text: #
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  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # 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
  #
  #   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.
  import contextlib
  import os
  import re
  import socket
  import subprocess
  import time
  from collections import OrderedDict
  from tempfile import NamedTemporaryFile, TemporaryDirectory
  from typing import Any, Dict, List, Optional, Union
  
  import pandas
  import unicodecsv as csv
  
  from airflow.configuration import conf
  from airflow.exceptions import AirflowException
  from airflow.hooks.base import BaseHook
  from airflow.hooks.dbapi import DbApiHook
  from airflow.security import utils
  from airflow.utils.helpers import as_flattened_list
  from airflow.utils.operator_helpers import AIRFLOW_VAR_NAME_FORMAT_MAPPING
  
  HIVE_QUEUE_PRIORITIES = ['VERY_HIGH', 'HIGH', 'NORMAL', 'LOW', 'VERY_LOW']
  
  
  def get_context_from_env_var() -> Dict[Any, Any]:
      """
      Extract context from env variable, e.g. dag_id, task_id and execution_date,
      so that they can be used inside BashOperator and PythonOperator.
  
      :return: The context of interest.
      """
      return {
          format_map['default']: os.environ.get(format_map['env_var_format'], '')
          for format_map in AIRFLOW_VAR_NAME_FORMAT_MAPPING.values()
      }
  
  
  class HiveCliHook(BaseHook):
      """Simple wrapper around the hive CLI.
  
      It also supports the ``beeline``
      a lighter CLI that runs JDBC and is replacing the heavier
      traditional CLI. To enable ``beeline``, set the use_beeline param in the
      extra field of your connection as in ``{ "use_beeline": true }``
  
      Note that you can also set default hive CLI parameters using the
      ``hive_cli_params`` to be used in your connection as in
      ``{"hive_cli_params": "-hiveconf mapred.job.tracker=some.jobtracker:444"}``
      Parameters passed here can be overridden by run_cli's hive_conf param
  
      The extra connection parameter ``auth`` gets passed as in the ``jdbc``
      connection string as is.
  
      :param hive_cli_conn_id: Reference to the
          :ref:`Hive CLI connection id <howto/connection:hive_cli>`.
      :type hive_cli_conn_id: str
      :param mapred_queue: queue used by the Hadoop Scheduler (Capacity or Fair)
      :type  mapred_queue: str
      :param mapred_queue_priority: priority within the job queue.
          Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW
      :type  mapred_queue_priority: str
      :param mapred_job_name: This name will appear in the jobtracker.
          This can make monitoring easier.
      :type  mapred_job_name: str
      """
  
      conn_name_attr = 'hive_cli_conn_id'
      default_conn_name = 'hive_cli_default'
      conn_type = 'hive_cli'
      hook_name = 'Hive Client Wrapper'
  
      def __init__(
          self,
          hive_cli_conn_id: str = default_conn_name,
          run_as: Optional[str] = None,
          mapred_queue: Optional[str] = None,
          mapred_queue_priority: Optional[str] = None,
          mapred_job_name: Optional[str] = None,
      ) -> None:
          super().__init__()
          conn = self.get_connection(hive_cli_conn_id)
          self.hive_cli_params: str = conn.extra_dejson.get('hive_cli_params', '')
          self.use_beeline: bool = conn.extra_dejson.get('use_beeline', False)
          self.auth = conn.extra_dejson.get('auth', 'noSasl')
          self.conn = conn
          self.run_as = run_as
          self.sub_process: Any = None
  
          if mapred_queue_priority:
              mapred_queue_priority = mapred_queue_priority.upper()
              if mapred_queue_priority not in HIVE_QUEUE_PRIORITIES:
                  raise AirflowException(
                      f"Invalid Mapred Queue Priority. Valid values are: {', '.join(HIVE_QUEUE_PRIORITIES)}"
                  )
  
          self.mapred_queue = mapred_queue or conf.get('hive', 'default_hive_mapred_queue')
          self.mapred_queue_priority = mapred_queue_priority
          self.mapred_job_name = mapred_job_name
  
      def _get_proxy_user(self) -> str:
          """This function set the proper proxy_user value in case the user overwrite the default."""
          conn = self.conn
  
          proxy_user_value: str = conn.extra_dejson.get('proxy_user', "")
          if proxy_user_value == "login" and conn.login:
              return f"hive.server2.proxy.user={conn.login}"
          if proxy_user_value == "owner" and self.run_as:
              return f"hive.server2.proxy.user={self.run_as}"
          if proxy_user_value != "":  # There is a custom proxy user
              return f"hive.server2.proxy.user={proxy_user_value}"
          return proxy_user_value  # The default proxy user (undefined)
  
