text: import copy
  import datetime
  import logging
  import random
  import string
  import traceback
  import warnings
  from pathlib import Path
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
  
  from great_expectations._version import get_versions  # isort:skip
  
  __version__ = get_versions()["version"]  # isort:skip
  
  from great_expectations.execution_engine.sqlalchemy_data_splitter import (
      SqlAlchemyDataSplitter,
  )
  
  del get_versions  # isort:skip
  
  
  from great_expectations.core import IDDict
  from great_expectations.core.batch import BatchMarkers, BatchSpec
  from great_expectations.core.batch_spec import (
      RuntimeQueryBatchSpec,
      SqlAlchemyDatasourceBatchSpec,
  )
  from great_expectations.core.util import convert_to_json_serializable
  from great_expectations.data_context.types.base import ConcurrencyConfig
  from great_expectations.exceptions import (
      DatasourceKeyPairAuthBadPassphraseError,
      ExecutionEngineError,
      GreatExpectationsError,
      InvalidBatchSpecError,
      InvalidConfigError,
  )
  from great_expectations.exceptions import exceptions as ge_exceptions
  from great_expectations.execution_engine import ExecutionEngine
  from great_expectations.execution_engine.execution_engine import (
      MetricDomainTypes,
      SplitDomainKwargs,
  )
  from great_expectations.execution_engine.sqlalchemy_batch_data import (
      SqlAlchemyBatchData,
  )
  from great_expectations.expectations.row_conditions import (
      RowCondition,
      RowConditionParserType,
      parse_condition_to_sqlalchemy,
  )
  from great_expectations.util import (
      filter_properties_dict,
      get_sqlalchemy_selectable,
      get_sqlalchemy_url,
      import_library_module,
      import_make_url,
  )
  from great_expectations.validator.metric_configuration import MetricConfiguration
  
  logger = logging.getLogger(__name__)
  
  try:
      import sqlalchemy as sa
  
      make_url = import_make_url()
  except ImportError:
      sa = None
  
  try:
      from sqlalchemy.engine import LegacyRow
      from sqlalchemy.exc import OperationalError
      from sqlalchemy.sql import Selectable
      from sqlalchemy.sql.elements import (
          BooleanClauseList,
          Label,
          TextClause,
          quoted_name,
      )
  except ImportError:
      LegacyRow = None
      reflection = None
      DefaultDialect = None
      Selectable = None
      BooleanClauseList = None
      TextClause = None
      quoted_name = None
      OperationalError = None
      Label = None
  
  
  try:
      import psycopg2
      import sqlalchemy.dialects.postgresql.psycopg2 as sqlalchemy_psycopg2
  except (ImportError, KeyError):
      sqlalchemy_psycopg2 = None
  
  try:
      import sqlalchemy_redshift.dialect
  except ImportError:
      sqlalchemy_redshift = None
  
  try:
      import sqlalchemy_dremio.pyodbc
  
      if sa:
          sa.dialects.registry.register("dremio", "sqlalchemy_dremio.pyodbc", "dialect")
  except ImportError:
      sqlalchemy_dremio = None
  
  try:
      import snowflake.sqlalchemy.snowdialect
  
      if sa:
          # Sometimes "snowflake-sqlalchemy" fails to self-register in certain environments, so we do it explicitly.
          # (see https://stackoverflow.com/questions/53284762/nosuchmoduleerror-cant-load-plugin-sqlalchemy-dialectssnowflake)
          sa.dialects.registry.register("snowflake", "snowflake.sqlalchemy", "dialect")
  except (ImportError, KeyError, AttributeError):
      snowflake = None
  
  _BIGQUERY_MODULE_NAME = "sqlalchemy_bigquery"
  try:
      import sqlalchemy_bigquery as sqla_bigquery
  
      sa.dialects.registry.register("bigquery", _BIGQUERY_MODULE_NAME, "dialect")
      bigquery_types_tuple = None
  except ImportError:
      try:
          import pybigquery.sqlalchemy_bigquery as sqla_bigquery
  
          # deprecated-v0.14.7
          warnings.warn(
              "The pybigquery package is obsolete and its usage within Great Expectations is deprecated as of v0.14.7. "
              "As support will be removed in v0.17, please transition to sqlalchemy-bigquery",
              DeprecationWarning,
          )
          _BIGQUERY_MODULE_NAME = "pybigquery.sqlalchemy_bigquery"
          # Sometimes "pybigquery.sqlalchemy_bigquery" fails to self-register in Azure (our CI/CD pipeline) in certain cases, so we do it explicitly.
          # (see https://stackoverflow.com/questions/53284762/nosuchmoduleerror-cant-load-plugin-sqlalchemy-dialectssnowflake)
          sa.dialects.registry.register("bigquery", _BIGQUERY_MODULE_NAME, "dialect")
          try:
              getattr(sqla_bigquery, "INTEGER")
              bigquery_types_tuple = None
          except AttributeError:
              # In older versions of the pybigquery driver, types were not exported, so we use a hack
              logger.warning(
                  "Old pybigquery driver version detected. Consider upgrading to 0.4.14 or later."
              )
              from collections import namedtuple
  
