hunk.code_window: #
   import os
   import time
   import datetime
   import six
   import re
  
hunk.labels: keep keep add keep keep keep keep keep
hunk.after_edit: import pathlib
  
hunk.file_path: airflow/contrib/hooks/qubole_hook.py
hunk.type: add
file: # -*- coding: utf-8 -*-
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  # or more contributor license agreements.  See the NOTICE file
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  # 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
  #
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  # 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.
  
  import json
  
  from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
  from airflow.contrib.hooks.bigquery_hook import BigQueryHook
  from airflow.models import BaseOperator
  from airflow.utils.decorators import apply_defaults
  
  
  class GoogleCloudStorageToBigQueryOperator(BaseOperator):
      """
      Loads files from Google cloud storage into BigQuery.
  
      The schema to be used for the BigQuery table may be specified in one of
      two ways. You may either directly pass the schema fields in, or you may
      point the operator to a Google cloud storage object name. The object in
      Google cloud storage must be a JSON file with the schema fields in it.
  
      .. seealso::
          For more information on how to use this operator, take a look at the guide:
          :ref:`howto/operator:GoogleCloudStorageToBigQueryOperator`
  
      :param bucket: The bucket to load from. (templated)
      :type bucket: str
      :param source_objects: List of Google cloud storage URIs to load from. (templated)
          If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
      :type source_objects: list[str]
      :param destination_project_dataset_table: The dotted
          ``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into.
          If ``<project>`` is not included, project will be the project defined in
          the connection json. (templated)
      :type destination_project_dataset_table: str
      :param schema_fields: If set, the schema field list as defined here:
          https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
          Should not be set when source_format is 'DATASTORE_BACKUP'.
      :type schema_fields: list
      :param schema_object: If set, a GCS object path pointing to a .json file that
          contains the schema for the table. (templated)
      :type schema_object: str
      :param source_format: File format to export.
      :type source_format: str
      :param compression: [Optional] The compression type of the data source.
          Possible values include GZIP and NONE.
          The default value is NONE.
          This setting is ignored for Google Cloud Bigtable,
          Google Cloud Datastore backups and Avro formats.
      :type compression: str
      :param create_disposition: The create disposition if the table doesn't exist.
      :type create_disposition: str
      :param skip_leading_rows: Number of rows to skip when loading from a CSV.
      :type skip_leading_rows: int
      :param write_disposition: The write disposition if the table already exists.
      :type write_disposition: str
      :param field_delimiter: The delimiter to use when loading from a CSV.
      :type field_delimiter: str
      :param max_bad_records: The maximum number of bad records that BigQuery can
          ignore when running the job.
      :type max_bad_records: int
      :param quote_character: The value that is used to quote data sections in a CSV file.
      :type quote_character: str
      :param ignore_unknown_values: [Optional] Indicates if BigQuery should allow
          extra values that are not represented in the table schema.
          If true, the extra values are ignored. If false, records with extra columns
          are treated as bad records, and if there are too many bad records, an
          invalid error is returned in the job result.
      :type ignore_unknown_values: bool
      :param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
      :type allow_quoted_newlines: bool
      :param allow_jagged_rows: Accept rows that are missing trailing optional columns.
          The missing values are treated as nulls. If false, records with missing trailing
          columns are treated as bad records, and if there are too many bad records, an
          invalid error is returned in the job result. Only applicable to CSV, ignored
          for other formats.
      :type allow_jagged_rows: bool
      :param max_id_key: If set, the name of a column in the BigQuery table
          that's to be loaded. This will be used to select the MAX value from
          BigQuery after the load occurs. The results will be returned by the
          execute() command, which in turn gets stored in XCom for future
          operators to use. This can be helpful with incremental loads--during
          future executions, you can pick up from the max ID.
      :type max_id_key: str
      :param bigquery_conn_id: Reference to a specific BigQuery hook.
      :type bigquery_conn_id: str
      :param google_cloud_storage_conn_id: Reference to a specific Google
          cloud storage hook.
      :type google_cloud_storage_conn_id: str
      :param delegate_to: The account to impersonate, if any. For this to
          work, the service account making the request must have domain-wide
          delegation enabled.
      :type delegate_to: str
      :param schema_update_options: Allows the schema of the destination
          table to be updated as a side effect of the load job.
      :type schema_update_options: list
      :param src_fmt_configs: configure optional fields specific to the source format
      :type src_fmt_configs: dict
      :param external_table: Flag to specify if the destination table should be
          a BigQuery external table. Default Value is False.
      :type external_table: bool
      :param time_partitioning: configure optional time partitioning fields i.e.
          partition by field, type and  expiration as per API specifications.
          Note that 'field' is not available in concurrency with
          dataset.table$partition.
      :type time_partitioning: dict
      :param cluster_fields: Request that the result of this load be stored sorted
          by one or more columns. This is only available in conjunction with
          time_partitioning. The order of columns given determines the sort order.
          Not applicable for external tables.
      :type cluster_fields: list[str]
      :param autodetect: [Optional] Indicates if we should automatically infer the
          options and schema for CSV and JSON sources. (Default: ``False``)
      :type autodetect: bool
      """
      template_fields = ('bucket', 'source_objects',
                         'schema_object', 'destination_project_dataset_table')
      template_ext = ('.sql',)
      ui_color = '#f0eee4'
  
