import inspect
import logging
import traceback
import uuid
import warnings
from datetime import datetime
from functools import wraps
from typing import Dict, Iterable, List

import numpy as np
import pandas as pd
from dateutil.parser import parse

from great_expectations.data_asset import DataAsset
from great_expectations.data_asset.util import DocInherit, parse_result_format
from great_expectations.dataset.util import (
    check_sql_engine_dialect,
    get_approximate_percentile_disc_sql,
    get_sql_dialect_floating_point_infinity_value,
)
from great_expectations.util import import_library_module

from ..core import convert_to_json_serializable
from .dataset import Dataset
from .pandas_dataset import PandasDataset

logger = logging.getLogger(__name__)

try:
    import sqlalchemy as sa
    from sqlalchemy.dialects import registry
    from sqlalchemy.engine import reflection
    from sqlalchemy.engine.default import DefaultDialect
    from sqlalchemy.engine.result import RowProxy
    from sqlalchemy.exc import ProgrammingError
    from sqlalchemy.sql.elements import Label, TextClause, WithinGroup, quoted_name
    from sqlalchemy.sql.expression import BinaryExpression, literal
    from sqlalchemy.sql.operators import custom_op
    from sqlalchemy.sql.selectable import CTE, Select
except ImportError:
    logger.debug(
        "Unable to load SqlAlchemy context; install optional sqlalchemy dependency for support"
    )
    sa = None
    registry = None
    reflection = None
    BinaryExpression = None
    literal = None
    Select = None
    CTE = None
    custom_op = None
    Label = None
    WithinGroup = None
    TextClause = None
    RowProxy = None
    DefaultDialect = None
    ProgrammingError = 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 snowflake.sqlalchemy.snowdialect

    # 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)
    registry.register("snowflake", "snowflake.sqlalchemy", "dialect")
except (ImportError, KeyError):
    snowflake = None

try:
    import pybigquery.sqlalchemy_bigquery

    # Sometimes "pybigquery.sqlalchemy_bigquery" fails to self-register in certain environments, so we do it explicitly.
    # (see https://stackoverflow.com/questions/53284762/nosuchmoduleerror-cant-load-plugin-sqlalchemy-dialectssnowflake)
    registry.register("bigquery", "pybigquery.sqlalchemy_bigquery", "BigQueryDialect")
    try:
        getattr(pybigquery.sqlalchemy_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(pybigquery.sqlalchemy_bigquery._type_map)
        )
        bigquery_types_tuple = BigQueryTypes(**pybigquery.sqlalchemy_bigquery._type_map)
except ImportError:
    bigquery_types_tuple = None
    pybigquery = None


try:
    # SQLAlchemy does not export the "INT" type for the MS SQL Server dialect; however "INT" is supported by the engine.
    # Since SQLAlchemy exports the "INTEGER" type for the MS SQL Server dialect, alias "INT" to the "INTEGER" type.
    import sqlalchemy.dialects.mssql as mssqltypes

    try:
        getattr(mssqltypes, "INT")
    except AttributeError:
        mssqltypes.INT = mssqltypes.INTEGER
except ImportError:
    pass

try:
    import pyathena.sqlalchemy_athena
except ImportError:
    pyathena = None


class SqlAlchemyBatchReference:
    def __init__(self, engine, table_name=None, schema=None, query=None):
        self._engine = engine
        if table_name is None and query is None:
            raise ValueError("Table_name or query must be specified")

        self._table_name = table_name
        self._schema = schema
        self._query = query

    def get_init_kwargs(self):
        if self._table_name and self._query:
            # This is allowed in BigQuery where a temporary table name must be provided *with* the
            # custom sql to execute.
            kwargs = {
                "engine": self._engine,
                "table_name": self._table_name,
                "custom_sql": self._query,
            }
        elif self._table_name:
            kwargs = {"engine": self._engine, "table_name": self._table_name}
        else:
            kwargs = {"engine": self._engine, "custom_sql": self._query}
        if self._schema:
            kwargs["schema"] = self._schema

        return kwargs


class MetaSqlAlchemyDataset(Dataset):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    @classmethod
    def column_map_expectation(cls, func):
        """For SqlAlchemy, this decorator allows individual column_map_expectations to simply return the filter
        that describes the expected condition on their data.

        The decorator will then use that filter to obtain unexpected elements, relevant counts, and return the formatted
        object.
        """
        argspec = inspect.getfullargspec(func)[0][1:]

        @cls.expectation(argspec)
        @wraps(func)
        def inner_wrapper(
            self, column, mostly=None, result_format=None, *args, **kwargs
        ):
            if self.batch_kwargs.get("use_quoted_name"):
                column = quoted_name(column, quote=True)
            if result_format is None:
                result_format = self.default_expectation_args["result_format"]

            result_format = parse_result_format(result_format)

            if result_format["result_format"] == "COMPLETE":
                warnings.warn(
                    "Setting result format to COMPLETE for a SqlAlchemyDataset can be dangerous because it will not limit the number of returned results."
                )
                unexpected_count_limit = None
            else:
                unexpected_count_limit = result_format["partial_unexpected_count"]

            expected_condition: BinaryExpression = func(self, column, *args, **kwargs)

            # Added to prepare for when an ignore_values argument is added to the expectation
            ignore_values: list = [None]
            if func.__name__ in [
                "expect_column_values_to_not_be_null",
                "expect_column_values_to_be_null",
            ]:
                ignore_values = []
                # Counting the number of unexpected values can be expensive when there is a large
                # number of np.nan values.
                # This only happens on expect_column_values_to_not_be_null expectations.
                # Since there is no reason to look for most common unexpected values in this case,
                # we will instruct the result formatting method to skip this step.
                result_format["partial_unexpected_count"] = 0

            ignore_values_conditions: List[BinaryExpression] = []
            if (
                len(ignore_values) > 0
                and None not in ignore_values
                or len(ignore_values) > 1
                and None in ignore_values
            ):
                ignore_values_conditions += [
                    sa.column(column).in_(
                        [val for val in ignore_values if val is not None]
                    )
                ]
            if None in ignore_values:
                ignore_values_conditions += [sa.column(column).is_(None)]

            ignore_values_condition: BinaryExpression
            if len(ignore_values_conditions) > 1:
                ignore_values_condition = sa.or_(*ignore_values_conditions)
            elif len(ignore_values_conditions) == 1:
                ignore_values_condition = ignore_values_conditions[0]
            else:
                ignore_values_condition = BinaryExpression(
                    sa.literal(False), sa.literal(True), custom_op("=")
                )

            count_query: Select
            if self.sql_engine_dialect.name.lower() == "mssql":
                count_query = self._get_count_query_mssql(
                    expected_condition=expected_condition,
                    ignore_values_condition=ignore_values_condition,
                )
            else:
                count_query = self._get_count_query_generic_sqlalchemy(
                    expected_condition=expected_condition,
                    ignore_values_condition=ignore_values_condition,
                )

            count_results: dict = dict(self.engine.execute(count_query).fetchone())

            # Handle case of empty table gracefully:
            if (
                "element_count" not in count_results
                or count_results["element_count"] is None
            ):
                count_results["element_count"] = 0
            if "null_count" not in count_results or count_results["null_count"] is None:
                count_results["null_count"] = 0
            if (
                "unexpected_count" not in count_results
                or count_results["unexpected_count"] is None
            ):
                count_results["unexpected_count"] = 0

