datahub/metadata-ingestion-modules/gx-plugin/src/datahub_gx_plugin/action.py

import json
import logging
import sys
import time
from dataclasses import dataclass
from datahub.utilities._markupsafe_compat import MARKUPSAFE_PATCHED
from datetime import timezone
from decimal import Decimal
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union

import packaging.version
from great_expectations.checkpoint.actions import ValidationAction
from great_expectations.core.batch import Batch
from great_expectations.core.batch_spec import (
    RuntimeDataBatchSpec,
    RuntimeQueryBatchSpec,
    SqlAlchemyDatasourceBatchSpec,
)
from great_expectations.core.expectation_validation_result import (
    ExpectationSuiteValidationResult,
)
from great_expectations.data_asset.data_asset import DataAsset
from great_expectations.data_context import AbstractDataContext
from great_expectations.data_context.types.resource_identifiers import (
    ExpectationSuiteIdentifier,
    ValidationResultIdentifier,
)
from great_expectations.execution_engine import PandasExecutionEngine
from great_expectations.execution_engine.sqlalchemy_execution_engine import (
    SqlAlchemyExecutionEngine,
)
from great_expectations.validator.validator import Validator
from sqlalchemy.engine.base import Connection, Engine
from sqlalchemy.engine.url import make_url

import datahub.emitter.mce_builder as builder
from datahub.cli.env_utils import get_boolean_env_variable
from datahub.emitter.mcp import MetadataChangeProposalWrapper
from datahub.emitter.rest_emitter import DatahubRestEmitter
from datahub.emitter.serialization_helper import pre_json_transform
from datahub.ingestion.graph.config import ClientMode
from datahub.ingestion.source.sql.sqlalchemy_uri_mapper import (
    get_platform_from_sqlalchemy_uri,
)
from datahub.metadata.com.linkedin.pegasus2avro.assertion import (
    AssertionInfo,
    AssertionResult,
    AssertionResultType,
    AssertionRunEvent,
    AssertionRunStatus,
    AssertionStdAggregation,
    AssertionStdOperator,
    AssertionStdParameter,
    AssertionStdParameters,
    AssertionStdParameterType,
    AssertionType,
    BatchSpec,
    DatasetAssertionInfo,
    DatasetAssertionScope,
)
from datahub.metadata.com.linkedin.pegasus2avro.common import DataPlatformInstance
from datahub.metadata.schema_classes import PartitionSpecClass, PartitionTypeClass
from datahub.sql_parsing.sqlglot_lineage import create_lineage_sql_parsed_result
from datahub.utilities.urns.dataset_urn import DatasetUrn

# TODO: move this and version check used in tests to some common module
try:
    from great_expectations import __version__ as GX_VERSION  # type: ignore

    has_name_positional_arg = packaging.version.parse(
        GX_VERSION
    ) >= packaging.version.Version("0.18.14")
except Exception:
    has_name_positional_arg = False

if TYPE_CHECKING:
    from great_expectations.data_context.types.resource_identifiers import (
        GXCloudIdentifier,
    )

assert MARKUPSAFE_PATCHED
logger = logging.getLogger(__name__)
if get_boolean_env_variable("DATAHUB_DEBUG", False):
    handler = logging.StreamHandler(stream=sys.stdout)
    logger.addHandler(handler)
    logger.setLevel(logging.DEBUG)

GE_PLATFORM_NAME = "great-expectations"


