hunk.code_window:         conn.close()
           logging.info(
               "Done loading. Loaded a total of {i} rows".format(**locals()))
       @staticmethod
       def _serialize_cell(cell):
           if isinstance(cell, basestring):
               return "'" + str(cell).replace("'", "''") + "'"
           elif cell is None:
  
hunk.labels: keep keep keep keep keep replace keep keep keep
hunk.after_edit:     def _serialize_cell(cell, conn=None):
           """
           Returns the SQL literal of the cell as a string.
           :param cell: The cell to insert into the table
           :type cell: object
           :param conn: The database connection
           :type conn: connection object
           :return: The serialized cell
           :rtype: str
           """
  
hunk.file_path: airflow/hooks/dbapi_hook.py
hunk.type: replace
file: # -*- coding: utf-8 -*-
  #
  # Licensed 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.
  
  from airflow.contrib.hooks.bigquery_hook import BigQueryHook
  from airflow.operators.check_operator import CheckOperator, ValueCheckOperator, IntervalCheckOperator
  from airflow.utils.decorators import apply_defaults
  
  
  class BigQueryCheckOperator(CheckOperator):
      """
      Performs checks against Presto. The ``BigQueryCheckOperator`` expects
      a sql query that will return a single row. Each value on that
      first row is evaluated using python ``bool`` casting. If any of the
      values return ``False`` the check is failed and errors out.
  
      Note that Python bool casting evals the following as ``False``:
  
      * ``False``
      * ``0``
      * Empty string (``""``)
      * Empty list (``[]``)
      * Empty dictionary or set (``{}``)
  
      Given a query like ``SELECT COUNT(*) FROM foo``, it will fail only if
      the count ``== 0``. You can craft much more complex query that could,
      for instance, check that the table has the same number of rows as
      the source table upstream, or that the count of today's partition is
      greater than yesterday's partition, or that a set of metrics are less
      than 3 standard deviation for the 7 day average.
  
      This operator can be used as a data quality check in your pipeline, and
      depending on where you put it in your DAG, you have the choice to
      stop the critical path, preventing from
      publishing dubious data, or on the side and receive email alterts
      without stopping the progress of the DAG.
  
      :param sql: the sql to be executed
      :type sql: string
      :param bigquery_conn_id: reference to the BigQuery database
      :type presto_conn_id: string
      """
  
      @apply_defaults
      def __init__(
              self,
              sql,
              bigquery_conn_id='bigquery_default',
              *args,
              **kwargs):
          super(BigQueryCheckOperator, self).__init__(sql=sql, *args, **kwargs)
          self.bigquery_conn_id = bigquery_conn_id
          self.sql = sql
  
      def get_db_hook(self):
          return BigQueryHook(bigquery_conn_id=self.bigquery_conn_id)
  
  
  class BigQueryValueCheckOperator(ValueCheckOperator):
      """
      Performs a simple value check using sql code.
  
      :param sql: the sql to be executed
      :type sql: string
      """
  
      @apply_defaults
      def __init__(
              self, sql, pass_value, tolerance=None,
              bigquery_conn_id='bigquery_default',
              *args, **kwargs):
          super(BigQueryValueCheckOperator, self).__init__(
              sql=sql, pass_value=pass_value, tolerance=tolerance,
              *args, **kwargs)
          self.bigquery_conn_id = bigquery_conn_id
  
      def get_db_hook(self):
          return BigQueryHook(bigquery_conn_id=self.bigquery_conn_id)
  
  
  class BigQueryIntervalCheckOperator(IntervalCheckOperator):
      """
      Checks that the values of metrics given as SQL expressions are within
      a certain tolerance of the ones from days_back before.
  
      This method constructs a query like so:
  
      SELECT {metrics_threshold_dict_key} FROM {table}
          WHERE {date_filter_column}=<date>
  
      :param table: the table name
      :type table: str
      :param days_back: number of days between ds and the ds we want to check
          against. Defaults to 7 days
      :type days_back: int
      :param metrics_threshold: a dictionary of ratios indexed by metrics, for
          example 'COUNT(*)': 1.5 would require a 50 percent or less difference
          between the current day, and the prior days_back.
      :type metrics_threshold: dict
      """
  
      @apply_defaults
      def __init__(
              self, table, metrics_thresholds,
              date_filter_column='ds', days_back=-7,
              bigquery_conn_id='bigquery_default',
              *args, **kwargs):
          super(BigQueryIntervalCheckOperator, self).__init__(
              table=table, metrics_thresholds=metrics_thresholds,
              date_filter_column=date_filter_column, days_back=days_back,
              *args, **kwargs)
          self.bigquery_conn_id = bigquery_conn_id
  
      def get_db_hook(self):
          return BigQueryHook(bigquery_conn_id=self.bigquery_conn_id)
  