            # Some engines may return Decimal from count queries (lookin' at you MSSQL)
            # Convert to integers
            count_results["element_count"] = int(count_results["element_count"])
            count_results["null_count"] = int(count_results["null_count"])
            count_results["unexpected_count"] = int(count_results["unexpected_count"])

            # Retrieve unexpected values
            unexpected_query_results = self.engine.execute(
                sa.select([sa.column(column)])
                .select_from(self._table)
                .where(
                    sa.and_(
                        sa.not_(expected_condition), sa.not_(ignore_values_condition)
                    )
                )
                .limit(unexpected_count_limit)
            )

            nonnull_count: int = count_results["element_count"] - count_results[
                "null_count"
            ]

            if "output_strftime_format" in kwargs:
                output_strftime_format = kwargs["output_strftime_format"]
                maybe_limited_unexpected_list = []
                for x in unexpected_query_results.fetchall():
                    if isinstance(x[column], str):
                        col = parse(x[column])
                    else:
                        col = x[column]
                    maybe_limited_unexpected_list.append(
                        datetime.strftime(col, output_strftime_format)
                    )
            else:
                maybe_limited_unexpected_list = [
                    x[column] for x in unexpected_query_results.fetchall()
                ]

            success_count = nonnull_count - count_results["unexpected_count"]
            success, percent_success = self._calc_map_expectation_success(
                success_count, nonnull_count, mostly
            )

            return_obj = self._format_map_output(
                result_format,
                success,
                count_results["element_count"],
                nonnull_count,
                count_results["unexpected_count"],
                maybe_limited_unexpected_list,
                None,
            )

            if func.__name__ in [
                "expect_column_values_to_not_be_null",
                "expect_column_values_to_be_null",
            ]:
                # These results are unnecessary for the above expectations
                del return_obj["result"]["unexpected_percent_nonmissing"]
                del return_obj["result"]["missing_count"]
                del return_obj["result"]["missing_percent"]
                try:
                    del return_obj["result"]["partial_unexpected_counts"]
                    del return_obj["result"]["partial_unexpected_list"]
                except KeyError:
                    pass

            return return_obj

        inner_wrapper.__name__ = func.__name__
        inner_wrapper.__doc__ = func.__doc__

        return inner_wrapper

    def _get_count_query_mssql(
        self,
        expected_condition: BinaryExpression,
        ignore_values_condition: BinaryExpression,
    ) -> Select:
        # mssql expects all temporary table names to have a prefix '#'
        temp_table_name: str = f"#ge_tmp_{str(uuid.uuid4())[:8]}"

        with self.engine.begin():
            metadata: sa.MetaData = sa.MetaData(self.engine)
            temp_table_obj: sa.Table = sa.Table(
                temp_table_name,
                metadata,
                sa.Column("condition", sa.Integer, primary_key=False, nullable=False),
            )
            temp_table_obj.create(self.engine, checkfirst=True)

            count_case_statement: List[sa.sql.elements.Label] = [
                sa.case(
                    [
                        (
                            sa.and_(
                                sa.not_(expected_condition),
                                sa.not_(ignore_values_condition),
                            ),
                            1,
                        )
                    ],
                    else_=0,
                ).label("condition")
            ]
            inner_case_query: sa.sql.dml.Insert = temp_table_obj.insert().from_select(
                count_case_statement,
                sa.select(count_case_statement).select_from(self._table),
            )
            self.engine.execute(inner_case_query)

        element_count_query: Select = sa.select(
            [
                sa.func.count().label("element_count"),
                sa.func.sum(sa.case([(ignore_values_condition, 1)], else_=0)).label(
                    "null_count"
                ),
            ]
        ).select_from(self._table).alias("ElementAndNullCountsSubquery")

        unexpected_count_query: Select = sa.select(
            [sa.func.sum(sa.column("condition")).label("unexpected_count"),]
        ).select_from(temp_table_obj).alias("UnexpectedCountSubquery")

        count_query: Select = sa.select(
            [
                element_count_query.c.element_count,
                element_count_query.c.null_count,
                unexpected_count_query.c.unexpected_count,
            ]
        )

        return count_query

    def _get_count_query_generic_sqlalchemy(
        self,
        expected_condition: BinaryExpression,
        ignore_values_condition: BinaryExpression,
    ) -> Select:
        return sa.select(
            [
                sa.func.count().label("element_count"),
                sa.func.sum(sa.case([(ignore_values_condition, 1)], else_=0)).label(
                    "null_count"
                ),
                sa.func.sum(
                    sa.case(
                        [
                            (
                                sa.and_(
                                    sa.not_(expected_condition),
                                    sa.not_(ignore_values_condition),
                                ),
                                1,
                            )
                        ],
                        else_=0,
                    )
                ).label("unexpected_count"),
            ]
        ).select_from(self._table)


class SqlAlchemyDataset(MetaSqlAlchemyDataset):
    """

--ge-feature-maturity-info--

    id: validation_engine_sqlalchemy
    title: Validation Engine - SQLAlchemy
    icon:
    short_description: Use SQLAlchemy to validate data in a database
    description: Use SQLAlchemy to validate data in a database
    how_to_guide_url: https://docs.greatexpectations.io/en/latest/how_to_guides/creating_batches/how_to_load_a_database_table_or_a_query_result_as_a_batch.html
    maturity: Production
    maturity_details:
        api_stability: High
        implementation_completeness: Moderate (temp table handling/permissions not universal)
        unit_test_coverage: High
        integration_infrastructure_test_coverage: N/A
        documentation_completeness:  Minimal (none)
        bug_risk: Low

--ge-feature-maturity-info--
"""

    @classmethod
    def from_dataset(cls, dataset=None):
        if isinstance(dataset, SqlAlchemyDataset):
            return cls(table_name=str(dataset._table.name), engine=dataset.engine)
        else:
            raise ValueError("from_dataset requires a SqlAlchemy dataset")

    def __init__(
        self,
        table_name=None,
        engine=None,
        connection_string=None,
        custom_sql=None,
        schema=None,
        *args,
        **kwargs,
    ):

        if custom_sql and not table_name:
            # NOTE: Eugene 2020-01-31: @James, this is a not a proper fix, but without it the "public" schema
            # was used for a temp table and raising an error
            schema = None
            table_name = f"ge_tmp_{str(uuid.uuid4())[:8]}"
            # mssql expects all temporary table names to have a prefix '#'
            if engine.dialect.name.lower() == "mssql":
                table_name = f"#{table_name}"
            self.generated_table_name = table_name
        else:
            self.generated_table_name = None

        if table_name is None:
            raise ValueError("No table_name provided.")

        if engine is None and connection_string is None:
            raise ValueError("Engine or connection_string must be provided.")

        if engine is not None:
            self.engine = engine
        else:
            try:
                self.engine = sa.create_engine(connection_string)
            except Exception as err:
                # Currently we do no error handling if the engine doesn't work out of the box.
                raise err

        if self.engine.dialect.name.lower() == "bigquery":
            # In BigQuery the table name is already qualified with its schema name
            self._table = sa.Table(table_name, sa.MetaData(), schema=None)
        else:
            self._table = sa.Table(table_name, sa.MetaData(), schema=schema)