class DataHubValidationAction(ValidationAction):
    def __init__(
        self,
        data_context: AbstractDataContext,
        # this would capture `name` positional arg added in GX 0.18.14
        *args: Union[str, Any],
        server_url: str,
        env: str = builder.DEFAULT_ENV,
        platform_alias: Optional[str] = None,
        platform_instance_map: Optional[Dict[str, str]] = None,
        graceful_exceptions: bool = True,
        token: Optional[str] = None,
        timeout_sec: Optional[float] = None,
        retry_status_codes: Optional[List[int]] = None,
        retry_max_times: Optional[int] = None,
        extra_headers: Optional[Dict[str, str]] = None,
        exclude_dbname: Optional[bool] = None,
        parse_table_names_from_sql: bool = False,
        convert_urns_to_lowercase: bool = False,
        name: str = "DataHubValidationAction",
    ):
        if has_name_positional_arg:
            if len(args) >= 1 and isinstance(args[0], str):
                name = args[0]
            super().__init__(data_context, name)
        else:
            super().__init__(data_context)
        self.server_url = server_url
        self.env = env
        self.platform_alias = platform_alias
        self.platform_instance_map = platform_instance_map
        self.graceful_exceptions = graceful_exceptions
        self.token = token
        self.timeout_sec = timeout_sec
        self.retry_status_codes = retry_status_codes
        self.retry_max_times = retry_max_times
        self.extra_headers = extra_headers
        self.exclude_dbname = exclude_dbname
        self.parse_table_names_from_sql = parse_table_names_from_sql
        self.convert_urns_to_lowercase = convert_urns_to_lowercase

    def _run(
        self,
        validation_result_suite: ExpectationSuiteValidationResult,
        validation_result_suite_identifier: Union[
            ValidationResultIdentifier, "GXCloudIdentifier"
        ],
        data_asset: Union[Validator, DataAsset, Batch],
        payload: Optional[Any] = None,
        expectation_suite_identifier: Optional[ExpectationSuiteIdentifier] = None,
        checkpoint_identifier: Optional[Any] = None,
    ) -> Dict:
        datasets = []
        try:
            emitter = DatahubRestEmitter(
                gms_server=self.server_url,
                token=self.token,
                read_timeout_sec=self.timeout_sec,
                connect_timeout_sec=self.timeout_sec,
                retry_status_codes=self.retry_status_codes,
                retry_max_times=self.retry_max_times,
                extra_headers=self.extra_headers,
                client_mode=ClientMode.INGESTION,
                datahub_component="gx-plugin",
            )

            expectation_suite_name = validation_result_suite.meta.get(
                "expectation_suite_name"
            )
            run_id = validation_result_suite.meta.get("run_id")
            if hasattr(data_asset, "active_batch_id"):
                batch_identifier = data_asset.active_batch_id
            else:
                batch_identifier = data_asset.batch_id

            if isinstance(
                validation_result_suite_identifier, ValidationResultIdentifier
            ):
                expectation_suite_name = validation_result_suite_identifier.expectation_suite_identifier.expectation_suite_name
                run_id = validation_result_suite_identifier.run_id
                batch_identifier = validation_result_suite_identifier.batch_identifier

            # Returns datasets and corresponding batch requests
            datasets = self.get_dataset_partitions(batch_identifier, data_asset)

            if len(datasets) == 0 or datasets[0]["dataset_urn"] is None:
                warn("Metadata not sent to datahub. No datasets found.")
                return {"datahub_notification_result": "none required"}

            # Returns assertion info and assertion results
            assertions = self.get_assertions_with_results(
                validation_result_suite,
                expectation_suite_name,
                run_id,
                payload,
                datasets,
            )

            logger.info("Sending metadata to datahub ...")
            logger.info("Dataset URN - {urn}".format(urn=datasets[0]["dataset_urn"]))

            for assertion in assertions:
                logger.info(
                    "Assertion URN - {urn}".format(urn=assertion["assertionUrn"])
                )

                # Construct a MetadataChangeProposalWrapper object.
                assertion_info_mcp = MetadataChangeProposalWrapper(
                    entityUrn=assertion["assertionUrn"],
                    aspect=assertion["assertionInfo"],
                )
                emitter.emit_mcp(assertion_info_mcp)

                # Construct a MetadataChangeProposalWrapper object.
                assertion_platform_mcp = MetadataChangeProposalWrapper(
                    entityUrn=assertion["assertionUrn"],
                    aspect=assertion["assertionPlatform"],
                )
                emitter.emit_mcp(assertion_platform_mcp)

                for assertionResult in assertion["assertionResults"]:
                    dataset_assertionResult_mcp = MetadataChangeProposalWrapper(
                        entityUrn=assertionResult.assertionUrn,
                        aspect=assertionResult,
                    )