              BigQueryTypes = namedtuple("BigQueryTypes", sorted(sqla_bigquery._type_map))
              bigquery_types_tuple = BigQueryTypes(**sqla_bigquery._type_map)
      except (ImportError, AttributeError):
          sqla_bigquery = None
          bigquery_types_tuple = None
          pybigquery = None
  
  try:
      import teradatasqlalchemy.dialect
      import teradatasqlalchemy.types as teradatatypes
  except ImportError:
      teradatasqlalchemy = None
  
  
  def _get_dialect_type_module(dialect):
      """Given a dialect, returns the dialect type, which is defines the engine/system that is used to communicates
      with the database/database implementation. Currently checks for RedShift/BigQuery dialects"""
      if dialect is None:
          logger.warning(
              "No sqlalchemy dialect found; relying in top-level sqlalchemy types."
          )
          return sa
      try:
          # Redshift does not (yet) export types to top level; only recognize base SA types
          if isinstance(dialect, sqlalchemy_redshift.dialect.RedshiftDialect):
              return dialect.sa
      except (TypeError, AttributeError):
          pass
  
      # Bigquery works with newer versions, but use a patch if we had to define bigquery_types_tuple
      try:
          if (
              isinstance(
                  dialect,
                  sqla_bigquery.BigQueryDialect,
              )
              and bigquery_types_tuple is not None
          ):
              return bigquery_types_tuple
      except (TypeError, AttributeError):
          pass
  
      # Teradata types module
      try:
          if (
              issubclass(
                  dialect,
                  teradatasqlalchemy.dialect.TeradataDialect,
              )
              and teradatatypes is not None
          ):
              return teradatatypes
      except (TypeError, AttributeError):
          pass
  
      return dialect
  
  
  class SqlAlchemyExecutionEngine(ExecutionEngine):
      def __init__(
          self,
          name: Optional[str] = None,
          credentials: Optional[dict] = None,
          data_context: Optional[Any] = None,
          engine=None,
          connection_string: Optional[str] = None,
          url: Optional[str] = None,
          batch_data_dict: Optional[dict] = None,
          create_temp_table: bool = True,
          concurrency: Optional[ConcurrencyConfig] = None,
          **kwargs,  # These will be passed as optional parameters to the SQLAlchemy engine, **not** the ExecutionEngine
      ):
          """Builds a SqlAlchemyExecutionEngine, using a provided connection string/url/engine/credentials to access the
          desired database. Also initializes the dialect to be used and configures usage statistics.
  
              Args:
                  name (str): \
                      The name of the SqlAlchemyExecutionEngine
                  credentials: \
                      If the Execution Engine is not provided, the credentials can be used to build the Execution
                      Engine. If the Engine is provided, it will be used instead
                  data_context (DataContext): \
                      An object representing a Great Expectations project that can be used to access Expectation
                      Suites and the Project Data itself
                  engine (Engine): \
                      A SqlAlchemy Engine used to set the SqlAlchemyExecutionEngine being configured, useful if an
                      Engine has already been configured and should be reused. Will override Credentials
                      if provided.
                  connection_string (string): \
                      If neither the engines nor the credentials have been provided, a connection string can be used
                      to access the data. This will be overridden by both the engine and credentials if those are
                      provided.
                  url (string): \
                      If neither the engines, the credentials, nor the connection_string have been provided,
                      a url can be used to access the data. This will be overridden by all other configuration
                      options if any are provided.
                  concurrency (ConcurrencyConfig): Concurrency config used to configure the sqlalchemy engine.
          """
          super().__init__(name=name, batch_data_dict=batch_data_dict)
          self._name = name
  
          self._credentials = credentials
          self._connection_string = connection_string
          self._url = url
          self._create_temp_table = create_temp_table
  
          if engine is not None:
              if credentials is not None:
                  logger.warning(
                      "Both credentials and engine were provided during initialization of SqlAlchemyExecutionEngine. "
                      "Ignoring credentials."
                  )
              self.engine = engine
          else:
              concurrency: ConcurrencyConfig
              if data_context is None or data_context.concurrency is None:
                  concurrency = ConcurrencyConfig()
              else:
                  concurrency = data_context.concurrency
  
              concurrency.add_sqlalchemy_create_engine_parameters(kwargs)
  
              if credentials is not None:
                  self.engine = self._build_engine(credentials=credentials, **kwargs)
              elif connection_string is not None:
                  self.engine = sa.create_engine(connection_string, **kwargs)
              elif url is not None:
                  parsed_url = make_url(url)
                  self.drivername = parsed_url.drivername
                  self.engine = sa.create_engine(url, **kwargs)
              else:
                  raise InvalidConfigError(
                      "Credentials or an engine are required for a SqlAlchemyExecutionEngine."
                  )
  
          # these are two backends where temp_table_creation is not supported we set the default value to False.
          if self.engine.dialect.name.lower() in [
              "trino",
              "awsathena",  # WKS 202201 - AWS Athena currently doesn't support temp_tables.
          ]:
              self._create_temp_table = False
  