        # Get the dialect **for purposes of identifying types**
        if self.engine.dialect.name.lower() in [
            "postgresql",
            "mysql",
            "sqlite",
            "oracle",
            "mssql",
            "oracle",
        ]:
            # These are the officially included and supported dialects by sqlalchemy
            self.dialect = import_library_module(
                module_name="sqlalchemy.dialects." + self.engine.dialect.name
            )

        elif self.engine.dialect.name.lower() == "snowflake":
            self.dialect = import_library_module(
                module_name="snowflake.sqlalchemy.snowdialect"
            )

        elif self.engine.dialect.name.lower() == "redshift":
            self.dialect = import_library_module(
                module_name="sqlalchemy_redshift.dialect"
            )
        elif self.engine.dialect.name.lower() == "bigquery":
            self.dialect = import_library_module(
                module_name="pybigquery.sqlalchemy_bigquery"
            )
        elif self.engine.dialect.name.lower() == "awsathena":
            self.dialect = import_library_module(
                module_name="pyathena.sqlalchemy_athena"
            )
        else:
            self.dialect = None

        if engine and engine.dialect.name.lower() in ["sqlite", "mssql", "snowflake"]:
            # sqlite/mssql/snowflake temp tables only persist within a connection so override the engine
            self.engine = engine.connect()

        if schema is not None and custom_sql is not None:
            # temporary table will be written to temp schema, so don't allow
            # a user-defined schema
            # NOTE: 20200306 - JPC - Previously, this would disallow both custom_sql (a query) and a schema, but
            # that is overly restrictive -- snowflake could have had a schema specified, for example, in which to create
            # a temporary table.
            # raise ValueError("Cannot specify both schema and custom_sql.")
            pass

        if custom_sql is not None and self.engine.dialect.name.lower() == "bigquery":
            if (
                self.generated_table_name is not None
                and self.engine.dialect.dataset_id is None
            ):
                raise ValueError(
                    "No BigQuery dataset specified. Use bigquery_temp_table batch_kwarg or a specify a "
                    "default dataset in engine url"
                )

        if custom_sql:
            self.create_temporary_table(table_name, custom_sql, schema_name=schema)

            if self.generated_table_name is not None:
                if self.engine.dialect.name.lower() == "bigquery":
                    logger.warning(
                        "Created permanent table {table_name}".format(
                            table_name=table_name
                        )
                    )
                if self.engine.dialect.name.lower() == "awsathena":
                    logger.warning(
                        "Created permanent table default.{table_name}".format(
                            table_name=table_name
                        )
                    )

        try:
            insp = reflection.Inspector.from_engine(self.engine)
            self.columns = insp.get_columns(table_name, schema=schema)
        except KeyError:
            # we will get a KeyError for temporary tables, since
            # reflection will not find the temporary schema
            self.columns = self.column_reflection_fallback()

        # Use fallback because for mssql reflection doesn't throw an error but returns an empty list
        if len(self.columns) == 0:
            self.columns = self.column_reflection_fallback()

        # Only call super once connection is established and table_name and columns known to allow autoinspection
        super().__init__(*args, **kwargs)

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

    def attempt_allowing_relative_error(self):
        detected_redshift: bool = (
            sqlalchemy_redshift is not None
            and check_sql_engine_dialect(
                actual_sql_engine_dialect=self.sql_engine_dialect,
                candidate_sql_engine_dialect=sqlalchemy_redshift.dialect.RedshiftDialect,
            )
        )
        # noinspection PyTypeChecker
        detected_psycopg2: bool = (
            sqlalchemy_psycopg2 is not None
            and check_sql_engine_dialect(
                actual_sql_engine_dialect=self.sql_engine_dialect,
                candidate_sql_engine_dialect=sqlalchemy_psycopg2.PGDialect_psycopg2,
            )
        )
        return detected_redshift or detected_psycopg2

    def head(self, n=5):
        """Returns a *PandasDataset* with the first *n* rows of the given Dataset"""

        try:
            df = next(
                pd.read_sql_table(
                    table_name=self._table.name,
                    schema=self._table.schema,
                    con=self.engine,
                    chunksize=n,
                )
            )
        except (ValueError, NotImplementedError):
            # it looks like MetaData that is used by pd.read_sql_table
            # cannot work on a temp table.
            # If it fails, we are trying to get the data using read_sql
            head_sql_str = "select * from "
            if self._table.schema and self.engine.dialect.name.lower() != "bigquery":
                head_sql_str += self._table.schema + "." + self._table.name
            elif self.engine.dialect.name.lower() == "bigquery":
                head_sql_str += "`" + self._table.name + "`"
            else:
                head_sql_str += self._table.name
            head_sql_str += " limit {:d}".format(n)

            # Limit is unknown in mssql! Use top instead!
            if self.engine.dialect.name.lower() == "mssql":
                head_sql_str = "select top({n}) * from {table}".format(
                    n=n, table=self._table.name
                )

            df = pd.read_sql(head_sql_str, con=self.engine)
        except StopIteration:
            df = pd.DataFrame(columns=self.get_table_columns())

        return PandasDataset(
            df,
            expectation_suite=self.get_expectation_suite(
                discard_failed_expectations=False,
                discard_result_format_kwargs=False,
                discard_catch_exceptions_kwargs=False,
                discard_include_config_kwargs=False,
            ),
        )

    def get_row_count(self, table_name=None):
        if table_name is None:
            table_name = self._table
        else:
            table_name = sa.table(table_name)
        count_query = sa.select([sa.func.count()]).select_from(table_name)
        return int(self.engine.execute(count_query).scalar())

    def get_column_count(self):
        return len(self.columns)

    def get_table_columns(self) -> List[str]:
        return [col["name"] for col in self.columns]

    def get_column_nonnull_count(self, column):
        ignore_values = [None]
        count_query = sa.select(
            [
                sa.func.count().label("element_count"),
                sa.func.sum(
                    sa.case(
                        [
                            (
                                sa.or_(
                                    sa.column(column).in_(ignore_values),
                                    # Below is necessary b/c sa.in_() uses `==` but None != None
                                    # But we only consider this if None is actually in the list of ignore values
                                    sa.column(column).is_(None)
                                    if None in ignore_values
                                    else False,
                                ),
                                1,
                            )
                        ],
                        else_=0,
                    )
                ).label("null_count"),
            ]
        ).select_from(self._table)
        count_results = dict(self.engine.execute(count_query).fetchone())
        element_count = int(count_results.get("element_count") or 0)
        null_count = int(count_results.get("null_count") or 0)
        return element_count - null_count

    def get_column_sum(self, column):
        return self.engine.execute(
            sa.select([sa.func.sum(sa.column(column))]).select_from(self._table)
        ).scalar()

    def get_column_max(self, column, parse_strings_as_datetimes=False):
        if parse_strings_as_datetimes:
            raise NotImplementedError
        return self.engine.execute(
            sa.select([sa.func.max(sa.column(column))]).select_from(self._table)
        ).scalar()

    def get_column_min(self, column, parse_strings_as_datetimes=False):
        if parse_strings_as_datetimes:
            raise NotImplementedError
        return self.engine.execute(
            sa.select([sa.func.min(sa.column(column))]).select_from(self._table)
        ).scalar()