                    # Emit Result! (timeseries aspect)
                    emitter.emit_mcp(dataset_assertionResult_mcp)
            logger.info("Metadata sent to datahub.")
            result = "DataHub notification succeeded"
        except Exception as e:
            result = "DataHub notification failed"
            if self.graceful_exceptions:
                logger.error(e)
                logger.info("Suppressing error because graceful_exceptions is set")
            else:
                raise

        return {"datahub_notification_result": result}

    def get_assertions_with_results(
        self,
        validation_result_suite,
        expectation_suite_name,
        run_id,
        payload,
        datasets,
    ):
        dataPlatformInstance = DataPlatformInstance(
            platform=builder.make_data_platform_urn(GE_PLATFORM_NAME)
        )
        docs_link = None
        if payload:
            # process the payload
            for action_names in payload.keys():
                if payload[action_names]["class"] == "UpdateDataDocsAction":
                    data_docs_pages = payload[action_names]
                    for docs_link_key, docs_link_val in data_docs_pages.items():
                        if "file://" not in docs_link_val and docs_link_key != "class":
                            docs_link = docs_link_val

        assertions_with_results = []
        for result in validation_result_suite.results:
            expectation_config = result["expectation_config"]
            expectation_type = expectation_config["expectation_type"]
            success = bool(result["success"])
            kwargs = {
                k: v for k, v in expectation_config["kwargs"].items() if k != "batch_id"
            }

            result = result["result"]
            assertion_datasets = [d["dataset_urn"] for d in datasets]
            if len(datasets) == 1 and "column" in kwargs:
                assertion_fields = [
                    builder.make_schema_field_urn(
                        datasets[0]["dataset_urn"], kwargs["column"]
                    )
                ]
            else:
                assertion_fields = None  # type:ignore

            # Be careful what fields to consider for creating assertion urn.
            # Any change in fields below would lead to a new assertion
            # FIXME - Currently, when using evaluation parameters, new assertion is
            # created when runtime resolved kwargs are different,
            # possibly for each validation run
            assertionUrn = builder.make_assertion_urn(
                builder.datahub_guid(
                    pre_json_transform(
                        {
                            "platform": GE_PLATFORM_NAME,
                            "nativeType": expectation_type,
                            "nativeParameters": kwargs,
                            "dataset": assertion_datasets[0],
                            "fields": assertion_fields,
                        }
                    )
                )
            )
            logger.debug(
                "GE expectation_suite_name - {name}, expectation_type - {type}, Assertion URN - {urn}".format(
                    name=expectation_suite_name, type=expectation_type, urn=assertionUrn
                )
            )
            assertionInfo: AssertionInfo = self.get_assertion_info(
                expectation_type,
                kwargs,
                assertion_datasets[0],
                assertion_fields,
                expectation_suite_name,
            )

            # TODO: Understand why their run time is incorrect.
            run_time = run_id.run_time.astimezone(timezone.utc)
            evaluation_parameters = (
                {
                    k: convert_to_string(v)
                    for k, v in validation_result_suite.evaluation_parameters.items()
                    if k and v
                }
                if validation_result_suite.evaluation_parameters
                else None
            )

            nativeResults = {
                k: convert_to_string(v)
                for k, v in result.items()
                if (
                    k
                    in [
                        "observed_value",
                        "partial_unexpected_list",
                        "partial_unexpected_counts",
                        "details",
                    ]
                    and v
                )
            }

            actualAggValue = (
                result.get("observed_value")
                if isinstance(result.get("observed_value"), (int, float))
                else None
            )