          # Get the dialect **for purposes of identifying types**
          if self.engine.dialect.name.lower() in [
              "postgresql",
              "mysql",
              "sqlite",
              "oracle",
              "mssql",
          ]:
              # These are the officially included and supported dialects by sqlalchemy
              self.dialect_module = import_library_module(
                  module_name=f"sqlalchemy.dialects.{self.engine.dialect.name}"
              )
  
          elif self.engine.dialect.name.lower() == "snowflake":
              self.dialect_module = import_library_module(
                  module_name="snowflake.sqlalchemy.snowdialect"
              )
          elif self.engine.dialect.name.lower() == "dremio":
              # WARNING: Dremio Support is experimental, functionality is not fully under test
              self.dialect_module = import_library_module(
                  module_name="sqlalchemy_dremio.pyodbc"
              )
          elif self.engine.dialect.name.lower() == "redshift":
              self.dialect_module = import_library_module(
                  module_name="sqlalchemy_redshift.dialect"
              )
          elif self.engine.dialect.name.lower() == "bigquery":
              self.dialect_module = import_library_module(
                  module_name=_BIGQUERY_MODULE_NAME
              )
          elif self.engine.dialect.name.lower() == "teradatasql":
              # WARNING: Teradata Support is experimental, functionality is not fully under test
              self.dialect_module = import_library_module(
                  module_name="teradatasqlalchemy.dialect"
              )
          else:
              self.dialect_module = None
  
          # <WILL> 20210726 - engine_backup is used by the snowflake connector, which requires connection and engine
          # to be closed and disposed separately. Currently self.engine can refer to either a Connection or Engine,
          # depending on the backend. This will need to be cleaned up in an upcoming refactor, so that Engine and
          # Connection can be handled separately.
          self._engine_backup = None
          if self.engine and self.engine.dialect.name.lower() in [
              "sqlite",
              "mssql",
              "snowflake",
              "mysql",
          ]:
              self._engine_backup = self.engine
              # sqlite/mssql temp tables only persist within a connection so override the engine
              self.engine = self.engine.connect()
  
          # Send a connect event to provide dialect type
          if data_context is not None and getattr(
              data_context, "_usage_statistics_handler", None
          ):
              handler = data_context._usage_statistics_handler
              handler.send_usage_message(
                  event="execution_engine.sqlalchemy.connect",
                  event_payload={
                      "anonymized_name": handler.anonymizer.anonymize(self.name),
                      "sqlalchemy_dialect": self.engine.name,
                  },
                  success=True,
              )
  
          # Gather the call arguments of the present function (and add the "class_name"), filter out the Falsy values,
          # and set the instance "_config" variable equal to the resulting dictionary.
          self._config = {
              "name": name,
              "credentials": credentials,
              "data_context": data_context,
              "engine": engine,
              "connection_string": connection_string,
              "url": url,
              "batch_data_dict": batch_data_dict,
              "module_name": self.__class__.__module__,
              "class_name": self.__class__.__name__,
          }
          self._config.update(kwargs)
          filter_properties_dict(properties=self._config, clean_falsy=True, inplace=True)
  
          self._sqlalchemy_data_splitter = SqlAlchemyDataSplitter()
  
      @property
      def credentials(self) -> Optional[dict]:
          return self._credentials
  
      @property
      def connection_string(self) -> Optional[str]:
          return self._connection_string
  
      @property
      def url(self) -> Optional[str]:
          return self._url
  
      def _build_engine(self, credentials: dict, **kwargs) -> "sa.engine.Engine":
          """
          Using a set of given credentials, constructs an Execution Engine , connecting to a database using a URL or a
          private key path.
          """
          # Update credentials with anything passed during connection time
          drivername = credentials.pop("drivername")
          schema_name = credentials.pop("schema_name", None)
          if schema_name is not None:
              logger.warning(
                  "schema_name specified creating a URL with schema is not supported. Set a default "
                  "schema on the user connecting to your database."
              )
  
          create_engine_kwargs = kwargs
          connect_args = credentials.pop("connect_args", None)
          if connect_args:
              create_engine_kwargs["connect_args"] = connect_args
  
          if "private_key_path" in credentials:
              options, create_engine_kwargs = self._get_sqlalchemy_key_pair_auth_url(
                  drivername, credentials
              )
          else:
              options = get_sqlalchemy_url(drivername, **credentials)
  
          self.drivername = drivername
          engine = sa.create_engine(options, **create_engine_kwargs)
          return engine
  
      def _get_sqlalchemy_key_pair_auth_url(
          self, drivername: str, credentials: dict
      ) -> Tuple["sa.engine.url.URL", Dict]:
          """
          Utilizing a private key path and a passphrase in a given credentials dictionary, attempts to encode the provided
          values into a private key. If passphrase is incorrect, this will fail and an exception is raised.
  