    def get_column_value_counts(self, column, sort="value", collate=None):
        if sort not in ["value", "count", "none"]:
            raise ValueError("sort must be either 'value', 'count', or 'none'")

        query = (
            sa.select(
                [
                    sa.column(column).label("value"),
                    sa.func.count(sa.column(column)).label("count"),
                ]
            )
            .where(sa.column(column) != None)
            .group_by(sa.column(column))
        )
        if sort == "value":
            # NOTE: depending on the way the underlying database collates columns,
            # ordering can vary. postgresql collate "C" matches default sort
            # for python and most other systems, but is not universally supported,
            # so we use the default sort for the system, unless specifically overridden
            if collate is not None:
                query = query.order_by(sa.column(column).collate(collate))
            else:
                query = query.order_by(sa.column(column))
        elif sort == "count":
            query = query.order_by(sa.column("count").desc())
        results = self.engine.execute(query.select_from(self._table)).fetchall()
        series = pd.Series(
            [row[1] for row in results],
            index=pd.Index(data=[row[0] for row in results], name="value"),
            name="count",
        )
        return series

    def get_column_mean(self, column):
        return self.engine.execute(
            sa.select([sa.func.avg(sa.column(column))]).select_from(self._table)
        ).scalar()

    def get_column_unique_count(self, column):
        return self.engine.execute(
            sa.select([sa.func.count(sa.func.distinct(sa.column(column)))]).select_from(
                self._table
            )
        ).scalar()

    def get_column_median(self, column):
        # AWS Athena does not support offset
        if self.sql_engine_dialect.name.lower() == "awsathena":
            raise NotImplementedError("AWS Athena does not support OFFSET.")
        nonnull_count = self.get_column_nonnull_count(column)
        element_values = self.engine.execute(
            sa.select([sa.column(column)])
            .order_by(sa.column(column))
            .where(sa.column(column) != None)
            .offset(max(nonnull_count // 2 - 1, 0))
            .limit(2)
            .select_from(self._table)
        )

        column_values = list(element_values.fetchall())

        if len(column_values) == 0:
            column_median = None
        elif nonnull_count % 2 == 0:
            # An even number of column values: take the average of the two center values
            column_median = (
                float(
                    column_values[0][0]
                    + column_values[1][0]  # left center value  # right center value
                )
                / 2.0
            )  # Average center values
        else:
            # An odd number of column values, we can just take the center value
            column_median = column_values[1][0]  # True center value
        return column_median

    def get_column_quantiles(
        self, column: str, quantiles: Iterable, allow_relative_error: bool = False
    ) -> list:
        if self.sql_engine_dialect.name.lower() == "mssql":
            return self._get_column_quantiles_mssql(column=column, quantiles=quantiles)
        elif self.sql_engine_dialect.name.lower() == "bigquery":
            return self._get_column_quantiles_bigquery(
                column=column, quantiles=quantiles
            )
        elif self.sql_engine_dialect.name.lower() == "mysql":
            return self._get_column_quantiles_mysql(column=column, quantiles=quantiles)
        else:
            return self._get_column_quantiles_generic_sqlalchemy(
                column=column,
                quantiles=quantiles,
                allow_relative_error=allow_relative_error,
            )

    def _get_column_quantiles_mssql(self, column: str, quantiles: Iterable) -> list:
        # mssql requires over(), so we add an empty over() clause
        selects: List[WithinGroup] = [
            sa.func.percentile_disc(quantile)
            .within_group(sa.column(column).asc())
            .over()
            for quantile in quantiles
        ]
        quantiles_query: Select = sa.select(selects).select_from(self._table)

        try:
            quantiles_results: RowProxy = self.engine.execute(
                quantiles_query
            ).fetchone()
            return list(quantiles_results)
        except ProgrammingError as pe:
            exception_message: str = "An SQL syntax Exception occurred."
            exception_traceback: str = traceback.format_exc()
            exception_message += f'{type(pe).__name__}: "{str(pe)}".  Traceback: "{exception_traceback}".'
            logger.error(exception_message)
            raise pe

    def _get_column_quantiles_bigquery(self, column: str, quantiles: Iterable) -> list:
        # BigQuery does not support "WITHIN", so we need a special case for it
        selects: List[WithinGroup] = [
            sa.func.percentile_disc(sa.column(column), quantile).over()
            for quantile in quantiles
        ]
        quantiles_query: Select = sa.select(selects).select_from(self._table)

        try:
            quantiles_results: RowProxy = self.engine.execute(
                quantiles_query
            ).fetchone()
            return list(quantiles_results)
        except ProgrammingError as pe:
            exception_message: str = "An SQL syntax Exception occurred."
            exception_traceback: str = traceback.format_exc()
            exception_message += f'{type(pe).__name__}: "{str(pe)}".  Traceback: "{exception_traceback}".'
            logger.error(exception_message)
            raise pe

    def _get_column_quantiles_mysql(self, column: str, quantiles: Iterable) -> list:
        # MySQL does not support "percentile_disc", so we implement it as a compound query.
        # Please see https://stackoverflow.com/questions/19770026/calculate-percentile-value-using-mysql for reference.
        percent_rank_query: CTE = sa.select(
            [
                sa.column(column),
                sa.cast(
                    sa.func.percent_rank().over(order_by=sa.column(column).asc()),
                    sa.dialects.mysql.DECIMAL(18, 15),
                ).label("p"),
            ]
        ).order_by(sa.column("p").asc()).select_from(self._table).cte("t")

        selects: List[WithinGroup] = []
        for idx, quantile in enumerate(quantiles):
            # pymysql cannot handle conversion of numpy float64 to float; convert just in case
            if np.issubdtype(type(quantile), np.float_):
                quantile = float(quantile)
            quantile_column: Label = sa.func.first_value(sa.column(column)).over(
                order_by=sa.case(
                    [
                        (
                            percent_rank_query.c.p
                            <= sa.cast(quantile, sa.dialects.mysql.DECIMAL(18, 15)),
                            percent_rank_query.c.p,
                        )
                    ],
                    else_=None,
                ).desc()
            ).label(f"q_{idx}")
            selects.append(quantile_column)
        quantiles_query: Select = sa.select(selects).distinct().order_by(
            percent_rank_query.c.p.desc()
        )

        try:
            quantiles_results: RowProxy = self.engine.execute(
                quantiles_query
            ).fetchone()
            return list(quantiles_results)
        except ProgrammingError as pe:
            exception_message: str = "An SQL syntax Exception occurred."
            exception_traceback: str = traceback.format_exc()
            exception_message += f'{type(pe).__name__}: "{str(pe)}".  Traceback: "{exception_traceback}".'
            logger.error(exception_message)
            raise pe

