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/great_expectations/execution_engine/sqlalchemy_execution_engine.py

import copy
import datetime
import hashlib
import logging
import math
import os
import random
import re
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.core.usage_statistics.events import UsageStatsEvents
from great_expectations.core.util import convert_to_json_serializable
from great_expectations.execution_engine.bundled_metric_configuration import (
    BundledMetricConfiguration,
)
from great_expectations.execution_engine.split_and_sample.sqlalchemy_data_sampler import (
    SqlAlchemyDataSampler,
)
from great_expectations.execution_engine.split_and_sample.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.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.execution_engine.sqlalchemy_dialect import GESqlDialect
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 Dialect, Row
    from sqlalchemy.exc import OperationalError
    from sqlalchemy.sql import Selectable
    from sqlalchemy.sql.elements import (
        BooleanClauseList,
        Label,
        TextClause,
        quoted_name,
    )
    from sqlalchemy.sql.selectable import Select, TextualSelect
except ImportError:
    BooleanClauseList = None
    DefaultDialect = None
    Dialect = None
    Label = None
    OperationalError = None
    reflection = None
    Row = None
    Select = None
    Selectable = None
    TextClause = None
    TextualSelect = None
    quoted_name = None


try:
    import psycopg2  # noqa: F401
    import sqlalchemy.dialects.postgresql.psycopg2 as sqlalchemy_psycopg2  # noqa: F401
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(
            GESqlDialect.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(
            GESqlDialect.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(
        GESqlDialect.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(
            GESqlDialect.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
    ) -> None:
        """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
        os.environ["SF_PARTNER"] = "great_expectations_oss"

        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.dialect_name in [
            GESqlDialect.TRINO,
            GESqlDialect.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.dialect_name in [
            GESqlDialect.POSTGRESQL,
            GESqlDialect.MYSQL,
            GESqlDialect.SQLITE,
            GESqlDialect.ORACLE,
            GESqlDialect.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.dialect_name == GESqlDialect.SNOWFLAKE:
            self.dialect_module = import_library_module(
                module_name="snowflake.sqlalchemy.snowdialect"
            )
        elif self.dialect_name == GESqlDialect.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.dialect_name == GESqlDialect.REDSHIFT:
            self.dialect_module = import_library_module(
                module_name="sqlalchemy_redshift.dialect"
            )
        elif self.dialect_name == GESqlDialect.BIGQUERY:
            self.dialect_module = import_library_module(
                module_name=_BIGQUERY_MODULE_NAME
            )
        elif self.dialect_name == GESqlDialect.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.dialect_name in [
            GESqlDialect.SQLITE,
            GESqlDialect.MSSQL,
            GESqlDialect.SNOWFLAKE,
            GESqlDialect.MYSQL,
        ]:
            self._engine_backup = self.engine
            # sqlite/mssql temp tables only persist within a connection so override the engine
            self.engine = self.engine.connect()
            if (
                self._engine_backup.dialect.name.lower() == GESqlDialect.SQLITE
                and not isinstance(self._engine_backup, sa.engine.base.Connection)
            ):
                raw_connection = self._engine_backup.raw_connection()
                raw_connection.create_function("sqrt", 1, lambda x: math.sqrt(x))
                raw_connection.create_function(
                    "md5",
                    2,
                    lambda x, d: hashlib.md5(str(x).encode("utf-8")).hexdigest()[
                        -1 * d :
                    ],
                )

        # 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=UsageStatsEvents.EXECUTION_ENGINE_SQLALCHEMY_CONNECT.value,
                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._data_splitter = SqlAlchemyDataSplitter(dialect=self.dialect_name)
        self._data_sampler = SqlAlchemyDataSampler()

    @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

    @property
    def dialect(self) -> Dialect:
        return self.engine.dialect

    @property
    def dialect_name(self) -> str:
        """Retrieve the string name of the engine dialect in lowercase e.g. "postgresql".

        Returns:
            String representation of the sql dialect.
        """
        return self.engine.dialect.name.lower()

    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

    @staticmethod
    def _get_sqlalchemy_key_pair_auth_url(
        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)
        """
        data_object: SqlAlchemyBatchData

        batch_id: Optional[str] = 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:
                # noinspection PyProtectedMember
                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[BundledMetricConfiguration],
    ) -> Dict[Tuple[str, str, str], Any]:
        """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[BundledMetricConfiguration]): \
                    "BundledMetricConfiguration" contains MetricProvider's MetricConfiguration (its unique identifier),
                    its metric provider function (the function that actually executes the metric), and arguments to pass
                    to metric provider function (dictionary of metrics defined in registry and corresponding arguments).

