe(bqstorage_client=bqstorageclient)
    print(dataframe.head())
    # [END bigquerystorage_pandas_tutorial_read_table]
    # [END bigquerystorage_pandas_tutorial_all]

    out, _ = capsys.readouterr()
    assert "country_name" in out 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def manage_tables(dataset_id, table_id):
  """Create tables in datasets in BigQuery."""
  try:
    get_bq_dataset(dataset_id)
  except exceptions.NotFound as e:
    return flask.jsonify(error=e.code, text=e.message), e.code

  table_ref = bq_client.dataset(dataset_id).table(table_id)
  try:
    jsonschema.validate(flask.request.json, bq_table_schema)
  except jsonschema.ValidationError:
    error_msg = 'unable to validate provided payload.'
    return flask.jsonify(error=400, text=error_msg), 400

  schema = [bigquery.SchemaField(field['name'], field['type'],
                                 field.get('mode') or 'NULLABLE')
            for field in flask.request.json]
  table = bigquery.Table(table_ref, schema=schema)
  try:
    table = bq_client.create_table(table)
  except exceptions.GoogleAPIError as e:
    return flask.jsonify(error=e.message), 400

  return flask.jsonify(result='success'), 200 

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

    # [START bigquery_create_table]
    from google.cloud import bigquery

    # Construct a BigQuery client object.
    client = bigquery.Client()

    # TODO(developer): Set table_id to the ID of the table to create.
    # table_id = "your-project.your_dataset.your_table_name"

    schema = [
        bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
    ]

    table = bigquery.Table(table_id, schema=schema)
    table = client.create_table(table)  # Make an API request.
    print(
        "Created table {}.{}.{}".format(table.project, table.dataset_id, table.table_id)
    )
    # [END bigquery_create_table] 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def test_update_table_require_partition_filter(capsys, random_table_id, client):

    # Make a partitioned table.
    schema = [bigquery.SchemaField("transaction_timestamp", "TIMESTAMP")]
    table = bigquery.Table(random_table_id, schema=schema)
    table.time_partitioning = bigquery.TimePartitioning(field="transaction_timestamp")
    table = client.create_table(table)

    update_table_require_partition_filter.update_table_require_partition_filter(
        random_table_id
    )
    out, _ = capsys.readouterr()
    assert (
        "Updated table '{}' with require_partition_filter=True".format(random_table_id)
        in out
    ) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def test_update_table_schema(self):
        dataset = self.temp_dataset(_make_dataset_id("update_table"))

        TABLE_NAME = "test_table"
        table_arg = Table(dataset.table(TABLE_NAME), schema=SCHEMA)
        self.assertFalse(_table_exists(table_arg))
        table = retry_403(Config.CLIENT.create_table)(table_arg)
        self.to_delete.insert(0, table)
        self.assertTrue(_table_exists(table))
        voter = bigquery.SchemaField("voter", "BOOLEAN", mode="NULLABLE")
        schema = table.schema
        schema.append(voter)
        table.schema = schema

        updated_table = Config.CLIENT.update_table(table, ["schema"])

        self.assertEqual(len(updated_table.schema), len(schema))
        for found, expected in zip(updated_table.schema, schema):
            self.assertEqual(found.name, expected.name)
            self.assertEqual(found.field_type, expected.field_type)
            self.assertEqual(found.mode, expected.mode) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def test_load_table_from_dataframe_w_nullable_int64_datatype_automatic_schema(self):
        """Test that a DataFrame containing column with None-type values and int64 datatype
        can be uploaded without specifying a schema.

        https://github.com/googleapis/python-bigquery/issues/22
        """

        dataset_id = _make_dataset_id("bq_load_test")
        self.temp_dataset(dataset_id)
        table_id = "{}.{}.load_table_from_dataframe_w_nullable_int64_datatype".format(
            Config.CLIENT.project, dataset_id
        )
        df_data = collections.OrderedDict(
            [("x", pandas.Series([1, 2, None, 4], dtype="Int64"))]
        )
        dataframe = pandas.DataFrame(df_data, columns=df_data.keys())
        load_job = Config.CLIENT.load_table_from_dataframe(dataframe, table_id)
        load_job.result()
        table = Config.CLIENT.get_table(table_id)
        self.assertEqual(tuple(table.schema), (bigquery.SchemaField("x", "INTEGER"),))
        self.assertEqual(table.num_rows, 4) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def test_list_rows_max_results_w_bqstorage(self):
        table_ref = DatasetReference("bigquery-public-data", "utility_us").table(
            "country_code_iso"
        )
        bqstorage_client = bigquery_storage_v1.BigQueryReadClient(
            credentials=Config.CLIENT._credentials
        )