          Args:
              drivername(str) - The name of the driver class
              credentials(dict) - A dictionary of database credentials used to access the database
  
          Returns:
              a tuple consisting of a url with the serialized key-pair authentication, and a dictionary of engine kwargs.
          """
          from cryptography.hazmat.backends import default_backend
          from cryptography.hazmat.primitives import serialization
  
          private_key_path = credentials.pop("private_key_path")
          private_key_passphrase = credentials.pop("private_key_passphrase")
  
          with Path(private_key_path).expanduser().resolve().open(mode="rb") as key:
              try:
                  p_key = serialization.load_pem_private_key(
                      key.read(),
                      password=private_key_passphrase.encode()
                      if private_key_passphrase
                      else None,
                      backend=default_backend(),
                  )
              except ValueError as e:
                  if "incorrect password" in str(e).lower():
                      raise DatasourceKeyPairAuthBadPassphraseError(
                          datasource_name="SqlAlchemyDatasource",
                          message="Decryption of key failed, was the passphrase incorrect?",
                      ) from e
                  else:
                      raise e
          pkb = p_key.private_bytes(
              encoding=serialization.Encoding.DER,
              format=serialization.PrivateFormat.PKCS8,
              encryption_algorithm=serialization.NoEncryption(),
          )
  
          credentials_driver_name = credentials.pop("drivername", None)
          create_engine_kwargs = {"connect_args": {"private_key": pkb}}
          return (
              get_sqlalchemy_url(drivername or credentials_driver_name, **credentials),
              create_engine_kwargs,
          )
  
      def get_domain_records(
          self,
          domain_kwargs: Dict,
      ) -> Selectable:
          """
          Uses the given domain kwargs (which include row_condition, condition_parser, and ignore_row_if directives) to
          obtain and/or query a batch. Returns in the format of an SqlAlchemy table/column(s) object.
  
          Args:
              domain_kwargs (dict) - A dictionary consisting of the domain kwargs specifying which data to obtain
  
          Returns:
              An SqlAlchemy table/column(s) (the selectable object for obtaining data on which to compute)
          """
          batch_id = domain_kwargs.get("batch_id")
          if batch_id is None:
              # We allow no batch id specified if there is only one batch
              if self.active_batch_data:
                  data_object = self.active_batch_data
              else:
                  raise GreatExpectationsError(
                      "No batch is specified, but could not identify a loaded batch."
                  )
          else:
              if batch_id in self.loaded_batch_data_dict:
                  data_object = self.loaded_batch_data_dict[batch_id]
              else:
                  raise GreatExpectationsError(
                      f"Unable to find batch with batch_id {batch_id}"
                  )
  
          selectable: Selectable
          if "table" in domain_kwargs and domain_kwargs["table"] is not None:
              # TODO: Add logic to handle record_set_name once implemented
              # (i.e. multiple record sets (tables) in one batch
              if domain_kwargs["table"] != data_object.selectable.name:
                  selectable = sa.Table(
                      domain_kwargs["table"],
                      sa.MetaData(),
                      schema=data_object._schema_name,
                  )
              else:
                  selectable = data_object.selectable
          elif "query" in domain_kwargs:
              raise ValueError(
                  "query is not currently supported by SqlAlchemyExecutionEngine"
              )
          else:
              selectable = data_object.selectable
  
          """
          If a custom query is passed, selectable will be TextClause and not formatted
          as a subquery wrapped in "(subquery) alias". TextClause must first be converted
          to TextualSelect using sa.columns() before it can be converted to type Subquery
          """
          if TextClause and isinstance(selectable, TextClause):
              selectable = selectable.columns().subquery()
  
          # Filtering by row condition.
          if (
              "row_condition" in domain_kwargs
              and domain_kwargs["row_condition"] is not None
          ):
              condition_parser = domain_kwargs["condition_parser"]
              if condition_parser == "great_expectations__experimental__":
                  parsed_condition = parse_condition_to_sqlalchemy(
                      domain_kwargs["row_condition"]
                  )
                  selectable = (
                      sa.select([sa.text("*")])
                      .select_from(selectable)
                      .where(parsed_condition)
                  )
              else:
                  raise GreatExpectationsError(
                      "SqlAlchemyExecutionEngine only supports the great_expectations condition_parser."
                  )
  
          # Filtering by filter_conditions
          filter_conditions: List[RowCondition] = domain_kwargs.get(
              "filter_conditions", []
          )
          # For SqlAlchemyExecutionEngine only one filter condition is allowed
          if len(filter_conditions) == 1:
              filter_condition = filter_conditions[0]
              assert (
                  filter_condition.condition_type == RowConditionParserType.GE
              ), "filter_condition must be of type GE for SqlAlchemyExecutionEngine"
  
              selectable = (
                  sa.select([sa.text("*")])
                  .select_from(selectable)
                  .where(parse_condition_to_sqlalchemy(filter_condition.condition))
              )
          elif len(filter_conditions) > 1:
              raise GreatExpectationsError(
                  "SqlAlchemyExecutionEngine currently only supports a single filter condition."
              )
  