      def _prepare_cli_cmd(self) -> List[Any]:
          """This function creates the command list from available information"""
          conn = self.conn
          hive_bin = 'hive'
          cmd_extra = []
  
          if self.use_beeline:
              hive_bin = 'beeline'
              jdbc_url = f"jdbc:hive2://{conn.host}:{conn.port}/{conn.schema}"
              if conf.get('core', 'security') == 'kerberos':
                  template = conn.extra_dejson.get('principal', "hive/_HOST@EXAMPLE.COM")
                  if "_HOST" in template:
                      template = utils.replace_hostname_pattern(utils.get_components(template))
  
                  proxy_user = self._get_proxy_user()
  
                  jdbc_url += f";principal={template};{proxy_user}"
              elif self.auth:
                  jdbc_url += ";auth=" + self.auth
  
              jdbc_url = f'"{jdbc_url}"'
  
              cmd_extra += ['-u', jdbc_url]
              if conn.login:
                  cmd_extra += ['-n', conn.login]
              if conn.password:
                  cmd_extra += ['-p', conn.password]
  
          hive_params_list = self.hive_cli_params.split()
  
          return [hive_bin] + cmd_extra + hive_params_list
  
      @staticmethod
      def _prepare_hiveconf(d: Dict[Any, Any]) -> List[Any]:
          """
          This function prepares a list of hiveconf params
          from a dictionary of key value pairs.
  
          :param d:
          :type d: dict
  
          >>> hh = HiveCliHook()
          >>> hive_conf = {"hive.exec.dynamic.partition": "true",
          ... "hive.exec.dynamic.partition.mode": "nonstrict"}
          >>> hh._prepare_hiveconf(hive_conf)
          ["-hiveconf", "hive.exec.dynamic.partition=true",\
          "-hiveconf", "hive.exec.dynamic.partition.mode=nonstrict"]
          """
          if not d:
              return []
          return as_flattened_list(zip(["-hiveconf"] * len(d), [f"{k}={v}" for k, v in d.items()]))
  
      def run_cli(
          self,
          hql: Union[str, str],
          schema: Optional[str] = None,
          verbose: bool = True,
          hive_conf: Optional[Dict[Any, Any]] = None,
      ) -> Any:
          """
          Run an hql statement using the hive cli. If hive_conf is specified
          it should be a dict and the entries will be set as key/value pairs
          in HiveConf
  
  
          :param hive_conf: if specified these key value pairs will be passed
              to hive as ``-hiveconf "key"="value"``. Note that they will be
              passed after the ``hive_cli_params`` and thus will override
              whatever values are specified in the database.
          :type hive_conf: dict
  
          >>> hh = HiveCliHook()
          >>> result = hh.run_cli("USE airflow;")
          >>> ("OK" in result)
          True
          """
          conn = self.conn
          schema = schema or conn.schema
          if schema:
              hql = f"USE {schema};\n{hql}"
  
          with TemporaryDirectory(prefix='airflow_hiveop_') as tmp_dir:
              with NamedTemporaryFile(dir=tmp_dir) as f:
                  hql += '\n'
                  f.write(hql.encode('UTF-8'))
                  f.flush()
                  hive_cmd = self._prepare_cli_cmd()
                  env_context = get_context_from_env_var()
                  # Only extend the hive_conf if it is defined.
                  if hive_conf:
                      env_context.update(hive_conf)
                  hive_conf_params = self._prepare_hiveconf(env_context)
                  if self.mapred_queue:
                      hive_conf_params.extend(
                          [
                              '-hiveconf',
                              f'mapreduce.job.queuename={self.mapred_queue}',
                              '-hiveconf',
                              f'mapred.job.queue.name={self.mapred_queue}',
                              '-hiveconf',
                              f'tez.queue.name={self.mapred_queue}',
                          ]
                      )
  
                  if self.mapred_queue_priority:
                      hive_conf_params.extend(
                          ['-hiveconf', f'mapreduce.job.priority={self.mapred_queue_priority}']
                      )
  
                  if self.mapred_job_name:
                      hive_conf_params.extend(['-hiveconf', f'mapred.job.name={self.mapred_job_name}'])
  
                  hive_cmd.extend(hive_conf_params)
                  hive_cmd.extend(['-f', f.name])
  
                  if verbose:
                      self.log.info("%s", " ".join(hive_cmd))
                  sub_process: Any = subprocess.Popen(
                      hive_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=tmp_dir, close_fds=True
                  )
                  self.sub_process = sub_process
                  stdout = ''
                  while True:
                      line = sub_process.stdout.readline()
                      if not line:
                          break
                      stdout += line.decode('UTF-8')
                      if verbose:
                          self.log.info(line.decode('UTF-8').strip())
                  sub_process.wait()
  