    # Support for computing the quantiles column for PostGreSQL and Redshift is included in the same method as that for
    # the generic sqlalchemy compatible DBMS engine, because users often use the postgresql driver to connect to Redshift
    # The key functional difference is that Redshift does not support the aggregate function
    # "percentile_disc", but does support the approximate percentile_disc or percentile_cont function version instead.```
    def _get_column_quantiles_generic_sqlalchemy(
        self, column: str, quantiles: Iterable, allow_relative_error: bool
    ) -> list:
        selects: List[WithinGroup] = [
            sa.func.percentile_disc(quantile).within_group(sa.column(column).asc())
            for quantile in quantiles
        ]
        quantiles_query: Select = sa.select(selects).select_from(self._table)

        try:
            quantiles_results: RowProxy = self.engine.execute(
                quantiles_query
            ).fetchone()
            return list(quantiles_results)
        except ProgrammingError:
            # ProgrammingError: (psycopg2.errors.SyntaxError) Aggregate function "percentile_disc" is not supported;
            # use approximate percentile_disc or percentile_cont instead.
            if self.attempt_allowing_relative_error():
                # Redshift does not have a percentile_disc method, but does support an approximate version.
                sql_approx: str = get_approximate_percentile_disc_sql(
                    selects=selects, sql_engine_dialect=self.sql_engine_dialect
                )
                selects_approx: List[TextClause] = [sa.text(sql_approx)]
                quantiles_query_approx: Select = sa.select(selects_approx).select_from(
                    self._table
                )
                if allow_relative_error:
                    try:
                        quantiles_results: RowProxy = self.engine.execute(
                            quantiles_query_approx
                        ).fetchone()
                        return list(quantiles_results)
                    except ProgrammingError as pe:
                        exception_message: str = "An SQL syntax Exception occurred."
                        exception_traceback: str = traceback.format_exc()
                        exception_message += f'{type(pe).__name__}: "{str(pe)}".  Traceback: "{exception_traceback}".'
                        logger.error(exception_message)
                        raise pe
                else:
                    raise ValueError(
                        f'The SQL engine dialect "{str(self.sql_engine_dialect)}" does not support computing quantiles '
                        "without approximation error; set allow_relative_error to True to allow approximate quantiles."
                    )
            else:
                raise ValueError(
                    f'The SQL engine dialect "{str(self.sql_engine_dialect)}" does not support computing quantiles with '
                    "approximation error; set allow_relative_error to False to disable approximate quantiles."
                )

    def get_column_stdev(self, column):
        if self.sql_engine_dialect.name.lower() == "mssql":
            # Note: "stdev_samp" is not a recognized built-in function name (but "stdev" does exist for "mssql").
            # This function is used to compute statistical standard deviation from sample data (per the reference in
            # https://sqlserverrider.wordpress.com/2013/03/06/standard-deviation-functions-stdev-and-stdevp-sql-server).
            res = self.engine.execute(
                sa.select([sa.func.stdev(sa.column(column))])
                .select_from(self._table)
                .where(sa.column(column) is not None)
            ).fetchone()
        else:
            res = self.engine.execute(
                sa.select([sa.func.stddev_samp(sa.column(column))])
                .select_from(self._table)
                .where(sa.column(column) is not None)
            ).fetchone()
        return float(res[0])

    def get_column_hist(self, column, bins):
        """return a list of counts corresponding to bins

        Args:
            column: the name of the column for which to get the histogram
            bins: tuple of bin edges for which to get histogram values; *must* be tuple to support caching
        """
        case_conditions = []
        idx = 0
        bins = list(bins)

        # If we have an infinte lower bound, don't express that in sql
        if (
            bins[0]
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=True
            )
        ) or (
            bins[0]
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=True
            )
        ):
            case_conditions.append(
                sa.func.sum(
                    sa.case([(sa.column(column) < bins[idx + 1], 1)], else_=0)
                ).label("bin_" + str(idx))
            )
            idx += 1

        for idx in range(idx, len(bins) - 2):
            case_conditions.append(
                sa.func.sum(
                    sa.case(
                        [
                            (
                                sa.and_(
                                    bins[idx] <= sa.column(column),
                                    sa.column(column) < bins[idx + 1],
                                ),
                                1,
                            )
                        ],
                        else_=0,
                    )
                ).label("bin_" + str(idx))
            )

        if (
            bins[-1]
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=False
            )
        ) or (
            bins[-1]
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=False
            )
        ):
            case_conditions.append(
                sa.func.sum(
                    sa.case([(bins[-2] <= sa.column(column), 1)], else_=0)
                ).label("bin_" + str(len(bins) - 1))
            )
        else:
            case_conditions.append(
                sa.func.sum(
                    sa.case(
                        [
                            (
                                sa.and_(
                                    bins[-2] <= sa.column(column),
                                    sa.column(column) <= bins[-1],
                                ),
                                1,
                            )
                        ],
                        else_=0,
                    )
                ).label("bin_" + str(len(bins) - 1))
            )

        query = (
            sa.select(case_conditions)
            .where(sa.column(column) != None,)
            .select_from(self._table)
        )

        # Run the data through convert_to_json_serializable to ensure we do not have Decimal types
        hist = convert_to_json_serializable(list(self.engine.execute(query).fetchone()))
        return hist

    def get_column_count_in_range(
        self, column, min_val=None, max_val=None, strict_min=False, strict_max=True
    ):
        if min_val is None and max_val is None:
            raise ValueError("Must specify either min or max value")
        if min_val is not None and max_val is not None and min_val > max_val:
            raise ValueError("Min value must be <= to max value")

        if (
            min_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=True
            )
        ) or (
            min_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=True
            )
        ):
            min_val = get_sql_dialect_floating_point_infinity_value(
                schema=self.sql_engine_dialect.name.lower(), negative=True
            )

        if (
            min_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=False
            )
        ) or (
            min_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=False
            )
        ):
            min_val = get_sql_dialect_floating_point_infinity_value(
                schema=self.sql_engine_dialect.name.lower(), negative=False
            )

        if (
            max_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=True
            )
        ) or (
            max_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=True
            )
        ):
            max_val = get_sql_dialect_floating_point_infinity_value(
                schema=self.sql_engine_dialect.name.lower(), negative=True
            )

        if (
            max_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_np", negative=False
            )
        ) or (
            max_val
            == get_sql_dialect_floating_point_infinity_value(
                schema="api_cast", negative=False
            )
        ):
            max_val = get_sql_dialect_floating_point_infinity_value(
                schema=self.sql_engine_dialect.name.lower(), negative=False
            )

        min_condition = None
        max_condition = None
        if min_val is not None:
            if strict_min:
                min_condition = sa.column(column) > min_val
            else:
                min_condition = sa.column(column) >= min_val
        if max_val is not None:
            if strict_max:
                max_condition = sa.column(column) < max_val
            else:
                max_condition = sa.column(column) <= max_val

        if min_condition is not None and max_condition is not None:
            condition = sa.and_(min_condition, max_condition)
        elif min_condition is not None:
            condition = min_condition
        else:
            condition = max_condition

        query = (
            sa.select([sa.func.count(sa.column(column))])
            .where(sa.and_(sa.column(column) != None, condition))
            .select_from(self._table)
        )

        return self.engine.execute(query).scalar()

    def create_temporary_table(self, table_name, custom_sql, schema_name=None):
        """
        Create Temporary table based on sql query. This will be used as a basis for executing expectations.
        WARNING: this feature is new in v0.4.
        It hasn't been tested in all SQL dialects, and may change based on community feedback.
        :param custom_sql:
        """