          if "column" in domain_kwargs:
              return selectable
  
          # Filtering by ignore_row_if directive
          if (
              "column_A" in domain_kwargs
              and "column_B" in domain_kwargs
              and "ignore_row_if" in domain_kwargs
          ):
              if self.active_batch_data.use_quoted_name:
                  # Checking if case-sensitive and using appropriate name
                  # noinspection PyPep8Naming
                  column_A_name = quoted_name(domain_kwargs["column_A"], quote=True)
                  # noinspection PyPep8Naming
                  column_B_name = quoted_name(domain_kwargs["column_B"], quote=True)
              else:
                  # noinspection PyPep8Naming
                  column_A_name = domain_kwargs["column_A"]
                  # noinspection PyPep8Naming
                  column_B_name = domain_kwargs["column_B"]
  
              ignore_row_if = domain_kwargs["ignore_row_if"]
              if ignore_row_if == "both_values_are_missing":
                  selectable = get_sqlalchemy_selectable(
                      sa.select([sa.text("*")])
                      .select_from(get_sqlalchemy_selectable(selectable))
                      .where(
                          sa.not_(
                              sa.and_(
                                  sa.column(column_A_name) == None,
                                  sa.column(column_B_name) == None,
                              )
                          )
                      )
                  )
              elif ignore_row_if == "either_value_is_missing":
                  selectable = get_sqlalchemy_selectable(
                      sa.select([sa.text("*")])
                      .select_from(get_sqlalchemy_selectable(selectable))
                      .where(
                          sa.not_(
                              sa.or_(
                                  sa.column(column_A_name) == None,
                                  sa.column(column_B_name) == None,
                              )
                          )
                      )
                  )
              else:
                  if ignore_row_if not in ["neither", "never"]:
                      raise ValueError(
                          f'Unrecognized value of ignore_row_if ("{ignore_row_if}").'
                      )
  
                  if ignore_row_if == "never":
                      # deprecated-v0.13.29
                      warnings.warn(
                          f"""The correct "no-action" value of the "ignore_row_if" directive for the column pair case is \
  "neither" (the use of "{ignore_row_if}" is deprecated as of v0.13.29 and will be removed in v0.16).  Please use "neither" moving forward.
  """,
                          DeprecationWarning,
                      )
  
              return selectable
  
          if "column_list" in domain_kwargs and "ignore_row_if" in domain_kwargs:
              if self.active_batch_data.use_quoted_name:
                  # Checking if case-sensitive and using appropriate name
                  column_list = [
                      quoted_name(domain_kwargs[column_name], quote=True)
                      for column_name in domain_kwargs["column_list"]
                  ]
              else:
                  column_list = domain_kwargs["column_list"]
  
              ignore_row_if = domain_kwargs["ignore_row_if"]
              if ignore_row_if == "all_values_are_missing":
                  selectable = get_sqlalchemy_selectable(
                      sa.select([sa.text("*")])
                      .select_from(get_sqlalchemy_selectable(selectable))
                      .where(
                          sa.not_(
                              sa.and_(
                                  *(
                                      sa.column(column_name) == None
                                      for column_name in column_list
                                  )
                              )
                          )
                      )
                  )
              elif ignore_row_if == "any_value_is_missing":
                  selectable = get_sqlalchemy_selectable(
                      sa.select([sa.text("*")])
                      .select_from(get_sqlalchemy_selectable(selectable))
                      .where(
                          sa.not_(
                              sa.or_(
                                  *(
                                      sa.column(column_name) == None
                                      for column_name in column_list
                                  )
                              )
                          )
                      )
                  )
              else:
                  if ignore_row_if != "never":
                      raise ValueError(
                          f'Unrecognized value of ignore_row_if ("{ignore_row_if}").'
                      )
  
              return selectable
  
          return selectable
  
      def get_compute_domain(
          self,
          domain_kwargs: Dict,
          domain_type: Union[str, MetricDomainTypes],
          accessor_keys: Optional[Iterable[str]] = None,
      ) -> Tuple[Selectable, dict, dict]:
          """Uses a given batch dictionary and domain kwargs to obtain a SqlAlchemy column object.
  
          Args:
              domain_kwargs (dict) - A dictionary consisting of the domain kwargs specifying which data to obtain
              domain_type (str or MetricDomainTypes) - an Enum value indicating which metric domain the user would
              like to be using, or a corresponding string value representing it. String types include "identity",
              "column", "column_pair", "table" and "other". Enum types include capitalized versions of these from the
              class MetricDomainTypes.
              accessor_keys (str iterable) - keys that are part of the compute domain but should be ignored when
              describing the domain and simply transferred with their associated values into accessor_domain_kwargs.
  
          Returns:
              SqlAlchemy column
          """
          selectable = self.get_domain_records(domain_kwargs)
  
          split_domain_kwargs = self._split_domain_kwargs(
              domain_kwargs, domain_type, accessor_keys
          )
  
          return selectable, split_domain_kwargs.compute, split_domain_kwargs.accessor
  
      def _split_column_metric_domain_kwargs(
          self,
          domain_kwargs: Dict,
          domain_type: MetricDomainTypes,
      ) -> SplitDomainKwargs:
          """Split domain_kwargs for column domain types into compute and accessor domain kwargs.
  
          Args:
              domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
              domain_type: an Enum value indicating which metric domain the user would
              like to be using.
  