      @apply_defaults
      def __init__(self,
                   bucket,
                   source_objects,
                   destination_project_dataset_table,
                   schema_fields=None,
                   schema_object=None,
                   source_format='CSV',
                   compression='NONE',
                   create_disposition='CREATE_IF_NEEDED',
                   skip_leading_rows=0,
                   write_disposition='WRITE_EMPTY',
                   field_delimiter=',',
                   max_bad_records=0,
                   quote_character=None,
                   ignore_unknown_values=False,
                   allow_quoted_newlines=False,
                   allow_jagged_rows=False,
                   max_id_key=None,
                   bigquery_conn_id='google_cloud_default',
                   google_cloud_storage_conn_id='google_cloud_default',
                   delegate_to=None,
                   schema_update_options=(),
                   src_fmt_configs=None,
                   external_table=False,
                   time_partitioning=None,
                   cluster_fields=None,
                   autodetect=False,
                   *args, **kwargs):
  
          super().__init__(*args, **kwargs)
  
          # GCS config
          if src_fmt_configs is None:
              src_fmt_configs = {}
          if time_partitioning is None:
              time_partitioning = {}
          self.bucket = bucket
          self.source_objects = source_objects
          self.schema_object = schema_object
  
          # BQ config
          self.destination_project_dataset_table = destination_project_dataset_table
          self.schema_fields = schema_fields
          self.source_format = source_format
          self.compression = compression
          self.create_disposition = create_disposition
          self.skip_leading_rows = skip_leading_rows
          self.write_disposition = write_disposition
          self.field_delimiter = field_delimiter
          self.max_bad_records = max_bad_records
          self.quote_character = quote_character
          self.ignore_unknown_values = ignore_unknown_values
          self.allow_quoted_newlines = allow_quoted_newlines
          self.allow_jagged_rows = allow_jagged_rows
          self.external_table = external_table
  
          self.max_id_key = max_id_key
          self.bigquery_conn_id = bigquery_conn_id
          self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
          self.delegate_to = delegate_to
  
          self.schema_update_options = schema_update_options
          self.src_fmt_configs = src_fmt_configs
          self.time_partitioning = time_partitioning
          self.cluster_fields = cluster_fields
          self.autodetect = autodetect
  
      def execute(self, context):
          bq_hook = BigQueryHook(bigquery_conn_id=self.bigquery_conn_id,
                                 delegate_to=self.delegate_to)
  
          if not self.schema_fields:
              if self.schema_object and self.source_format != 'DATASTORE_BACKUP':
                  gcs_hook = GoogleCloudStorageHook(
                      google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
                      delegate_to=self.delegate_to)
                  schema_fields = json.loads(gcs_hook.download(
                      self.bucket,
                      self.schema_object).decode("utf-8"))
              elif self.schema_object is None and self.autodetect is False:
                  raise ValueError('At least one of `schema_fields`, `schema_object`, '
                                   'or `autodetect` must be passed.')
              else:
                  schema_fields = None
  
          else:
              schema_fields = self.schema_fields
  
          source_uris = ['gs://{}/{}'.format(self.bucket, source_object)
                         for source_object in self.source_objects]
          conn = bq_hook.get_conn()
          cursor = conn.cursor()
  
          if self.external_table:
              cursor.create_external_table(
                  external_project_dataset_table=self.destination_project_dataset_table,
                  schema_fields=schema_fields,
                  source_uris=source_uris,
                  source_format=self.source_format,
                  compression=self.compression,
                  skip_leading_rows=self.skip_leading_rows,
                  field_delimiter=self.field_delimiter,
                  max_bad_records=self.max_bad_records,
                  quote_character=self.quote_character,
                  ignore_unknown_values=self.ignore_unknown_values,
                  allow_quoted_newlines=self.allow_quoted_newlines,
                  allow_jagged_rows=self.allow_jagged_rows,
                  src_fmt_configs=self.src_fmt_configs
              )
          else:
              cursor.run_load(
                  destination_project_dataset_table=self.destination_project_dataset_table,
                  schema_fields=schema_fields,
                  source_uris=source_uris,
                  source_format=self.source_format,
                  autodetect=self.autodetect,
                  create_disposition=self.create_disposition,
                  skip_leading_rows=self.skip_leading_rows,
                  write_disposition=self.write_disposition,
                  field_delimiter=self.field_delimiter,
                  max_bad_records=self.max_bad_records,
                  quote_character=self.quote_character,
                  ignore_unknown_values=self.ignore_unknown_values,
                  allow_quoted_newlines=self.allow_quoted_newlines,
                  allow_jagged_rows=self.allow_jagged_rows,
                  schema_update_options=self.schema_update_options,
                  src_fmt_configs=self.src_fmt_configs,
                  time_partitioning=self.time_partitioning,
                  cluster_fields=self.cluster_fields)
  
          if self.max_id_key:
              cursor.execute('SELECT MAX({}) FROM {}'.format(
                  self.max_id_key,
                  self.destination_project_dataset_table))
              row = cursor.fetchone()
              max_id = row[0] if row[0] else 0
              self.log.info(
                  'Loaded BQ data with max %s.%s=%s',
                  self.destination_project_dataset_table, self.max_id_key, max_id
              )
              return max_id
  