        ###
        # NOTE: 20200310 - The update to support snowflake transient table creation revealed several
        # import cases that are not fully handled.
        # The snowflake-related change updated behavior to allow both custom_sql and schema to be specified. But
        # the underlying incomplete handling of schema remains.
        #
        # Several cases we need to consider:
        #
        # 1. Distributed backends (e.g. Snowflake and BigQuery) often use a `<database>.<schema>.<table>`
        # syntax, but currently we are biased towards only allowing schema.table
        #
        # 2. In the wild, we see people using several ways to declare the schema they want to use:
        # a. In the connection string, the original RFC only specifies database, but schema is supported by some
        # backends (Snowflake) as a query parameter.
        # b. As a default for a user (the equivalent of USE SCHEMA being provided at the beginning of a session)
        # c. As part of individual queries.
        #
        # 3. We currently don't make it possible to select from a table in one query, but create a temporary table in
        # another schema, except for with BigQuery and (now) snowflake, where you can specify the table name (and
        # potentially triple of database, schema, table) in the batch_kwargs.
        #
        # The SqlAlchemyDataset interface essentially predates the batch_kwargs concept and so part of what's going
        # on, I think, is a mismatch between those. I think we should rename custom_sql -> "temp_table_query" or
        # similar, for example.
        ###

        if self.sql_engine_dialect.name.lower() == "bigquery":
            stmt = "CREATE OR REPLACE TABLE `{table_name}` AS {custom_sql}".format(
                table_name=table_name, custom_sql=custom_sql
            )
        elif self.sql_engine_dialect.name.lower() == "snowflake":
            table_type = "TEMPORARY" if self.generated_table_name else "TRANSIENT"

            logger.info("Creating temporary table %s" % table_name)
            if schema_name is not None:
                table_name = schema_name + "." + table_name
            stmt = "CREATE OR REPLACE {table_type} TABLE {table_name} AS {custom_sql}".format(
                table_type=table_type, table_name=table_name, custom_sql=custom_sql
            )
        elif self.sql_engine_dialect.name == "mysql":
            # Note: We can keep the "MySQL" clause separate for clarity, even though it is the same as the generic case.
            stmt = "CREATE TEMPORARY TABLE {table_name} AS {custom_sql}".format(
                table_name=table_name, custom_sql=custom_sql
            )
        elif self.sql_engine_dialect.name == "mssql":
            # Insert "into #{table_name}" in the custom sql query right before the "from" clause
            # Split is case sensitive so detect case.
            # Note: transforming custom_sql to uppercase/lowercase has uninteded consequences (i.e., changing column names), so this is not an option!
            if "from" in custom_sql:
                strsep = "from"
            else:
                strsep = "FROM"
            custom_sqlmod = custom_sql.split(strsep, maxsplit=1)
            stmt = (
                custom_sqlmod[0] + "into {table_name} from" + custom_sqlmod[1]
            ).format(table_name=table_name)
        elif self.sql_engine_dialect.name.lower() == "awsathena":
            stmt = "CREATE TABLE {table_name} AS {custom_sql}".format(
                table_name=table_name, custom_sql=custom_sql
            )
        else:
            stmt = 'CREATE TEMPORARY TABLE "{table_name}" AS {custom_sql}'.format(
                table_name=table_name, custom_sql=custom_sql
            )

        self.engine.execute(stmt)

    def column_reflection_fallback(self):
        """If we can't reflect the table, use a query to at least get column names."""
        col_info_dict_list: List[Dict]
        if self.sql_engine_dialect.name.lower() == "mssql":
            type_module = self._get_dialect_type_module()
            # Get column names and types from the database
            # StackOverflow to the rescue: https://stackoverflow.com/a/38634368
            col_info_query: TextClause = sa.text(
                f"""
SELECT
    cols.NAME, ty.NAME
FROM
    tempdb.sys.columns AS cols
JOIN
    sys.types AS ty
ON
    cols.user_type_id = ty.user_type_id
WHERE
    object_id = OBJECT_ID('tempdb..{self._table}')
                """
            )
            col_info_tuples_list = self.engine.execute(col_info_query).fetchall()
            col_info_dict_list = [
                {"name": col_name, "type": getattr(type_module, col_type.upper())()}
                for col_name, col_type in col_info_tuples_list
            ]
        else:
            query: Select = sa.select([sa.text("*")]).select_from(self._table).limit(1)
            col_names: list = self.engine.execute(query).keys()
            col_info_dict_list = [{"name": col_name} for col_name in col_names]
        return col_info_dict_list

    ###
    ###
    ###
    #
    # Table Expectation Implementations
    #
    ###
    ###
    ###

    # noinspection PyUnusedLocal
    @DocInherit
    @MetaSqlAlchemyDataset.expectation(["other_table_name"])
    def expect_table_row_count_to_equal_other_table(
        self,
        other_table_name,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        """Expect the number of rows in this table to equal the number of rows in a different table.

        expect_table_row_count_to_equal is a :func:`expectation \
        <great_expectations.data_asset.data_asset.DataAsset.expectation>`, not a
        ``column_map_expectation`` or ``column_aggregate_expectation``.

        Args:
            other_table_name (str): \
                The name of the other table to which to compare.

        Other Parameters:
            result_format (string or None): \
                Which output mode to use: `BOOLEAN_ONLY`, `BASIC`, `COMPLETE`, or `SUMMARY`.
                For more detail, see :ref:`result_format <result_format>`.
            include_config (boolean): \
                If True, then include the expectation config as part of the result object. \
                For more detail, see :ref:`include_config`.
            catch_exceptions (boolean or None): \
                If True, then catch exceptions and include them as part of the result object. \
                For more detail, see :ref:`catch_exceptions`.
            meta (dict or None): \
                A JSON-serializable dictionary (nesting allowed) that will be included in the output without \
                modification. For more detail, see :ref:`meta`.

        Returns:
           An ExpectationSuiteValidationResult

            Exact fields vary depending on the values passed to :ref:`result_format <result_format>` and
            :ref:`include_config`, :ref:`catch_exceptions`, and :ref:`meta`.

        See Also:
            expect_table_row_count_to_be_between
        """
        row_count = self.get_row_count()
        other_table_row_count = self.get_row_count(table_name=other_table_name)

        return {
            "success": row_count == other_table_row_count,
            "result": {
                "observed_value": {"self": row_count, "other": other_table_row_count,}
            },
        }

    ###
    ###
    ###
    #
    # Compound Column Expectation Implementations
    #
    ###
    ###
    ###

    @DocInherit
    @MetaSqlAlchemyDataset.expectation(["column_list", "ignore_row_if"])
    def expect_compound_columns_to_be_unique(
        self,
        column_list,
        ignore_row_if="all_values_are_missing",
        result_format=None,
        row_condition=None,
        condition_parser=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        columns = [
            sa.column(col["name"]) for col in self.columns if col["name"] in column_list
        ]
        query = (
            sa.select([sa.func.count()])
            .group_by(*columns)
            .having(sa.func.count() > 1)
            .select_from(self._table)
        )

        if ignore_row_if == "all_values_are_missing":
            query = query.where(sa.and_(*[col != None for col in columns]))
        elif ignore_row_if == "any_value_is_missing":
            query = query.where(sa.or_(*[col != None for col in columns]))
        elif ignore_row_if == "never":
            pass
        else:
            raise ValueError(
                "ignore_row_if was set to an unexpected value: %s" % ignore_row_if
            )

        unexpected_count = self.engine.execute(query).fetchone()

        if unexpected_count is None:
            # This can happen when the condition filters out all rows
            unexpected_count = 0
        else:
            unexpected_count = unexpected_count[0]

        total_count_query = sa.select([sa.func.count()]).select_from(self._table)
        total_count = self.engine.execute(total_count_query).fetchone()[0]

        return {
            "success": unexpected_count == 0,
            "result": {"unexpected_percent": 100.0 * unexpected_count / total_count},
        }