            ds = datasets[0]
            # https://docs.greatexpectations.io/docs/reference/expectations/result_format/
            assertionResult = AssertionRunEvent(
                timestampMillis=int(round(time.time() * 1000)),
                assertionUrn=assertionUrn,
                asserteeUrn=ds["dataset_urn"],
                runId=run_time.strftime("%Y-%m-%dT%H:%M:%SZ"),
                result=AssertionResult(
                    type=(
                        AssertionResultType.SUCCESS
                        if success
                        else AssertionResultType.FAILURE
                    ),
                    rowCount=parse_int_or_default(result.get("element_count")),
                    missingCount=parse_int_or_default(result.get("missing_count")),
                    unexpectedCount=parse_int_or_default(
                        result.get("unexpected_count")
                    ),
                    actualAggValue=actualAggValue,
                    externalUrl=docs_link,
                    nativeResults=nativeResults,
                ),
                batchSpec=ds["batchSpec"],
                status=AssertionRunStatus.COMPLETE,
                runtimeContext=evaluation_parameters,
            )
            if ds.get("partitionSpec") is not None:
                assertionResult.partitionSpec = ds.get("partitionSpec")
            assertionResults = [assertionResult]
            assertions_with_results.append(
                {
                    "assertionUrn": assertionUrn,
                    "assertionInfo": assertionInfo,
                    "assertionPlatform": dataPlatformInstance,
                    "assertionResults": assertionResults,
                }
            )
        return assertions_with_results

    def get_assertion_info(
        self, expectation_type, kwargs, dataset, fields, expectation_suite_name
    ):
        # TODO - can we find exact type of min and max value
        def get_min_max(kwargs, type=AssertionStdParameterType.UNKNOWN):
            return AssertionStdParameters(
                minValue=AssertionStdParameter(
                    value=convert_to_string(kwargs.get("min_value")),
                    type=type,
                ),
                maxValue=AssertionStdParameter(
                    value=convert_to_string(kwargs.get("max_value")),
                    type=type,
                ),
            )

        known_expectations: Dict[str, DataHubStdAssertion] = {
            # column aggregate expectations
            "expect_column_min_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.MIN,
                parameters=get_min_max(kwargs),
            ),
            "expect_column_max_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.MAX,
                parameters=get_min_max(kwargs),
            ),
            "expect_column_median_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.MEDIAN,
                parameters=get_min_max(kwargs),
            ),
            "expect_column_stdev_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.STDDEV,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_column_mean_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.MEAN,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_column_unique_value_count_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.UNIQUE_COUNT,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_column_proportion_of_unique_values_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.UNIQUE_PROPOTION,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_column_sum_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.SUM,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_column_quantile_values_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation._NATIVE_,
            ),
            # column map expectations
            "expect_column_values_to_not_be_null": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.NOT_NULL,
                aggregation=AssertionStdAggregation.IDENTITY,
            ),
            "expect_column_values_to_be_in_set": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.IN,
                aggregation=AssertionStdAggregation.IDENTITY,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("value_set")),
                        type=AssertionStdParameterType.SET,
                    )
                ),
            ),
            "expect_column_values_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.IDENTITY,
                parameters=get_min_max(kwargs),
            ),
            "expect_column_values_to_match_regex": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.REGEX_MATCH,
                aggregation=AssertionStdAggregation.IDENTITY,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=kwargs.get("regex"),
                        type=AssertionStdParameterType.STRING,
                    )
                ),
            ),
            "expect_column_values_to_match_regex_list": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_COLUMN,
                operator=AssertionStdOperator.REGEX_MATCH,
                aggregation=AssertionStdAggregation.IDENTITY,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("regex_list")),
                        type=AssertionStdParameterType.LIST,
                    )
                ),
            ),
            "expect_table_columns_to_match_ordered_list": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_SCHEMA,
                operator=AssertionStdOperator.EQUAL_TO,
                aggregation=AssertionStdAggregation.COLUMNS,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("column_list")),
                        type=AssertionStdParameterType.LIST,
                    )
                ),
            ),
            "expect_table_columns_to_match_set": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_SCHEMA,
                operator=AssertionStdOperator.EQUAL_TO,
                aggregation=AssertionStdAggregation.COLUMNS,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("column_set")),
                        type=AssertionStdParameterType.SET,
                    )
                ),
            ),
            "expect_table_column_count_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_SCHEMA,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.COLUMN_COUNT,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
            "expect_table_column_count_to_equal": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_SCHEMA,
                operator=AssertionStdOperator.EQUAL_TO,
                aggregation=AssertionStdAggregation.COLUMN_COUNT,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("value")),
                        type=AssertionStdParameterType.NUMBER,
                    )
                ),
            ),
            "expect_column_to_exist": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_SCHEMA,
                operator=AssertionStdOperator._NATIVE_,
                aggregation=AssertionStdAggregation._NATIVE_,
            ),
            "expect_table_row_count_to_equal": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_ROWS,
                operator=AssertionStdOperator.EQUAL_TO,
                aggregation=AssertionStdAggregation.ROW_COUNT,
                parameters=AssertionStdParameters(
                    value=AssertionStdParameter(
                        value=convert_to_string(kwargs.get("value")),
                        type=AssertionStdParameterType.NUMBER,
                    )
                ),
            ),
            "expect_table_row_count_to_be_between": DataHubStdAssertion(
                scope=DatasetAssertionScope.DATASET_ROWS,
                operator=AssertionStdOperator.BETWEEN,
                aggregation=AssertionStdAggregation.ROW_COUNT,
                parameters=get_min_max(kwargs, AssertionStdParameterType.NUMBER),
            ),
        }