                  if sub_process.returncode:
                      raise AirflowException(stdout)
  
                  return stdout
  
      def test_hql(self, hql: Union[str, str]) -> None:
          """Test an hql statement using the hive cli and EXPLAIN"""
          create, insert, other = [], [], []
          for query in hql.split(';'):  # naive
              query_original = query
              query = query.lower().strip()
  
              if query.startswith('create table'):
                  create.append(query_original)
              elif query.startswith(('set ', 'add jar ', 'create temporary function')):
                  other.append(query_original)
              elif query.startswith('insert'):
                  insert.append(query_original)
          other_ = ';'.join(other)
          for query_set in [create, insert]:
              for query in query_set:
  
                  query_preview = ' '.join(query.split())[:50]
                  self.log.info("Testing HQL [%s (...)]", query_preview)
                  if query_set == insert:
                      query = other_ + '; explain ' + query
                  else:
                      query = 'explain ' + query
                  try:
                      self.run_cli(query, verbose=False)
                  except AirflowException as e:
                      message = e.args[0].split('\n')[-2]
                      self.log.info(message)
                      error_loc = re.search(r'(\d+):(\d+)', message)
                      if error_loc and error_loc.group(1).isdigit():
                          lst = int(error_loc.group(1))
                          begin = max(lst - 2, 0)
                          end = min(lst + 3, len(query.split('\n')))
                          context = '\n'.join(query.split('\n')[begin:end])
                          self.log.info("Context :\n %s", context)
                  else:
                      self.log.info("SUCCESS")
  
      def load_df(
          self,
          df: pandas.DataFrame,
          table: str,
          field_dict: Optional[Dict[Any, Any]] = None,
          delimiter: str = ',',
          encoding: str = 'utf8',
          pandas_kwargs: Any = None,
          **kwargs: Any,
      ) -> None:
          """
          Loads a pandas DataFrame into hive.
  
          Hive data types will be inferred if not passed but column names will
          not be sanitized.
  
          :param df: DataFrame to load into a Hive table
          :type df: pandas.DataFrame
          :param table: target Hive table, use dot notation to target a
              specific database
          :type table: str
          :param field_dict: mapping from column name to hive data type.
              Note that it must be OrderedDict so as to keep columns' order.
          :type field_dict: collections.OrderedDict
          :param delimiter: field delimiter in the file
          :type delimiter: str
          :param encoding: str encoding to use when writing DataFrame to file
          :type encoding: str
          :param pandas_kwargs: passed to DataFrame.to_csv
          :type pandas_kwargs: dict
          :param kwargs: passed to self.load_file
          """
  
          def _infer_field_types_from_df(df: pandas.DataFrame) -> Dict[Any, Any]:
              dtype_kind_hive_type = {
                  'b': 'BOOLEAN',  # boolean
                  'i': 'BIGINT',  # signed integer
                  'u': 'BIGINT',  # unsigned integer
                  'f': 'DOUBLE',  # floating-point
                  'c': 'STRING',  # complex floating-point
                  'M': 'TIMESTAMP',  # datetime
                  'O': 'STRING',  # object
                  'S': 'STRING',  # (byte-)string
                  'U': 'STRING',  # Unicode
                  'V': 'STRING',  # void
              }
  
              order_type = OrderedDict()
              for col, dtype in df.dtypes.iteritems():
                  order_type[col] = dtype_kind_hive_type[dtype.kind]
              return order_type
  
          if pandas_kwargs is None:
              pandas_kwargs = {}
  
          with TemporaryDirectory(prefix='airflow_hiveop_') as tmp_dir:
              with NamedTemporaryFile(dir=tmp_dir, mode="w") as f:
                  if field_dict is None:
                      field_dict = _infer_field_types_from_df(df)
  
                  df.to_csv(
                      path_or_buf=f,
                      sep=delimiter,
                      header=False,
                      index=False,
                      encoding=encoding,
                      date_format="%Y-%m-%d %H:%M:%S",
                      **pandas_kwargs,
                  )
                  f.flush()
  
                  return self.load_file(
                      filepath=f.name, table=table, delimiter=delimiter, field_dict=field_dict, **kwargs
                  )
  
      def load_file(
          self,
          filepath: str,
          table: str,
          delimiter: str = ",",
          field_dict: Optional[Dict[Any, Any]] = None,
          create: bool = True,
          overwrite: bool = True,
          partition: Optional[Dict[str, Any]] = None,
          recreate: bool = False,
          tblproperties: Optional[Dict[str, Any]] = None,
      ) -> None:
          """
          Loads a local file into Hive
  
          Note that the table generated in Hive uses ``STORED AS textfile``
          which isn't the most efficient serialization format. If a
          large amount of data is loaded and/or if the tables gets
          queried considerably, you may want to use this operator only to
          stage the data into a temporary table before loading it into its
          final destination using a ``HiveOperator``.
  