          Returns:
              compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
              The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
          """
          assert (
              domain_type == MetricDomainTypes.COLUMN
          ), "This method only supports MetricDomainTypes.COLUMN"
  
          compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
          accessor_domain_kwargs: Dict = {}
  
          if "column" not in compute_domain_kwargs:
              raise ge_exceptions.GreatExpectationsError(
                  "Column not provided in compute_domain_kwargs"
              )
  
          # Checking if case-sensitive and using appropriate name
          if self.active_batch_data.use_quoted_name:
              accessor_domain_kwargs["column"] = quoted_name(
                  compute_domain_kwargs.pop("column"), quote=True
              )
          else:
              accessor_domain_kwargs["column"] = compute_domain_kwargs.pop("column")
  
          return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
  
      def _split_column_pair_metric_domain_kwargs(
          self,
          domain_kwargs: Dict,
          domain_type: MetricDomainTypes,
      ) -> SplitDomainKwargs:
          """Split domain_kwargs for column pair domain types into compute and accessor domain kwargs.
  
          Args:
              domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
              domain_type: an Enum value indicating which metric domain the user would
              like to be using.
  
          Returns:
              compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
              The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
          """
          assert (
              domain_type == MetricDomainTypes.COLUMN_PAIR
          ), "This method only supports MetricDomainTypes.COLUMN_PAIR"
  
          compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
          accessor_domain_kwargs: Dict = {}
  
          if not (
              "column_A" in compute_domain_kwargs and "column_B" in compute_domain_kwargs
          ):
              raise ge_exceptions.GreatExpectationsError(
                  "column_A or column_B not found within compute_domain_kwargs"
              )
  
          # Checking if case-sensitive and using appropriate name
          if self.active_batch_data.use_quoted_name:
              accessor_domain_kwargs["column_A"] = quoted_name(
                  compute_domain_kwargs.pop("column_A"), quote=True
              )
              accessor_domain_kwargs["column_B"] = quoted_name(
                  compute_domain_kwargs.pop("column_B"), quote=True
              )
          else:
              accessor_domain_kwargs["column_A"] = compute_domain_kwargs.pop("column_A")
              accessor_domain_kwargs["column_B"] = compute_domain_kwargs.pop("column_B")
  
          return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
  
      def _split_multi_column_metric_domain_kwargs(
          self,
          domain_kwargs: Dict,
          domain_type: MetricDomainTypes,
      ) -> SplitDomainKwargs:
          """Split domain_kwargs for multicolumn domain types into compute and accessor domain kwargs.
  
          Args:
              domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
              domain_type: an Enum value indicating which metric domain the user would
              like to be using.
  
          Returns:
              compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
              The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
          """
          assert (
              domain_type == MetricDomainTypes.MULTICOLUMN
          ), "This method only supports MetricDomainTypes.MULTICOLUMN"
  
          compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
          accessor_domain_kwargs: Dict = {}
  
          if "column_list" not in domain_kwargs:
              raise GreatExpectationsError("column_list not found within domain_kwargs")
  
          column_list = compute_domain_kwargs.pop("column_list")
  
          if len(column_list) < 2:
              raise GreatExpectationsError("column_list must contain at least 2 columns")
  
          # Checking if case-sensitive and using appropriate name
          if self.active_batch_data.use_quoted_name:
              accessor_domain_kwargs["column_list"] = [
                  quoted_name(column_name, quote=True) for column_name in column_list
              ]
          else:
              accessor_domain_kwargs["column_list"] = column_list
  
          return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
  
      def resolve_metric_bundle(
          self,
          metric_fn_bundle: Iterable[Tuple[MetricConfiguration, Any, dict, dict]],
      ) -> dict:
          """For every metric in a set of Metrics to resolve, obtains necessary metric keyword arguments and builds
          bundles of the metrics into one large query dictionary so that they are all executed simultaneously. Will fail
          if bundling the metrics together is not possible.
  
              Args:
                  metric_fn_bundle (Iterable[Tuple[MetricConfiguration, Callable, dict]): \
                      A Dictionary containing a MetricProvider's MetricConfiguration (its unique identifier), its metric provider function
                      (the function that actually executes the metric), and the arguments to pass to the metric provider function.
                      A dictionary of metrics defined in the registry and corresponding arguments
  
              Returns:
                  A dictionary of metric names and their corresponding now-queried values.
          """
          resolved_metrics = {}
  