        datasetAssertionInfo = DatasetAssertionInfo(
            dataset=dataset,
            fields=fields,
            operator=AssertionStdOperator._NATIVE_,
            aggregation=AssertionStdAggregation._NATIVE_,
            nativeType=expectation_type,
            nativeParameters={k: convert_to_string(v) for k, v in kwargs.items()},
            scope=DatasetAssertionScope.DATASET_ROWS,
        )

        if expectation_type in known_expectations.keys():
            assertion = known_expectations[expectation_type]
            datasetAssertionInfo.scope = assertion.scope
            datasetAssertionInfo.aggregation = assertion.aggregation
            datasetAssertionInfo.operator = assertion.operator
            datasetAssertionInfo.parameters = assertion.parameters

        # Heuristically mapping other expectations
        else:
            if "column" in kwargs and expectation_type.startswith(
                "expect_column_value"
            ):
                datasetAssertionInfo.scope = DatasetAssertionScope.DATASET_COLUMN
                datasetAssertionInfo.aggregation = AssertionStdAggregation.IDENTITY
            elif "column" in kwargs:
                datasetAssertionInfo.scope = DatasetAssertionScope.DATASET_COLUMN
                datasetAssertionInfo.aggregation = AssertionStdAggregation._NATIVE_

        return AssertionInfo(
            type=AssertionType.DATASET,
            datasetAssertion=datasetAssertionInfo,
            customProperties={"expectation_suite_name": expectation_suite_name},
        )

    def get_dataset_partitions(self, batch_identifier, data_asset):
        dataset_partitions = []

        logger.debug("Finding datasets being validated")

        # for now, we support only v3-api and sqlalchemy execution engine and Pandas engine
        is_sql_alchemy = isinstance(data_asset, Validator) and (
            isinstance(data_asset.execution_engine, SqlAlchemyExecutionEngine)
        )
        is_pandas = isinstance(data_asset.execution_engine, PandasExecutionEngine)
        if is_sql_alchemy or is_pandas:
            ge_batch_spec = data_asset.active_batch_spec
            partitionSpec = None
            batchSpecProperties = {
                "data_asset_name": str(
                    data_asset.active_batch_definition.data_asset_name
                ),
                "datasource_name": str(
                    data_asset.active_batch_definition.datasource_name
                ),
            }
            sqlalchemy_uri = None
            if is_sql_alchemy and isinstance(
                data_asset.execution_engine.engine, Engine
            ):
                sqlalchemy_uri = data_asset.execution_engine.engine.url
            # For snowflake sqlalchemy_execution_engine.engine is actually instance of Connection
            elif is_sql_alchemy and isinstance(
                data_asset.execution_engine.engine, Connection
            ):
                sqlalchemy_uri = data_asset.execution_engine.engine.engine.url

            if isinstance(ge_batch_spec, SqlAlchemyDatasourceBatchSpec):
                # e.g. ConfiguredAssetSqlDataConnector with splitter_method or sampling_method
                schema_name = ge_batch_spec.get("schema_name")
                table_name = ge_batch_spec.get("table_name")

                dataset_urn = make_dataset_urn_from_sqlalchemy_uri(
                    sqlalchemy_uri,
                    schema_name,
                    table_name,
                    self.env,
                    self.get_platform_instance(
                        data_asset.active_batch_definition.datasource_name
                    ),
                    self.exclude_dbname,
                    self.platform_alias,
                    self.convert_urns_to_lowercase,
                )
                batchSpec = BatchSpec(
                    nativeBatchId=batch_identifier,
                    customProperties=batchSpecProperties,
                )