            Returns:
                A dictionary of "MetricConfiguration" IDs and their corresponding now-queried (fully resolved) values.
        """
        resolved_metrics: Dict[Tuple[str, str, str], Any] = {}

        res: List[Row]

        # We need a different query for each domain (where clause).
        queries: Dict[Tuple, dict] = {}

        query: dict

        domain_id: Tuple[str, str, str]

        bundled_metric_configuration: BundledMetricConfiguration
        for bundled_metric_configuration in metric_fn_bundle:
            bundled_metric_configuration: BundledMetricConfiguration
            metric_to_resolve: MetricConfiguration = (
                bundled_metric_configuration.metric_configuration
            )
            metric_fn: Any = bundled_metric_configuration.metric_fn
            compute_domain_kwargs: dict = (
                bundled_metric_configuration.compute_domain_kwargs
            )

            if not isinstance(compute_domain_kwargs, IDDict):
                compute_domain_kwargs = IDDict(compute_domain_kwargs)

            domain_id = IDDict.convert_dictionary_to_id_dict(
                data=convert_to_json_serializable(data=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(
                    metric_fn.label(
                        metric_to_resolve.metric_name.join(
                            random.choices(string.ascii_lowercase, k=2)
                        )
                    )
                )
            else:
                queries[domain_id]["select"].append(
                    metric_fn.label(metric_to_resolve.metric_name)
                )

            queries[domain_id]["ids"].append(metric_to_resolve.id)

        for query in queries.values():
            domain_kwargs: dict = query["domain_kwargs"]
            selectable: Any = 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()
                elif (Select and isinstance(selectable, Select)) or (
                    TextualSelect and isinstance(selectable, TextualSelect)
                ):
                    res = self.engine.execute(
                        sa.select(query["select"]).select_from(selectable.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.convert_dictionary_to_id_dict(data=convert_to_json_serializable(data=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"

            idx: int
            metric_id: Tuple[str, str, str]
            for idx, metric_id in enumerate(query["ids"]):
                # Converting SQL query execution results into JSON-serializable format produces simple data types,
                # amenable for subsequent post-processing by higher-level "Metric" and "Expectation" layers.
                resolved_metrics[metric_id] = convert_to_json_serializable(
                    data=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()

    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._data_splitter.get_splitter_method(splitter_method_name)

    def execute_split_query(self, split_query: Selectable) -> List[Row]:
        """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.
        """
        if self.dialect_name == "awsathena":
            # Note: Athena does not support casting to string, only to varchar
            # but sqlalchemy currently generates a query as `CAST(colname AS STRING)` instead
            # of `CAST(colname AS VARCHAR)` with other dialects.
            split_query: str = str(
                split_query.compile(self.engine, compile_kwargs={"literal_binds": True})
            )

            pattern = re.compile(r"(CAST\(EXTRACT\(.*?\))( AS STRING\))", re.IGNORECASE)
            split_query = re.sub(pattern, r"\1 AS VARCHAR)", split_query)

        return self.engine.execute(split_query).fetchall()

    def get_data_for_batch_identifiers(
        self, table_name: str, splitter_method_name: str, splitter_kwargs: dict
    ) -> List[dict]:
        """Build data used to construct batch identifiers for the input table using the provided splitter config.

        Sql splitter configurations yield the unique values that comprise a batch by introspecting your data.

        Args:
            table_name: Table to split.
            splitter_method_name: Desired splitter method to use.
            splitter_kwargs: Dict of directives used by the splitter method as keyword arguments of key=value.

        Returns:
            List of dicts of the form [{column_name: {"key": value}}]
        """
        return self._data_splitter.get_data_for_batch_identifiers(
            execution_engine=self,
            table_name=table_name,
            splitter_method_name=splitter_method_name,
            splitter_kwargs=splitter_kwargs,
        )

    def _build_selectable_from_batch_spec(
        self, batch_spec: BatchSpec
    ) -> Union[Selectable, str]:
        if "splitter_method" in batch_spec:
            splitter_fn: Callable = self._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:
            if self.dialect_name == GESqlDialect.SQLITE:
                split_clause = sa.text("1 = 1")
            else:
                split_clause = sa.true()

        table_name: str = batch_spec["table_name"]
        sampling_method: Optional[str] = batch_spec.get("sampling_method")
        if sampling_method is not None:
            if sampling_method in [
                "_sample_using_limit",
                "sample_using_limit",
                "_sample_using_random",
                "sample_using_random",
            ]:
                sampler_fn = self._data_sampler.get_sampler_method(sampling_method)
                return sampler_fn(
                    execution_engine=self,
                    batch_spec=batch_spec,
                    where_clause=split_clause,
                )
            else:
                sampler_fn = self._data_sampler.get_sampler_method(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),
                        )
                    )
                )

        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(
            {
                "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")

        if batch_spec.get("bigquery_temp_table"):
            # deprecated-v0.15.3
            warnings.warn(
                "BigQuery tables that are created as the result of a query are no longer created as "
                "permanent tables. Thus, a named permanent table through the `bigquery_temp_table`"
                "parameter is not required. The `bigquery_temp_table` parameter is deprecated as of"
                "v0.15.3 and will be removed in v0.18.",
                DeprecationWarning,
            )

        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,
                create_temp_table=create_temp_table,
                source_table_name=source_table_name,
                source_schema_name=source_schema_name,
            )
        elif isinstance(batch_spec, SqlAlchemyDatasourceBatchSpec):
            selectable: Union[Selectable, str] = self._build_selectable_from_batch_spec(
                batch_spec=batch_spec
            )
            batch_data = SqlAlchemyBatchData(
                execution_engine=self,
                selectable=selectable,
                create_temp_table=create_temp_table,
                source_table_name=source_table_name,
                source_schema_name=source_schema_name,
            )

        return batch_data, batch_markers

great-expectations / great_expectations / 20220913.63

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