    ###
    ###
    ###
    #
    # Column Map Expectation Implementations
    #
    ###
    ###
    ###

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_be_null(
        self,
        column,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        return sa.column(column) == None

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_be_null(
        self,
        column,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):

        return sa.column(column) != None

    def _get_dialect_type_module(self):
        if self.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(
                self.sql_engine_dialect, sqlalchemy_redshift.dialect.RedshiftDialect
            ):
                return self.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(
                    self.sql_engine_dialect,
                    pybigquery.sqlalchemy_bigquery.BigQueryDialect,
                )
                and bigquery_types_tuple is not None
            ):
                return bigquery_types_tuple
        except (TypeError, AttributeError):
            pass

        return self.dialect

    @DocInherit
    @DataAsset.expectation(["column", "type_", "mostly"])
    def expect_column_values_to_be_of_type(
        self,
        column,
        type_,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if mostly is not None:
            raise ValueError(
                "SqlAlchemyDataset does not support column map semantics for column types"
            )

        try:
            col_data = [col for col in self.columns if col["name"] == column][0]
            col_type = type(col_data["type"])
        except IndexError:
            raise ValueError("Unrecognized column: %s" % column)
        except KeyError:
            raise ValueError("No database type data available for column: %s" % column)

        try:
            # Our goal is to be as explicit as possible. We will match the dialect
            # if that is possible. If there is no dialect available, we *will*
            # match against a top-level SqlAlchemy type if that's possible.
            #
            # This is intended to be a conservative approach.
            #
            # In particular, we *exclude* types that would be valid under an ORM
            # such as "float" for postgresql with this approach

            if type_ is None:
                # vacuously true
                success = True
            else:
                type_module = self._get_dialect_type_module()
                success = issubclass(col_type, getattr(type_module, type_))

            return {"success": success, "result": {"observed_value": col_type.__name__}}

        except AttributeError:
            raise ValueError("Type not recognized by current driver: %s" % type_)

    @DocInherit
    @DataAsset.expectation(["column", "type_list", "mostly"])
    def expect_column_values_to_be_in_type_list(
        self,
        column,
        type_list,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if mostly is not None:
            raise ValueError(
                "SqlAlchemyDataset does not support column map semantics for column types"
            )

        try:
            col_data = [col for col in self.columns if col["name"] == column][0]
            col_type = type(col_data["type"])
        except IndexError:
            raise ValueError("Unrecognized column: %s" % column)
        except KeyError:
            raise ValueError("No database type data available for column: %s" % column)

        # Our goal is to be as explicit as possible. We will match the dialect
        # if that is possible. If there is no dialect available, we *will*
        # match against a top-level SqlAlchemy type.
        #
        # This is intended to be a conservative approach.
        #
        # In particular, we *exclude* types that would be valid under an ORM
        # such as "float" for postgresql with this approach

        if type_list is None:
            success = True
        else:
            types = []
            type_module = self._get_dialect_type_module()
            for type_ in type_list:
                try:
                    type_class = getattr(type_module, type_)
                    types.append(type_class)
                except AttributeError:
                    logger.debug("Unrecognized type: %s" % type_)
            if len(types) == 0:
                logger.warning(
                    "No recognized sqlalchemy types in type_list for current dialect."
                )
            types = tuple(types)
            success = issubclass(col_type, types)

        return {"success": success, "result": {"observed_value": col_type.__name__}}

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_be_in_set(
        self,
        column,
        value_set,
        mostly=None,
        parse_strings_as_datetimes=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if value_set is None:
            # vacuously true
            return True

        if parse_strings_as_datetimes:
            parsed_value_set = self._parse_value_set(value_set)
        else:
            parsed_value_set = value_set
        return sa.column(column).in_(tuple(parsed_value_set))

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_be_in_set(
        self,
        column,
        value_set,
        mostly=None,
        parse_strings_as_datetimes=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if parse_strings_as_datetimes:
            parsed_value_set = self._parse_value_set(value_set)
        else:
            parsed_value_set = value_set
        return sa.column(column).notin_(tuple(parsed_value_set))

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_be_between(
        self,
        column,
        min_value=None,
        max_value=None,
        strict_min=False,
        strict_max=False,
        allow_cross_type_comparisons=None,
        parse_strings_as_datetimes=None,
        output_strftime_format=None,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if parse_strings_as_datetimes:
            if min_value:
                min_value = parse(min_value)

            if max_value:
                max_value = parse(max_value)

        if min_value is not None and max_value is not None and min_value > max_value:
            raise ValueError("min_value cannot be greater than max_value")

        if min_value is None and max_value is None:
            raise ValueError("min_value and max_value cannot both be None")

        if min_value is None:
            if strict_max:
                return sa.column(column) < max_value
            else:
                return sa.column(column) <= max_value

        elif max_value is None:
            if strict_min:
                return min_value < sa.column(column)
            else:
                return min_value <= sa.column(column)

        else:
            if strict_min and strict_max:
                return sa.and_(
                    min_value < sa.column(column), sa.column(column) < max_value
                )
            elif strict_min:
                return sa.and_(
                    min_value < sa.column(column), sa.column(column) <= max_value
                )
            elif strict_max:
                return sa.and_(
                    min_value <= sa.column(column), sa.column(column) < max_value
                )
            else:
                return sa.and_(
                    min_value <= sa.column(column), sa.column(column) <= max_value
                )

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_value_lengths_to_equal(
        self,
        column,
        value,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        return sa.func.length(sa.column(column)) == value

    @DocInherit
    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_value_lengths_to_be_between(
        self,
        column,
        min_value=None,
        max_value=None,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):

        if min_value is None and max_value is None:
            raise ValueError("min_value and max_value cannot both be None")

        # Assert that min_value and max_value are integers
        try:
            if min_value is not None and not float(min_value).is_integer():
                raise ValueError("min_value and max_value must be integers")

            if max_value is not None and not float(max_value).is_integer():
                raise ValueError("min_value and max_value must be integers")

        except ValueError:
            raise ValueError("min_value and max_value must be integers")

        if min_value is not None and max_value is not None:
            return sa.and_(
                sa.func.length(sa.column(column)) >= min_value,
                sa.func.length(sa.column(column)) <= max_value,
            )

        elif min_value is None and max_value is not None:
            return sa.func.length(sa.column(column)) <= max_value

        elif min_value is not None and max_value is None:
            return sa.func.length(sa.column(column)) >= min_value