                splitter_method = ge_batch_spec.get("splitter_method")
                if (
                    splitter_method is not None
                    and splitter_method != "_split_on_whole_table"
                ):
                    batch_identifiers = ge_batch_spec.get("batch_identifiers", {})
                    partitionSpec = PartitionSpecClass(
                        partition=convert_to_string(batch_identifiers)
                    )
                sampling_method = ge_batch_spec.get("sampling_method", "")
                if sampling_method == "_sample_using_limit":
                    batchSpec.limit = ge_batch_spec["sampling_kwargs"]["n"]

                dataset_partitions.append(
                    {
                        "dataset_urn": dataset_urn,
                        "partitionSpec": partitionSpec,
                        "batchSpec": batchSpec,
                    }
                )
            elif isinstance(ge_batch_spec, RuntimeQueryBatchSpec):
                if not self.parse_table_names_from_sql:
                    warn(
                        "Enable parse_table_names_from_sql in DatahubValidationAction config\
                            to try to parse the tables being asserted from SQL query"
                    )
                    return []
                query = data_asset.batches[
                    batch_identifier
                ].batch_request.runtime_parameters["query"]
                partitionSpec = PartitionSpecClass(
                    type=PartitionTypeClass.QUERY,
                    partition=f"Query_{builder.datahub_guid(pre_json_transform(query))}",
                )

                batchSpec = BatchSpec(
                    nativeBatchId=batch_identifier,
                    query=query,
                    customProperties=batchSpecProperties,
                )

                data_platform = get_platform_from_sqlalchemy_uri(str(sqlalchemy_uri))
                sql_parser_in_tables = create_lineage_sql_parsed_result(
                    query=query,
                    platform=data_platform,
                    env=self.env,
                    platform_instance=None,
                    default_db=None,
                )
                tables = [
                    DatasetUrn.from_string(table_urn).name
                    for table_urn in sql_parser_in_tables.in_tables
                ]
                if sql_parser_in_tables.debug_info.table_error:
                    logger.warning(
                        f"Sql parser failed on {query} with {sql_parser_in_tables.debug_info.table_error}"
                    )
                    tables = []

                if len(set(tables)) != 1:
                    warn(
                        "DataHubValidationAction does not support cross dataset assertions."
                    )
                    return []
                for table in tables:
                    dataset_urn = make_dataset_urn_from_sqlalchemy_uri(
                        sqlalchemy_uri,
                        None,
                        table,
                        self.env,
                        self.get_platform_instance(
                            data_asset.active_batch_definition.datasource_name
                        ),
                        self.exclude_dbname,
                        self.platform_alias,
                        self.convert_urns_to_lowercase,
                    )
                    dataset_partitions.append(
                        {
                            "dataset_urn": dataset_urn,
                            "partitionSpec": partitionSpec,
                            "batchSpec": batchSpec,
                        }
                    )
            elif isinstance(ge_batch_spec, RuntimeDataBatchSpec):
                data_platform = self.get_platform_instance(
                    data_asset.active_batch_definition.datasource_name
                )
                dataset_urn = builder.make_dataset_urn_with_platform_instance(
                    platform=(
                        data_platform
                        if self.platform_alias is None
                        else self.platform_alias
                    ),
                    name=data_asset.active_batch_definition.datasource_name,
                    platform_instance="",
                    env=self.env,
                )
                batchSpec = BatchSpec(
                    nativeBatchId=batch_identifier,
                    query="",
                    customProperties=batchSpecProperties,
                )
                dataset_partitions.append(
                    {
                        "dataset_urn": dataset_urn,
                        "partitionSpec": partitionSpec,
                        "batchSpec": batchSpec,
                    }
                )
            else:
                warn(
                    "DataHubValidationAction does not recognize this GE batch spec type- {batch_spec_type}.".format(
                        batch_spec_type=type(ge_batch_spec)
                    )
                )
        else:
            # TODO - v2-spec - SqlAlchemyDataset support
            warn(
                "DataHubValidationAction does not recognize this GE data asset type - {asset_type}. This is either using v2-api or execution engine other than sqlalchemy.".format(
                    asset_type=type(data_asset)
                )
            )