          :param filepath: local filepath of the file to load
          :type filepath: str
          :param table: target Hive table, use dot notation to target a
              specific database
          :type table: str
          :param delimiter: field delimiter in the file
          :type delimiter: str
          :param field_dict: A dictionary of the fields name in the file
              as keys and their Hive types as values.
              Note that it must be OrderedDict so as to keep columns' order.
          :type field_dict: collections.OrderedDict
          :param create: whether to create the table if it doesn't exist
          :type create: bool
          :param overwrite: whether to overwrite the data in table or partition
          :type overwrite: bool
          :param partition: target partition as a dict of partition columns
              and values
          :type partition: dict
          :param recreate: whether to drop and recreate the table at every
              execution
          :type recreate: bool
          :param tblproperties: TBLPROPERTIES of the hive table being created
          :type tblproperties: dict
          """
          hql = ''
          if recreate:
              hql += f"DROP TABLE IF EXISTS {table};\n"
          if create or recreate:
              if field_dict is None:
                  raise ValueError("Must provide a field dict when creating a table")
              fields = ",\n    ".join(f"`{k.strip('`')}` {v}" for k, v in field_dict.items())
              hql += f"CREATE TABLE IF NOT EXISTS {table} (\n{fields})\n"
              if partition:
                  pfields = ",\n    ".join(p + " STRING" for p in partition)
                  hql += f"PARTITIONED BY ({pfields})\n"
              hql += "ROW FORMAT DELIMITED\n"
              hql += f"FIELDS TERMINATED BY '{delimiter}'\n"
              hql += "STORED AS textfile\n"
              if tblproperties is not None:
                  tprops = ", ".join(f"'{k}'='{v}'" for k, v in tblproperties.items())
                  hql += f"TBLPROPERTIES({tprops})\n"
              hql += ";"
              self.log.info(hql)
              self.run_cli(hql)
          hql = f"LOAD DATA LOCAL INPATH '{filepath}' "
          if overwrite:
              hql += "OVERWRITE "
          hql += f"INTO TABLE {table} "
          if partition:
              pvals = ", ".join(f"{k}='{v}'" for k, v in partition.items())
              hql += f"PARTITION ({pvals})"
  
          # As a workaround for HIVE-10541, add a newline character
          # at the end of hql (AIRFLOW-2412).
          hql += ';\n'
  
          self.log.info(hql)
          self.run_cli(hql)
  
      def kill(self) -> None:
          """Kill Hive cli command"""
          if hasattr(self, 'sub_process'):
              if self.sub_process.poll() is None:
                  print("Killing the Hive job")
                  self.sub_process.terminate()
                  time.sleep(60)
                  self.sub_process.kill()
  
  
  class HiveMetastoreHook(BaseHook):
      """
      Wrapper to interact with the Hive Metastore
  
      :param metastore_conn_id: reference to the
          :ref: `metastore thrift service connection id <howto/connection:hive_metastore>`.
      :type metastore_conn_id: str
      """
  
      # java short max val
      MAX_PART_COUNT = 32767
  
      conn_name_attr = 'metastore_conn_id'
      default_conn_name = 'metastore_default'
      conn_type = 'hive_metastore'
      hook_name = 'Hive Metastore Thrift'
  
      def __init__(self, metastore_conn_id: str = default_conn_name) -> None:
          super().__init__()
          self.conn_id = metastore_conn_id
          self.metastore = self.get_metastore_client()
  
      def __getstate__(self) -> Dict[str, Any]:
          # This is for pickling to work despite the thrift hive client not
          # being picklable
          state = dict(self.__dict__)
          del state['metastore']
          return state
  
      def __setstate__(self, d: Dict[str, Any]) -> None:
          self.__dict__.update(d)
          self.__dict__['metastore'] = self.get_metastore_client()
  
      def get_metastore_client(self) -> Any:
          """Returns a Hive thrift client."""
          import hmsclient
          from thrift.protocol import TBinaryProtocol
          from thrift.transport import TSocket, TTransport
  
          conn = self._find_valid_server()
  
          if not conn:
              raise AirflowException("Failed to locate the valid server.")
  