        row_iterator = Config.CLIENT.list_rows(
            table_ref,
            selected_fields=[bigquery.SchemaField("country_name", "STRING")],
            max_results=100,
        )
        dataframe = row_iterator.to_dataframe(bqstorage_client=bqstorage_client)

        self.assertEqual(len(dataframe.index), 100) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def read_bigquery_schema_from_json_recursive(json_schema):
    """
    CAUTION: Recursive function
    This method can generate BQ schemas for nested records
    """
    result = []
    for field in json_schema:
        if field.get('type').lower() == 'record' and field.get('fields'):
            schema = bigquery.SchemaField(
                name=field.get('name'),
                field_type=field.get('type', 'STRING'),
                mode=field.get('mode', 'NULLABLE'),
                description=field.get('description'),
                fields=read_bigquery_schema_from_json_recursive(field.get('fields'))
            )
        else:
            schema = bigquery.SchemaField(
                name=field.get('name'),
                field_type=field.get('type', 'STRING'),
                mode=field.get('mode', 'NULLABLE'),
                description=field.get('description')
            )
        result.append(schema)
    return result 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def test_get_bq_translated_schema(self):
        """Tests TableUtil.get_bq_translated_schema().

        Tests TableUtil's ability to translate a json schema to a BigQuery
        schema in List[google.cloud.bigquery.schema.SchemaField] format.

        Returns:
            True if test passes, else False.
        """
        expected_bq_schema = [
            bigquery.SchemaField('string1', 'STRING', 'REQUIRED',
                                 'description1'),
            bigquery.SchemaField('numeric1', 'NUMERIC', 'REQUIRED',
                                 'description2')
        ]
        bq_schema = self.table_util.get_bq_translated_schema()

        assert expected_bq_schema == bq_schema 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def csv_in_gcs_to_table(bucket_name: str, object_name: str, dataset_id: str,
                        table_id: str,
                        schema: List[bigquery.SchemaField]) -> None:
    """Upload CSV to BigQuery table.
        If the table already exists, it overwrites the table data.

    Args:
        bucket_name: Bucket name for holding the object
        object_name: Name of object to be uploaded
        dataset_id: Dataset id where the table is located.
        table_id: String holding id of hte table.
        schema: Schema of the table_id
    """
    client = bigquery.Client()
    dataset_ref = client.dataset(dataset_id)
    job_config = bigquery.LoadJobConfig()
    job_config.schema = schema
    job_config.source_format = bigquery.SourceFormat.CSV
    job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
    uri = "gs://{}/{}".format(bucket_name, object_name)
    load_job = client.load_table_from_uri(uri,
                                          dataset_ref.table(table_id),
                                          job_config=job_config)
    load_job.result() 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def _generate_schema(entity_fields=None):
  """Generates a BigQuery schema.

  Args:
    entity_fields: list of bigquery.SchemaField objects, the fields to include
      in the entity record.

  Returns:
    A list of bigquery.SchemaField objects.
  """
  schema = [
      bigquery.SchemaField(
          'ndb_key', 'STRING', 'REQUIRED',
          description='ndb key of the entity.'),
      bigquery.SchemaField('timestamp', 'TIMESTAMP', 'REQUIRED'),
      bigquery.SchemaField(
          'actor',
          'STRING',
          'REQUIRED',
          description='User performing the action.'),
      bigquery.SchemaField(
          'method',
          'STRING',
          'REQUIRED',
          description='Method performing the action.'),
      bigquery.SchemaField(
          'summary',
          'STRING',
          'REQUIRED',
          description='User generated summary.')
  ]
  if entity_fields:
    schema.append(
        bigquery.SchemaField(
            'entity',
            'RECORD',
            'NULLABLE',
            description='Current attributes of the entity.',
            fields=entity_fields))
  return schema 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [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 SchemaField [as 別名]
def test_generate_schema_no_entity(self):
    generated_schema = bigquery._generate_schema()

    self.assertLen(generated_schema, 5)
    self.assertIsInstance(generated_schema[0], gcloud_bq.SchemaField) 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def _populate_schema_names(schema):
  """Creates a list with the names that are inside of the schema.

  Args:
    schema: List[bigquery.SchemaField], a list of bigquery.SchemaField objects.

  Returns:
    A list containing the names of the schema.
  """
  names = []
  for name in schema:
    names.append(name.name)
  return names 

# 需要導入模塊: from google.cloud import bigquery [as 別名]
# 或者: from google.cloud.bigquery import SchemaField [as 別名]
def build_schema(schema):
    SCHEMA = []
    for key in schema['properties'].keys():
        
        if not (bool(schema['properties'][key])):
            # if we endup with an empty record.
            continue

        schema_name, schema_type, schema_mode, schema_description, schema_fields = define_schema(schema['properties'][key], key)
        SCHEMA.append(SchemaField(schema_name, schema_type, schema_mode, schema_description, schema_fields))

    return SCHEMA