        return dataset_partitions

    def get_platform_instance(self, datasource_name):
        if self.platform_instance_map and datasource_name in self.platform_instance_map:
            return self.platform_instance_map[datasource_name]
        else:
            warn(
                f"Datasource {datasource_name} is not present in platform_instance_map"
            )
        return None


def parse_int_or_default(value, default_value=None):
    if value is None:
        return default_value
    else:
        return int(value)


def make_dataset_urn_from_sqlalchemy_uri(
    sqlalchemy_uri,
    schema_name,
    table_name,
    env,
    platform_instance=None,
    exclude_dbname=None,
    platform_alias=None,
    convert_urns_to_lowercase=False,
):
    data_platform = get_platform_from_sqlalchemy_uri(str(sqlalchemy_uri))
    url_instance = make_url(sqlalchemy_uri)

    if schema_name is None and "." in table_name:
        schema_name, table_name = table_name.split(".")[-2:]

    if data_platform in ["redshift", "postgres"]:
        schema_name = schema_name or "public"
        if url_instance.database is None:
            warn(
                f"DataHubValidationAction failed to locate database name for {data_platform}."
            )
            return None
        schema_name = (
            schema_name if exclude_dbname else f"{url_instance.database}.{schema_name}"
        )
    elif data_platform == "mssql":
        schema_name = schema_name or "dbo"
        if url_instance.database is None:
            warn(
                f"DataHubValidationAction failed to locate database name for {data_platform}."
            )
            return None
        schema_name = (
            schema_name if exclude_dbname else f"{url_instance.database}.{schema_name}"
        )
    elif data_platform in ["trino", "snowflake"]:
        if schema_name is None or url_instance.database is None:
            warn(
                "DataHubValidationAction failed to locate schema name and/or database name for {data_platform}.".format(
                    data_platform=data_platform
                )
            )
            return None
        # If data platform is snowflake, we artificially lowercase the Database name.
        # This is because DataHub also does this during ingestion.
        # Ref: https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/src/datahub/ingestion/source/snowflake/snowflake_utils.py#L155
        database_name = (
            url_instance.database.lower()
            if data_platform == "snowflake"
            else url_instance.database
        )
        if database_name.endswith(f"/{schema_name}"):
            database_name = database_name[: -len(f"/{schema_name}")]
        schema_name = (
            schema_name if exclude_dbname else f"{database_name}.{schema_name}"
        )

    elif data_platform == "bigquery":
        if url_instance.host is None or url_instance.database is None:
            warn(
                "DataHubValidationAction failed to locate host and/or database name for {data_platform}. ".format(
                    data_platform=data_platform
                )
            )
            return None
        schema_name = f"{url_instance.host}.{url_instance.database}"

    schema_name = schema_name or url_instance.database
    if schema_name is None:
        warn(
            f"DataHubValidationAction failed to locate schema name for {data_platform}."
        )
        return None

    dataset_name = f"{schema_name}.{table_name}"

    if convert_urns_to_lowercase:
        dataset_name = dataset_name.lower()

    dataset_urn = builder.make_dataset_urn_with_platform_instance(
        platform=data_platform if platform_alias is None else platform_alias,
        name=dataset_name,
        platform_instance=platform_instance,
        env=env,
    )

    return dataset_urn


@dataclass
class DataHubStdAssertion:
    scope: Union[str, DatasetAssertionScope]
    operator: Union[str, AssertionStdOperator]
    aggregation: Union[str, AssertionStdAggregation]
    parameters: Optional[AssertionStdParameters] = None


class DecimalEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, Decimal):
            return str(o)
        return super().default(o)


def convert_to_string(var: Any) -> str:
    try:
        tmp = (
            str(var)
            if isinstance(var, (str, int, float))
            else json.dumps(var, cls=DecimalEncoder)
        )
    except TypeError as e:
        logger.debug(e)
        tmp = str(var)
    return tmp


def warn(msg):
    logger.warning(msg)