class_docstr: Runs Hive job in Data Proc cluster.
  
  :param query: Hive query.
  :param query_file_uri: URI of the script that contains Hive queries. Can be placed in HDFS or S3.
  :param properties: A mapping of property names to values, used to configure Hive.
  :param script_variables: Mapping of query variable names to values.
  :param continue_on_failure: Whether to continue executing queries if a query fails.
  :param name: Name of the job. Used for labeling.
  :param cluster_id: ID of the cluster to run job in.
                     Will try to take the ID from Dataproc Hook object if it's specified. (templated)
  :param connection_id: ID of the Yandex.Cloud Airflow connection.
code: def __init__(
      self,
      *,
      query: str | None = None,
      query_file_uri: str | None = None,
      script_variables: dict[str, str] | None = None,
      continue_on_failure: bool = False,
      properties: dict[str, str] | None = None,
      name: str = "Hive job",
      cluster_id: str | None = None,
      connection_id: str | None = None,
      **kwargs,
  ) -> None:
      super().__init__(
          yandex_conn_id=connection_id, cluster_id=cluster_id, **kwargs
      )
      self.query = query
      self.query_file_uri = query_file_uri
      self.script_variables = script_variables
      self.continue_on_failure = continue_on_failure
      self.properties = properties
      self.name = name
  
imports: from __future__ import annotations
  import warnings
  from dataclasses import dataclass
  from typing import TYPE_CHECKING, Iterable, Sequence
  from airflow.models import BaseOperator
  from airflow.providers.yandex.hooks.yandexcloud_dataproc import DataprocHook
label_desc: Use image node_id 1 to create a new DataprocCreateHiveJobOperator object from inherited base classes: DataprocBaseOperator with example: obj = DataprocCreateHiveJobOperator()