          auth_mechanism = conn.extra_dejson.get('authMechanism', 'NOSASL')
  
          if conf.get('core', 'security') == 'kerberos':
              auth_mechanism = conn.extra_dejson.get('authMechanism', 'GSSAPI')
              kerberos_service_name = conn.extra_dejson.get('kerberos_service_name', 'hive')
  
          conn_socket = TSocket.TSocket(conn.host, conn.port)
  
          if conf.get('core', 'security') == 'kerberos' and auth_mechanism == 'GSSAPI':
              try:
                  import saslwrapper as sasl
              except ImportError:
                  import sasl
  
              def sasl_factory() -> sasl.Client:
                  sasl_client = sasl.Client()
                  sasl_client.setAttr("host", conn.host)
                  sasl_client.setAttr("service", kerberos_service_name)
                  sasl_client.init()
                  return sasl_client
  
              from thrift_sasl import TSaslClientTransport
  
              transport = TSaslClientTransport(sasl_factory, "GSSAPI", conn_socket)
          else:
              transport = TTransport.TBufferedTransport(conn_socket)
  
          protocol = TBinaryProtocol.TBinaryProtocol(transport)
  
          return hmsclient.HMSClient(iprot=protocol)
  
      def _find_valid_server(self) -> Any:
          conn = self.get_connection(self.conn_id)
          host_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
          self.log.info("Trying to connect to %s:%s", conn.host, conn.port)
          if host_socket.connect_ex((conn.host, conn.port)) == 0:
              self.log.info("Connected to %s:%s", conn.host, conn.port)
              host_socket.close()
              return conn
          else:
              self.log.error("Could not connect to %s:%s", conn.host, conn.port)
          return None
  
      def get_conn(self) -> Any:
          return self.metastore
  
      def check_for_partition(self, schema: str, table: str, partition: str) -> bool:
          """
          Checks whether a partition exists
  
          :param schema: Name of hive schema (database) @table belongs to
          :type schema: str
          :param table: Name of hive table @partition belongs to
          :type schema: str
          :partition: Expression that matches the partitions to check for
              (eg `a = 'b' AND c = 'd'`)
          :type schema: str
          :rtype: bool
  
          >>> hh = HiveMetastoreHook()
          >>> t = 'static_babynames_partitioned'
          >>> hh.check_for_partition('airflow', t, "ds='2015-01-01'")
          True
          """
          with self.metastore as client:
              partitions = client.get_partitions_by_filter(schema, table, partition, 1)
  
          return bool(partitions)
  
      def check_for_named_partition(self, schema: str, table: str, partition_name: str) -> Any:
          """
          Checks whether a partition with a given name exists
  
          :param schema: Name of hive schema (database) @table belongs to
          :type schema: str
          :param table: Name of hive table @partition belongs to
          :type table: str
          :partition: Name of the partitions to check for (eg `a=b/c=d`)
          :type table: str
          :rtype: bool
  
          >>> hh = HiveMetastoreHook()
          >>> t = 'static_babynames_partitioned'
          >>> hh.check_for_named_partition('airflow', t, "ds=2015-01-01")
          True
          >>> hh.check_for_named_partition('airflow', t, "ds=xxx")
          False
          """
          with self.metastore as client:
              return client.check_for_named_partition(schema, table, partition_name)
  
      def get_table(self, table_name: str, db: str = 'default') -> Any:
          """Get a metastore table object
  
          >>> hh = HiveMetastoreHook()
          >>> t = hh.get_table(db='airflow', table_name='static_babynames')
          >>> t.tableName
          'static_babynames'
          >>> [col.name for col in t.sd.cols]
          ['state', 'year', 'name', 'gender', 'num']
          """
          if db == 'default' and '.' in table_name:
              db, table_name = table_name.split('.')[:2]
          with self.metastore as client:
              return client.get_table(dbname=db, tbl_name=table_name)
  
      def get_tables(self, db: str, pattern: str = '*') -> Any:
          """Get a metastore table object"""
          with self.metastore as client:
              tables = client.get_tables(db_name=db, pattern=pattern)
              return client.get_table_objects_by_name(db, tables)
  
      def get_databases(self, pattern: str = '*') -> Any:
          """Get a metastore table object"""
          with self.metastore as client:
              return client.get_databases(pattern)
  
      def get_partitions(
          self, schema: str, table_name: str, partition_filter: Optional[str] = None
      ) -> List[Any]:
          """
          Returns a list of all partitions in a table. Works only
          for tables with less than 32767 (java short max val).
          For subpartitioned table, the number might easily exceed this.
  
