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Terminator factory

Module with the factory pattern to return terminators.

TerminatorFactory

Bases: object

TerminatorFactory class following the factory pattern.

Source code in mkdocs/lakehouse_engine/packages/terminators/terminator_factory.py 
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class TerminatorFactory(object):
    """TerminatorFactory class following the factory pattern."""

    _logger = LoggingHandler(__name__).get_logger()

    @staticmethod
    def execute_terminator(
        spec: TerminatorSpec, df: Optional[DataFrame] = None
    ) -> None:
        """Execute a terminator following the factory pattern.

        Args:
            spec: terminator specification.
            df: dataframe to be used in the terminator. Needed when a
                terminator requires one dataframe as input.

        Returns:
            Transformer function to be executed in .transform() spark function.
        """
        if spec.function == "optimize_dataset":
            from lakehouse_engine.terminators.dataset_optimizer import DatasetOptimizer

            DatasetOptimizer.optimize_dataset(**spec.args)
        elif spec.function == "terminate_spark":
            from lakehouse_engine.terminators.spark_terminator import SparkTerminator

            SparkTerminator.terminate_spark()
        elif spec.function == "expose_cdf":
            from lakehouse_engine.terminators.cdf_processor import CDFProcessor

            CDFProcessor.expose_cdf(spec)
        elif spec.function == "notify":
            if not Notifier.check_if_notification_is_failure_notification(spec):
                notifier = NotifierFactory.get_notifier(spec)
                notifier.create_notification()
                notifier.send_notification()
        else:
            raise NotImplementedError(
                f"The requested terminator {spec.function} is not implemented."
            )

execute_terminator(spec, df=None) staticmethod

Execute a terminator following the factory pattern.

Parameters:

Name Type Description Default
spec TerminatorSpec

terminator specification.

 required
df Optional[DataFrame]

dataframe to be used in the terminator. Needed when a terminator requires one dataframe as input.

 None

Returns:

Type Description
None

Transformer function to be executed in .transform() spark function.
Source code in mkdocs/lakehouse_engine/packages/terminators/terminator_factory.py 
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@staticmethod
def execute_terminator(
    spec: TerminatorSpec, df: Optional[DataFrame] = None
) -> None:
    """Execute a terminator following the factory pattern.

    Args:
        spec: terminator specification.
        df: dataframe to be used in the terminator. Needed when a
            terminator requires one dataframe as input.

    Returns:
        Transformer function to be executed in .transform() spark function.
    """
    if spec.function == "optimize_dataset":
        from lakehouse_engine.terminators.dataset_optimizer import DatasetOptimizer

        DatasetOptimizer.optimize_dataset(**spec.args)
    elif spec.function == "terminate_spark":
        from lakehouse_engine.terminators.spark_terminator import SparkTerminator

        SparkTerminator.terminate_spark()
    elif spec.function == "expose_cdf":
        from lakehouse_engine.terminators.cdf_processor import CDFProcessor

        CDFProcessor.expose_cdf(spec)
    elif spec.function == "notify":
        if not Notifier.check_if_notification_is_failure_notification(spec):
            notifier = NotifierFactory.get_notifier(spec)
            notifier.create_notification()
            notifier.send_notification()
    else:
        raise NotImplementedError(
            f"The requested terminator {spec.function} is not implemented."
        )