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_be_unique(
        self,
        column,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        # Duplicates are found by filtering a group by query
        dup_query = (
            sa.select([sa.column(column)])
            .select_from(self._table)
            .group_by(sa.column(column))
            .having(sa.func.count(sa.column(column)) > 1)
        )

        return sa.column(column).notin_(dup_query)

    def _get_dialect_regex_expression(self, column, regex, positive=True):
        try:
            # postgres
            if isinstance(self.sql_engine_dialect, sa.dialects.postgresql.dialect):
                if positive:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("~")
                    )
                else:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("!~")
                    )
        except AttributeError:
            pass

        try:
            # redshift
            if isinstance(
                self.sql_engine_dialect, sqlalchemy_redshift.dialect.RedshiftDialect
            ):
                if positive:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("~")
                    )
                else:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("!~")
                    )
        except (
            AttributeError,
            TypeError,
        ):  # TypeError can occur if the driver was not installed and so is None
            pass

        try:
            # MySQL
            if isinstance(self.sql_engine_dialect, sa.dialects.mysql.dialect):
                if positive:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("REGEXP")
                    )
                else:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("NOT REGEXP")
                    )
        except AttributeError:
            pass

        try:
            # Snowflake
            if isinstance(
                self.sql_engine_dialect,
                snowflake.sqlalchemy.snowdialect.SnowflakeDialect,
            ):
                if positive:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("RLIKE")
                    )
                else:
                    return BinaryExpression(
                        sa.column(column), literal(regex), custom_op("NOT RLIKE")
                    )
        except (
            AttributeError,
            TypeError,
        ):  # TypeError can occur if the driver was not installed and so is None
            pass

        try:
            # Bigquery
            if isinstance(
                self.sql_engine_dialect, pybigquery.sqlalchemy_bigquery.BigQueryDialect
            ):
                if positive:
                    return sa.func.REGEXP_CONTAINS(sa.column(column), literal(regex))
                else:
                    return sa.not_(
                        sa.func.REGEXP_CONTAINS(sa.column(column), literal(regex))
                    )
        except (
            AttributeError,
            TypeError,
        ):  # TypeError can occur if the driver was not installed and so is None
            pass

        return None

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_match_regex(
        self,
        column,
        regex,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        regex_expression = self._get_dialect_regex_expression(column, regex)
        if regex_expression is None:
            logger.warning(
                "Regex is not supported for dialect %s" % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return regex_expression

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_match_regex(
        self,
        column,
        regex,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        regex_expression = self._get_dialect_regex_expression(
            column, regex, positive=False
        )
        if regex_expression is None:
            logger.warning(
                "Regex is not supported for dialect %s" % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return regex_expression

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_match_regex_list(
        self,
        column,
        regex_list,
        match_on="any",
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):

        if match_on not in ["any", "all"]:
            raise ValueError("match_on must be any or all")

        if len(regex_list) == 0:
            raise ValueError("At least one regex must be supplied in the regex_list.")

        regex_expression = self._get_dialect_regex_expression(column, regex_list[0])
        if regex_expression is None:
            logger.warning(
                "Regex is not supported for dialect %s" % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        if match_on == "any":
            condition = sa.or_(
                *[
                    self._get_dialect_regex_expression(column, regex)
                    for regex in regex_list
                ]
            )
        else:
            condition = sa.and_(
                *[
                    self._get_dialect_regex_expression(column, regex)
                    for regex in regex_list
                ]
            )
        return condition

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_match_regex_list(
        self,
        column,
        regex_list,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if len(regex_list) == 0:
            raise ValueError("At least one regex must be supplied in the regex_list.")

        regex_expression = self._get_dialect_regex_expression(
            column, regex_list[0], positive=False
        )
        if regex_expression is None:
            logger.warning(
                "Regex is not supported for dialect %s" % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return sa.and_(
            *[
                self._get_dialect_regex_expression(column, regex, positive=False)
                for regex in regex_list
            ]
        )

    def _get_dialect_like_pattern_expression(self, column, like_pattern, positive=True):
        dialect_supported: bool = False

        try:
            # Bigquery
            if isinstance(
                self.sql_engine_dialect, pybigquery.sqlalchemy_bigquery.BigQueryDialect
            ):
                dialect_supported = True
        except (
            AttributeError,
            TypeError,
        ):  # TypeError can occur if the driver was not installed and so is None
            pass

        if isinstance(
            self.sql_engine_dialect,
            (
                sa.dialects.sqlite.dialect,
                sa.dialects.postgresql.dialect,
                sa.dialects.mysql.dialect,
                sa.dialects.mssql.dialect,
            ),
        ):
            dialect_supported = True

        try:
            if isinstance(
                self.sql_engine_dialect, sqlalchemy_redshift.dialect.RedshiftDialect
            ):
                dialect_supported = True
        except (AttributeError, TypeError):
            pass

        if dialect_supported:
            try:
                if positive:
                    return sa.column(column).like(literal(like_pattern))
                else:
                    return sa.not_(sa.column(column).like(literal(like_pattern)))
            except AttributeError:
                pass

        return None

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_match_like_pattern(
        self,
        column,
        like_pattern,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        like_pattern_expression = self._get_dialect_like_pattern_expression(
            column, like_pattern
        )
        if like_pattern_expression is None:
            logger.warning(
                "Like patterns are not supported for dialect %s"
                % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return like_pattern_expression

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_match_like_pattern(
        self,
        column,
        like_pattern,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        like_pattern_expression = self._get_dialect_like_pattern_expression(
            column, like_pattern, positive=False
        )
        if like_pattern_expression is None:
            logger.warning(
                "Like patterns are not supported for dialect %s"
                % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return like_pattern_expression

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_match_like_pattern_list(
        self,
        column,
        like_pattern_list,
        match_on="any",
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):

        if match_on not in ["any", "all"]:
            raise ValueError("match_on must be any or all")

        if len(like_pattern_list) == 0:
            raise ValueError(
                "At least one like_pattern must be supplied in the like_pattern_list."
            )

        like_pattern_expression = self._get_dialect_like_pattern_expression(
            column, like_pattern_list[0]
        )
        if like_pattern_expression is None:
            logger.warning(
                "Like patterns are not supported for dialect %s"
                % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        if match_on == "any":
            condition = sa.or_(
                *[
                    self._get_dialect_like_pattern_expression(column, like_pattern)
                    for like_pattern in like_pattern_list
                ]
            )
        else:
            condition = sa.and_(
                *[
                    self._get_dialect_like_pattern_expression(column, like_pattern)
                    for like_pattern in like_pattern_list
                ]
            )
        return condition

    @MetaSqlAlchemyDataset.column_map_expectation
    def expect_column_values_to_not_match_like_pattern_list(
        self,
        column,
        like_pattern_list,
        mostly=None,
        result_format=None,
        include_config=True,
        catch_exceptions=None,
        meta=None,
    ):
        if len(like_pattern_list) == 0:
            raise ValueError(
                "At least one like_pattern must be supplied in the like_pattern_list."
            )

        like_pattern_expression = self._get_dialect_like_pattern_expression(
            column, like_pattern_list[0], positive=False
        )
        if like_pattern_expression is None:
            logger.warning(
                "Like patterns are not supported for dialect %s"
                % str(self.sql_engine_dialect)
            )
            raise NotImplementedError

        return sa.and_(
            *[
                self._get_dialect_like_pattern_expression(
                    column, like_pattern, positive=False
                )
                for like_pattern in like_pattern_list
            ]
        )