          # We need a different query for each domain (where clause).
          queries: Dict[Tuple, dict] = {}
          for (
              metric_to_resolve,
              engine_fn,
              compute_domain_kwargs,
              accessor_domain_kwargs,
              metric_provider_kwargs,
          ) in metric_fn_bundle:
              if not isinstance(compute_domain_kwargs, IDDict):
                  compute_domain_kwargs = IDDict(compute_domain_kwargs)
              domain_id = compute_domain_kwargs.to_id()
              if domain_id not in queries:
                  queries[domain_id] = {
                      "select": [],
                      "ids": [],
                      "domain_kwargs": compute_domain_kwargs,
                  }
              if self.engine.dialect.name == "clickhouse":
                  queries[domain_id]["select"].append(
                      engine_fn.label(
                          metric_to_resolve.metric_name.join(
                              random.choices(string.ascii_lowercase, k=2)
                          )
                      )
                  )
              else:
                  queries[domain_id]["select"].append(
                      engine_fn.label(metric_to_resolve.metric_name)
                  )
              queries[domain_id]["ids"].append(metric_to_resolve.id)
          for query in queries.values():
              domain_kwargs = query["domain_kwargs"]
              selectable = self.get_domain_records(
                  domain_kwargs=domain_kwargs,
              )
              assert len(query["select"]) == len(query["ids"])
              try:
                  """
                  If a custom query is passed, selectable will be TextClause and not formatted
                  as a subquery wrapped in "(subquery) alias". TextClause must first be converted
                  to TextualSelect using sa.columns() before it can be converted to type Subquery
                  """
                  if TextClause and isinstance(selectable, TextClause):
                      res = self.engine.execute(
                          sa.select(query["select"]).select_from(
                              selectable.columns().subquery()
                          )
                      ).fetchall()
                  else:
                      res = self.engine.execute(
                          sa.select(query["select"]).select_from(selectable)
                      ).fetchall()
                  logger.debug(
                      f"SqlAlchemyExecutionEngine computed {len(res[0])} metrics on domain_id {IDDict(domain_kwargs).to_id()}"
                  )
              except OperationalError as oe:
                  exception_message: str = "An SQL execution Exception occurred.  "
                  exception_traceback: str = traceback.format_exc()
                  exception_message += f'{type(oe).__name__}: "{str(oe)}".  Traceback: "{exception_traceback}".'
                  logger.error(exception_message)
                  raise ExecutionEngineError(message=exception_message)
              assert (
                  len(res) == 1
              ), "all bundle-computed metrics must be single-value statistics"
              assert len(query["ids"]) == len(
                  res[0]
              ), "unexpected number of metrics returned"
              for idx, id in enumerate(query["ids"]):
                  resolved_metrics[id] = convert_to_json_serializable(res[0][idx])
  
          return resolved_metrics
  
      def close(self) -> None:
          """
          Note: Will 20210729
  
          This is a helper function that will close and dispose Sqlalchemy objects that are used to connect to a database.
          Databases like Snowflake require the connection and engine to be instantiated and closed separately, and not
          doing so has caused problems with hanging connections.
  
          Currently the ExecutionEngine does not support handling connections and engine separately, and will actually
          override the engine with a connection in some cases, obfuscating what object is used to actually used by the
          ExecutionEngine to connect to the external database. This will be handled in an upcoming refactor, which will
          allow this function to eventually become:
  
          self.connection.close()
          self.engine.dispose()
  
          More background can be found here: https://github.com/great-expectations/great_expectations/pull/3104/
          """
          if self._engine_backup:
              self.engine.close()
              self._engine_backup.dispose()
          else:
              self.engine.dispose()
  
      ### Sampling methods ###
  
      # _sample_using_limit
      # _sample_using_random
      # _sample_using_mod
      # _sample_using_a_list
      # _sample_using_md5
  
      def _sample_using_mod(
          self,
          column_name: str,
          mod: int,
          value: int,
      ) -> bool:
          """Take the mod of named column, and only keep rows that match the given value"""
          return sa.column(column_name) % mod == value
  
      def _sample_using_a_list(
          self,
          column_name: str,
          value_list: list,
      ) -> bool:
          """Match the values in the named column against value_list, and only keep the matches"""
          return sa.column(column_name).in_(value_list)
  
      def _sample_using_md5(
          self,
          column_name: str,
          hash_digits: int = 1,
          hash_value: str = "f",
      ) -> bool:
          """Hash the values in the named column, and split on that"""
          return (
              sa.func.right(
                  sa.func.md5(sa.cast(sa.column(column_name), sa.Text)), hash_digits
              )
              == hash_value
          )
  
      def get_splitter_method(self, splitter_method_name: str) -> Callable:
          """Get the appropriate splitter method from the method name.
  
          Args:
              splitter_method_name: name of the splitter to retrieve.
  
          Returns:
              splitter method.
          """
          return self._sqlalchemy_data_splitter.get_splitter_method(splitter_method_name)
  
      def execute_split_query(self, split_query: Selectable) -> List[LegacyRow]:
          """Use the execution engine to run the split query and fetch all of the results.
  
          Args:
              split_query: Query to be executed as a sqlalchemy Selectable.
  