          >>> hh = HiveMetastoreHook()
          >>> t = 'static_babynames_partitioned'
          >>> parts = hh.get_partitions(schema='airflow', table_name=t)
          >>> len(parts)
          1
          >>> parts
          [{'ds': '2015-01-01'}]
          """
          with self.metastore as client:
              table = client.get_table(dbname=schema, tbl_name=table_name)
              if len(table.partitionKeys) == 0:
                  raise AirflowException("The table isn't partitioned")
              else:
                  if partition_filter:
                      parts = client.get_partitions_by_filter(
                          db_name=schema,
                          tbl_name=table_name,
                          filter=partition_filter,
                          max_parts=HiveMetastoreHook.MAX_PART_COUNT,
                      )
                  else:
                      parts = client.get_partitions(
                          db_name=schema, tbl_name=table_name, max_parts=HiveMetastoreHook.MAX_PART_COUNT
                      )
  
                  pnames = [p.name for p in table.partitionKeys]
                  return [dict(zip(pnames, p.values)) for p in parts]
  
      @staticmethod
      def _get_max_partition_from_part_specs(
          part_specs: List[Any], partition_key: Optional[str], filter_map: Optional[Dict[str, Any]]
      ) -> Any:
          """
          Helper method to get max partition of partitions with partition_key
          from part specs. key:value pair in filter_map will be used to
          filter out partitions.
  
          :param part_specs: list of partition specs.
          :type part_specs: list
          :param partition_key: partition key name.
          :type partition_key: str
          :param filter_map: partition_key:partition_value map used for partition filtering,
                             e.g. {'key1': 'value1', 'key2': 'value2'}.
                             Only partitions matching all partition_key:partition_value
                             pairs will be considered as candidates of max partition.
          :type filter_map: map
          :return: Max partition or None if part_specs is empty.
          :rtype: basestring
          """
          if not part_specs:
              return None
  
          # Assuming all specs have the same keys.
          if partition_key not in part_specs[0].keys():
              raise AirflowException(f"Provided partition_key {partition_key} is not in part_specs.")
          is_subset = None
          if filter_map:
              is_subset = set(filter_map.keys()).issubset(set(part_specs[0].keys()))
          if filter_map and not is_subset:
              raise AirflowException(
                  f"Keys in provided filter_map {', '.join(filter_map.keys())} "
                  f"are not subset of part_spec keys: {', '.join(part_specs[0].keys())}"
              )
  
          candidates = [
              p_dict[partition_key]
              for p_dict in part_specs
              if filter_map is None or all(item in p_dict.items() for item in filter_map.items())
          ]
  
          if not candidates:
              return None
          else:
              return max(candidates)
  
      def max_partition(
          self,
          schema: str,
          table_name: str,
          field: Optional[str] = None,
          filter_map: Optional[Dict[Any, Any]] = None,
      ) -> Any:
          """
          Returns the maximum value for all partitions with given field in a table.
          If only one partition key exist in the table, the key will be used as field.
          filter_map should be a partition_key:partition_value map and will be used to
          filter out partitions.
  
          :param schema: schema name.
          :type schema: str
          :param table_name: table name.
          :type table_name: str
          :param field: partition key to get max partition from.
          :type field: str
          :param filter_map: partition_key:partition_value map used for partition filtering.
          :type filter_map: map
  
          >>> hh = HiveMetastoreHook()
          >>> filter_map = {'ds': '2015-01-01'}
          >>> t = 'static_babynames_partitioned'
          >>> hh.max_partition(schema='airflow',\
          ... table_name=t, field='ds', filter_map=filter_map)
          '2015-01-01'
          """
          with self.metastore as client:
              table = client.get_table(dbname=schema, tbl_name=table_name)
              key_name_set = {key.name for key in table.partitionKeys}
              if len(table.partitionKeys) == 1:
                  field = table.partitionKeys[0].name
              elif not field:
                  raise AirflowException("Please specify the field you want the max value for.")
              elif field not in key_name_set:
                  raise AirflowException("Provided field is not a partition key.")
  
              if filter_map and not set(filter_map.keys()).issubset(key_name_set):
                  raise AirflowException("Provided filter_map contains keys that are not partition key.")
  
              part_names = client.get_partition_names(
                  schema, table_name, max_parts=HiveMetastoreHook.MAX_PART_COUNT
              )
              part_specs = [client.partition_name_to_spec(part_name) for part_name in part_names]
  
          return HiveMetastoreHook._get_max_partition_from_part_specs(part_specs, field, filter_map)
  
      def table_exists(self, table_name: str, db: str = 'default') -> bool:
          """
          Check if table exists
  
          >>> hh = HiveMetastoreHook()
          >>> hh.table_exists(db='airflow', table_name='static_babynames')
          True
          >>> hh.table_exists(db='airflow', table_name='does_not_exist')
          False
          """
          try:
              self.get_table(table_name, db)
              return True
          except Exception:
              return False
  
      def drop_partitions(self, table_name, part_vals, delete_data=False, db='default'):
          """
          Drop partitions from the given table matching the part_vals input
  
          :param table_name: table name.
          :type table_name: str
          :param part_vals: list of partition specs.
          :type part_vals: list
          :param delete_data: Setting to control if underlying data have to deleted
                              in addition to dropping partitions.
          :type delete_data: bool
          :param db: Name of hive schema (database) @table belongs to
          :type db: str
  
