Great Expectations: How to access row of data in custom expectation with SQL data source

Question

I'm using Great Expectations library for data validation. I'm trying to create a custom expectation with SQL data source (using SqlAlchemyExecutionEngine) which takes some columns on the input and uses a custom method to calculate True/False based on these values. It works fine in sample test (using Pandas), but breaks down when using SQL. There is no data in the column_list variable, just some SQL Alchemy objects for data access.

I checked the documentation which mentions the problem (they say it's a feature) I'm facing. In the second option they mention possibility to execute SQL query in the expectation, but I don't need (nor want) this, I just need the data per row.

Here is the actual code:

"""
This is a template for creating custom MulticolumnMapExpectations.
For detailed instructions on how to use it, please see:
    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_multicolumn_map_expectations
"""

from typing import Optional

from great_expectations.core.expectation_configuration import ExpectationConfiguration
from great_expectations.compatibility.sqlalchemy import sqlalchemy as sa
from great_expectations.core.metric_domain_types import MetricDomainTypes
from great_expectations.exceptions import InvalidExpectationConfigurationError
from great_expectations.execution_engine import (
    PandasExecutionEngine,
    SparkDFExecutionEngine,
    SqlAlchemyExecutionEngine,
)
from great_expectations.expectations.expectation import MulticolumnMapExpectation
from great_expectations.expectations.metrics.map_metric_provider import (
    MulticolumnMapMetricProvider,
    multicolumn_condition_partial,
    # metric_partial
)

import pandas as pd


from d_dquality.expectations.helper_expect_seats import check_vehicle_seats


# This class defines a Metric to support your Expectation.
# For most MulticolumnMapExpectations, the main business logic for calculation will live in this class.
class MulticolumnValuesSensibleVehicleSeats(MulticolumnMapMetricProvider):
    # This is the id string that will be used to reference your metric.
    condition_metric_name = "multicolumn_values.sensible_vehicle_seats"
    # These point your metric at the provided keys to facilitate calculation
    condition_domain_keys = (
        "batch_id",
        "table",
        "column_list",
        "row_condition",
        "condition_parser",
        "ignore_row_if",
    )
    condition_value_keys = ()

    # This method implements the core logic for the PandasExecutionEngine
    @multicolumn_condition_partial(engine=PandasExecutionEngine)
    def _pandas(cls, column_list, **kwargs):
        _vehicle_type = column_list['vehicle_type'].values[0]
        _seats = column_list['seats'].values[0]
        result = check_vehicle_seats(vehicle_type=_vehicle_type, seats=_seats)
        return pd.Series([result])

    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
    @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)
    def _sqlalchemy(cls, column_list, **kwargs):
        breakpoint()
        return column_list[0].in_(['BUS'])


# This class defines the Expectation itself
class ExpectVehicleSeatsToBeSensible(MulticolumnMapExpectation):
    """Expect number of seats to be in certain range for given vehicle type."""

    # These examples will be shown in the public gallery.
    # They will also be executed as unit tests for your Expectation.
    examples = [
    {
        "data": {
            "vehicle_type": ['TRAILER'],
            "seats": [0]            
        },
        "only_for": ["pandas"],
        "tests": [
            {
                "title": "basic_positive_test",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {"column_list": ["vehicle_type", "seats"]},
                "out": {
                    "success": True,
                },
            }
        ],
    },
    {
        "data": {
            "vehicle_type": ['VEHICLE'],
            "seats": [5]         
        },
        "only_for": ["pandas"],
        "tests": [
            {
                "title": "basic_positive_test",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {"column_list": ["vehicle_type", "seats"]},
                "out": {
                    "success": True,
                },
            }
        ],
    },
    {
        "data": {
            "vehicle_type": ['VEHICLE'], 
            "seats": [0]   
        },
        "only_for": ["pandas"],
        "tests": [
            {
                "title": "basic_negative_test",
                "exact_match_out": False,
                "include_in_gallery": True,
                "in": {"column_list": ["vehicle_type", "seats"]},
                "out": {
                    "success": False,
                },
            }
        ],
    }
]

    # This is the id string of the Metric used by this Expectation.
    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
    map_metric = "multicolumn_values.sensible_vehicle_seats"

    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
    success_keys = (
        "column_list",
        "mostly",
    )

    # This dictionary contains default values for any parameters that should have default values
    default_kwarg_values = {}

    def validate_configuration(
        self, configuration: Optional[ExpectationConfiguration] = None
    ) -> None:
        """
        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
        necessary configuration arguments have been provided for the validation of the expectation.

        Args:
            configuration (OPTIONAL[ExpectationConfiguration]): \
                An optional Expectation Configuration entry that will be used to configure the expectation
        Returns:
            None. Raises InvalidExpectationConfigurationError if the config is not validated successfully
        """

        super().validate_configuration(configuration)
        configuration = configuration or self.configuration

        print("validation")
        print(configuration)
        # breakpoint()

        # # Check other things in configuration.kwargs and raise Exceptions if needed
        # try:
        #     assert (
        #         ...
        #     ), "message"
        #     assert (
        #         ...
        #     ), "message"
        # except AssertionError as e:
        #     raise InvalidExpectationConfigurationError(str(e))

    # This object contains metadata for display in the public Gallery
    library_metadata = {
        "tags": [],  # Tags for this Expectation in the Gallery
        "contributors": [  # Github handles for all contributors to this Expectation.
            "@your_name_here",  # Don't forget to add your github handle here!
        ],
    }


if __name__ == "__main__":
    ExpectVehicleSeatsToBeSensible().print_diagnostic_checklist()

Maybe I got this all wrong...anyway, I'd appreciate any advice.