          Returns:
              List of row results.
          """
          return self.engine.execute(split_query).fetchall()
  
      def _build_selectable_from_batch_spec(
          self, batch_spec: BatchSpec
      ) -> Union[Selectable, str]:
          if "splitter_method" in batch_spec:
              splitter_fn: Callable = self._sqlalchemy_data_splitter.get_splitter_method(
                  splitter_method_name=batch_spec["splitter_method"]
              )
              split_clause = splitter_fn(
                  batch_identifiers=batch_spec["batch_identifiers"],
                  **batch_spec["splitter_kwargs"],
              )
  
          else:
              split_clause = True
  
          table_name: str = batch_spec["table_name"]
          if "sampling_method" in batch_spec:
              if batch_spec["sampling_method"] == "_sample_using_limit":
                  # SQLalchemy's semantics for LIMIT are different than normal WHERE clauses,
                  # so the business logic for building the query needs to be different.
                  if self.engine.dialect.name.lower() == "oracle":
                      # limit doesn't compile properly for oracle so we will append rownum to query string later
                      raw_query = (
                          sa.select("*")
                          .select_from(
                              sa.table(
                                  table_name, schema=batch_spec.get("schema_name", None)
                              )
                          )
                          .where(split_clause)
                      )
                      query = str(
                          raw_query.compile(
                              self.engine, compile_kwargs={"literal_binds": True}
                          )
                      )
                      query += "\nAND ROWNUM <= %d" % batch_spec["sampling_kwargs"]["n"]
                      return query
                  else:
                      return (
                          sa.select("*")
                          .select_from(
                              sa.table(
                                  table_name, schema=batch_spec.get("schema_name", None)
                              )
                          )
                          .where(split_clause)
                          .limit(batch_spec["sampling_kwargs"]["n"])
                      )
              elif batch_spec["sampling_method"] == "_sample_using_random":
                  num_rows: int = self.engine.execute(
                      sa.select([sa.func.count()])
                      .select_from(
                          sa.table(table_name, schema=batch_spec.get("schema_name", None))
                      )
                      .where(split_clause)
                  ).scalar()
                  p: float = batch_spec["sampling_kwargs"]["p"] or 1.0
                  sample_size: int = round(p * num_rows)
                  return (
                      sa.select("*")
                      .select_from(
                          sa.table(table_name, schema=batch_spec.get("schema_name", None))
                      )
                      .where(split_clause)
                      .order_by(sa.func.random())
                      .limit(sample_size)
                  )
              else:
                  sampler_fn = getattr(self, batch_spec["sampling_method"])
                  return (
                      sa.select("*")
                      .select_from(
                          sa.table(table_name, schema=batch_spec.get("schema_name", None))
                      )
                      .where(
                          sa.and_(
                              split_clause,
                              sampler_fn(**batch_spec["sampling_kwargs"]),
                          )
                      )
                  )
          return (
              sa.select("*")
              .select_from(
                  sa.table(table_name, schema=batch_spec.get("schema_name", None))
              )
              .where(split_clause)
          )
  
      def get_batch_data_and_markers(
          self, batch_spec: BatchSpec
      ) -> Tuple[Any, BatchMarkers]:
          if not isinstance(
              batch_spec, (SqlAlchemyDatasourceBatchSpec, RuntimeQueryBatchSpec)
          ):
              raise InvalidBatchSpecError(
                  f"""SqlAlchemyExecutionEngine accepts batch_spec only of type SqlAlchemyDatasourceBatchSpec or
          RuntimeQueryBatchSpec (illegal type "{str(type(batch_spec))}" was received).
                          """
              )
  
          batch_data: Optional[SqlAlchemyBatchData] = None
          batch_markers: BatchMarkers = BatchMarkers(
              {
                  "ge_load_time": datetime.datetime.now(datetime.timezone.utc).strftime(
                      "%Y%m%dT%H%M%S.%fZ"
                  )
              }
          )
  
          source_schema_name: str = batch_spec.get("schema_name", None)
          source_table_name: str = batch_spec.get("table_name", None)
  
          temp_table_schema_name: Optional[str] = batch_spec.get("temp_table_schema_name")
          temp_table_name: Optional[str] = batch_spec.get("bigquery_temp_table")
  
          create_temp_table: bool = batch_spec.get(
              "create_temp_table", self._create_temp_table
          )
  
          if isinstance(batch_spec, RuntimeQueryBatchSpec):
              # query != None is already checked when RuntimeQueryBatchSpec is instantiated
              query: str = batch_spec.query
  
              batch_spec.query = "SQLQuery"
              batch_data = SqlAlchemyBatchData(
                  execution_engine=self,
                  query=query,
                  temp_table_schema_name=temp_table_schema_name,
                  temp_table_name=temp_table_name,
                  create_temp_table=create_temp_table,
                  source_table_name=source_table_name,
                  source_schema_name=source_schema_name,
              )
          elif isinstance(batch_spec, SqlAlchemyDatasourceBatchSpec):
              if self.engine.dialect.name.lower() == "oracle":
                  selectable: str = self._build_selectable_from_batch_spec(
                      batch_spec=batch_spec
                  )
              else:
                  selectable: Selectable = self._build_selectable_from_batch_spec(
                      batch_spec=batch_spec
                  )
  
              batch_data = SqlAlchemyBatchData(
                  execution_engine=self,
                  selectable=selectable,
                  temp_table_name=temp_table_name,
                  create_temp_table=create_temp_table,
                  source_table_name=source_table_name,
                  source_schema_name=source_schema_name,
              )
  
          return batch_data, batch_markers
  