          >>> hh = HiveMetastoreHook()
          >>> hh.drop_partitions(db='airflow', table_name='static_babynames',
          part_vals="['2020-05-01']")
          True
          """
          if self.table_exists(table_name, db):
              with self.metastore as client:
                  self.log.info(
                      "Dropping partition of table %s.%s matching the spec: %s", db, table_name, part_vals
                  )
                  return client.drop_partition(db, table_name, part_vals, delete_data)
          else:
              self.log.info("Table %s.%s does not exist!", db, table_name)
              return False
  
  
  class HiveServer2Hook(DbApiHook):
      """
      Wrapper around the pyhive library
  
      Notes:
      * the default authMechanism is PLAIN, to override it you
      can specify it in the ``extra`` of your connection in the UI
      * the default for run_set_variable_statements is true, if you
      are using impala you may need to set it to false in the
      ``extra`` of your connection in the UI
  
      :param hiveserver2_conn_id: Reference to the
          :ref: `Hive Server2 thrift service connection id <howto/connection:hiveserver2>`.
      :type hiveserver2_conn_id: str
      :param schema: Hive database name.
      :type schema: Optional[str]
      """
  
      conn_name_attr = 'hiveserver2_conn_id'
      default_conn_name = 'hiveserver2_default'
      conn_type = 'hiveserver2'
      hook_name = 'Hive Server 2 Thrift'
      supports_autocommit = False
  
      def get_conn(self, schema: Optional[str] = None) -> Any:
          """Returns a Hive connection object."""
          username: Optional[str] = None
          password: Optional[str] = None
  
          db = self.get_connection(self.hiveserver2_conn_id)  # type: ignore
  
          auth_mechanism = db.extra_dejson.get('authMechanism', 'NONE')
          if auth_mechanism == 'NONE' and db.login is None:
              # we need to give a username
              username = 'airflow'
          kerberos_service_name = None
          if conf.get('core', 'security') == 'kerberos':
              auth_mechanism = db.extra_dejson.get('authMechanism', 'KERBEROS')
              kerberos_service_name = db.extra_dejson.get('kerberos_service_name', 'hive')
  
          # pyhive uses GSSAPI instead of KERBEROS as a auth_mechanism identifier
          if auth_mechanism == 'GSSAPI':
              self.log.warning(
                  "Detected deprecated 'GSSAPI' for authMechanism for %s. Please use 'KERBEROS' instead",
                  self.hiveserver2_conn_id,  # type: ignore
              )
              auth_mechanism = 'KERBEROS'
  
          # Password should be set if and only if in LDAP or CUSTOM mode
          if auth_mechanism in ('LDAP', 'CUSTOM'):
              password = db.password
  
          from pyhive.hive import connect
  
          return connect(
              host=db.host,
              port=db.port,
              auth=auth_mechanism,
              kerberos_service_name=kerberos_service_name,
              username=db.login or username,
              password=password,
              database=schema or db.schema or 'default',
          )
  
      def _get_results(
          self,
          hql: Union[str, str, List[str]],
          schema: str = 'default',
          fetch_size: Optional[int] = None,
          hive_conf: Optional[Dict[Any, Any]] = None,
      ) -> Any:
          from pyhive.exc import ProgrammingError
  
          if isinstance(hql, str):
              hql = [hql]
          previous_description = None
          with contextlib.closing(self.get_conn(schema)) as conn, contextlib.closing(conn.cursor()) as cur:
  
              cur.arraysize = fetch_size or 1000
  
              # not all query services (e.g. impala AIRFLOW-4434) support the set command
  
              db = self.get_connection(self.hiveserver2_conn_id)  # type: ignore
  
              if db.extra_dejson.get('run_set_variable_statements', True):
                  env_context = get_context_from_env_var()
                  if hive_conf:
                      env_context.update(hive_conf)
                  for k, v in env_context.items():
                      cur.execute(f"set {k}={v}")
  
              for statement in hql:
                  cur.execute(statement)
                  # we only get results of statements that returns
                  lowered_statement = statement.lower().strip()
                  if (
                      lowered_statement.startswith('select')
                      or lowered_statement.startswith('with')
                      or lowered_statement.startswith('show')
                      or (lowered_statement.startswith('set') and '=' not in lowered_statement)
                  ):
                      description = cur.description
                      if previous_description and previous_description != description:
                          message = f'''The statements are producing different descriptions:
                                       Current: {repr(description)}
                                       Previous: {repr(previous_description)}'''
                          raise ValueError(message)
                      elif not previous_description:
                          previous_description = description
                          yield description
                      try:
                          # DB API 2 raises when no results are returned
                          # we're silencing here as some statements in the list
                          # may be `SET` or DDL
                          yield from cur
                      except ProgrammingError:
                          self.log.debug("get_results returned no records")
  
      def get_results(
          self,
          hql: Union[str, str],
          schema: str = 'default',
          fetch_size: Optional[int] = None,
          hive_conf: Optional[Dict[Any, Any]] = None,
      ) -> Dict[str, Any]:
          """
          Get results of the provided hql in target schema.
  
