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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: 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 hql: an hql (hive query language) statement to run with hive cli
        :type hql: str
        :param schema: Name of hive schema (database) to use
        :type schema: str
        :param verbose: Provides additional logging. Defaults to True.
        :type verbose: bool
        :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: 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 = self.get_connection(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

        host = self._find_valid_host()
        conn = self.conn

        if not host:
            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(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", 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_host(self) -> Any:
        conn = self.conn
        hosts = conn.host.split(',')
        for host in hosts:
            host_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
            self.log.info("Trying to connect to %s:%s", host, conn.port)
            if host_socket.connect_ex((host, conn.port)) == 0:
                self.log.info("Connected to %s:%s", host, conn.port)
                host_socket.close()
                return host
            else:
                self.log.error("Could not connect to %s:%s", 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 table: str
        :param partition: Expression that matches the partitions to check for
            (eg `a = 'b' AND c = 'd'`)
        :type partition: 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
        :param partition_name: Name of the partitions to check for (eg `a=b/c=d`)
        :type partition_name: 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, 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: 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: 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: 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: 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