          :param hql: hql to be executed.
          :type hql: str or list
          :param schema: target schema, default to 'default'.
          :type schema: str
          :param fetch_size: max size of result to fetch.
          :type fetch_size: int
          :param hive_conf: hive_conf to execute alone with the hql.
          :type hive_conf: dict
          :return: results of hql execution, dict with data (list of results) and header
          :rtype: dict
          """
          results_iter = self._get_results(hql, schema, fetch_size=fetch_size, hive_conf=hive_conf)
          header = next(results_iter)
          results = {'data': list(results_iter), 'header': header}
          return results
  
      def to_csv(
          self,
          hql: Union[str, str],
          csv_filepath: str,
          schema: str = 'default',
          delimiter: str = ',',
          lineterminator: str = '\r\n',
          output_header: bool = True,
          fetch_size: int = 1000,
          hive_conf: Optional[Dict[Any, Any]] = None,
      ) -> None:
          """
          Execute hql in target schema and write results to a csv file.
  
          :param hql: hql to be executed.
          :type hql: str or list
          :param csv_filepath: filepath of csv to write results into.
          :type csv_filepath: str
          :param schema: target schema, default to 'default'.
          :type schema: str
          :param delimiter: delimiter of the csv file, default to ','.
          :type delimiter: str
          :param lineterminator: lineterminator of the csv file.
          :type lineterminator: str
          :param output_header: header of the csv file, default to True.
          :type output_header: bool
          :param fetch_size: number of result rows to write into the csv file, default to 1000.
          :type fetch_size: int
          :param hive_conf: hive_conf to execute alone with the hql.
          :type hive_conf: dict
  
          """
          results_iter = self._get_results(hql, schema, fetch_size=fetch_size, hive_conf=hive_conf)
          header = next(results_iter)
          message = None
  
          i = 0
          with open(csv_filepath, 'wb') as file:
              writer = csv.writer(file, delimiter=delimiter, lineterminator=lineterminator, encoding='utf-8')
              try:
                  if output_header:
                      self.log.debug('Cursor description is %s', header)
                      writer.writerow([c[0] for c in header])
  
                  for i, row in enumerate(results_iter, 1):
                      writer.writerow(row)
                      if i % fetch_size == 0:
                          self.log.info("Written %s rows so far.", i)
              except ValueError as exception:
                  message = str(exception)
  
          if message:
              # need to clean up the file first
              os.remove(csv_filepath)
              raise ValueError(message)
  
          self.log.info("Done. Loaded a total of %s rows.", i)
  
      def get_records(
          self, hql: Union[str, str], schema: str = 'default', hive_conf: Optional[Dict[Any, Any]] = None
      ) -> Any:
          """
          Get a set of records from a Hive query.
  
          :param hql: hql to be executed.
          :type hql: str or list
          :param schema: target schema, default to 'default'.
          :type schema: str
          :param hive_conf: hive_conf to execute alone with the hql.
          :type hive_conf: dict
          :return: result of hive execution
          :rtype: list
  
          >>> hh = HiveServer2Hook()
          >>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
          >>> len(hh.get_records(sql))
          100
          """
          return self.get_results(hql, schema=schema, hive_conf=hive_conf)['data']
  
      def get_pandas_df(  # type: ignore
          self,
          hql: Union[str, str],
          schema: str = 'default',
          hive_conf: Optional[Dict[Any, Any]] = None,
          **kwargs,
      ) -> pandas.DataFrame:
          """
          Get a pandas dataframe from a Hive query
  
          :param hql: hql to be executed.
          :type hql: str or list
          :param schema: target schema, default to 'default'.
          :type schema: str
          :param hive_conf: hive_conf to execute alone with the hql.
          :type hive_conf: dict
          :param kwargs: (optional) passed into pandas.DataFrame constructor
          :type kwargs: dict
          :return: result of hive execution
          :rtype: DataFrame
  
          >>> hh = HiveServer2Hook()
          >>> sql = "SELECT * FROM airflow.static_babynames LIMIT 100"
          >>> df = hh.get_pandas_df(sql)
          >>> len(df.index)
          100
  
          :return: pandas.DateFrame
          """
          res = self.get_results(hql, schema=schema, hive_conf=hive_conf)
          df = pandas.DataFrame(res['data'], columns=[c[0] for c in res['header']], **kwargs)
          return df
  