BaseDataSink

laktory.models.datasinks.BaseDataSink

Bases: BaseModel, PipelineChild

Base class for data sinks

PARAMETER	DESCRIPTION
as_stream	If True output DataFrame is written as Streaming DataFrame. If None, write mode is derived fromDataFrame. TYPE: bool | None | VariableType DEFAULT: None
checkpoint_path_	Path to which the checkpoint file for which a streaming dataframe should be written. TYPE: str | Path | VariableType DEFAULT: None
custom_writer	Custom writer that fully replaces Laktory's built-in write logic. Laktory manages the streaming query lifecycle (foreachBatch, trigger, checkpoint, start/await). Can be set as a plain string (func_name only) or a full CustomWriter object with func_name, func_args, and func_kwargs. Mutually exclusive with mode and merge_cdc_options. TYPE: CustomWriter | None | VariableType DEFAULT: None
databricks_data_profiling_config	Databricks Data Quality Monitor data profiling configuration TYPE: Literal[None] | VariableType DEFAULT: None
is_quarantine	Sink used to store quarantined results from a pipeline node expectations. TYPE: bool | VariableType DEFAULT: False
merge_cdc_options	Merge options to handle input DataFrames that are Change Data Capture (CDC). Only used when MERGE mode is selected. TYPE: DataSinkMergeCDCOptions | VariableType DEFAULT: None
metadata	Table and columns metadata. TYPE: Literal[None] | VariableType DEFAULT: None
mode	Write mode. Spark OVERWRITE: Overwrite existing data. APPEND: Append contents of this DataFrame to existing data. ERROR: Throw an exception if data already exists. IGNORE: Silently ignore this operation if data already exists. Spark Streaming APPEND: Only the new rows in the streaming DataFrame/Dataset will be written to the sink. COMPLETE: All the rows in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. UPDATE: Only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. Polars Delta OVERWRITE: Overwrite existing data. APPEND: Append contents of this DataFrame to existing data. ERROR: Throw an exception if data already exists. IGNORE: Silently ignore this operation if data already exists. Laktory MERGE: Append, update and optionally delete records. Only supported for DELTA format. Requires cdc specification. TYPE: Literal['COMPLETE', 'IGNORE', 'MERGE', 'ERRORIFEXISTS', 'OVERWRITE', 'UPDATE', 'ERROR', 'APPEND'] | None | VariableType DEFAULT: None
schema_definition	Explicit table schema used when creating the table. If not set, schema is inferred from the transformer output DataFrame. TYPE: DataFrameSchema | VariableType DEFAULT: None
type	Name of the data sink type TYPE: Literal['FILE', 'HIVE_METASTORE', 'UNITY_CATALOG'] | VariableType
writer_kwargs	Keyword arguments passed directly to dataframe backend writer. Passed to .options() method when using PySpark. TYPE: dict[str | VariableType, Any | VariableType] | VariableType DEFAULT: {}
writer_methods	DataFrame backend writer methods. TYPE: list[ReaderWriterMethod | VariableType] | VariableType DEFAULT: []

METHOD	DESCRIPTION
create	Initialize the sink if required.
is_streaming	Return True if the write should use Spark Structured Streaming.
purge	Delete sink data and checkpoints
read	Read dataframe from sink.
write	Write dataframe into sink.

ATTRIBUTE	DESCRIPTION
ldp_auto_cdc_flow_kwargs	Keyword arguments for dp.create_auto_cdc_flow function TYPE: dict[str, str]
sdp_pre_merge_view_name	SPD view applying node transformer prior to applying CDC changes.

ldp_auto_cdc_flow_kwargs property

Keyword arguments for dp.create_auto_cdc_flow function

sdp_pre_merge_view_name property

SPD view applying node transformer prior to applying CDC changes.

create(df=None)

Initialize the sink if required.

Some sinks (e.g., Unity Catalog or Delta tables) must exist before metadata can be applied or data can be written in append mode.

PARAMETER	DESCRIPTION
df	Input DataFrame that may be used during sink initialization. TYPE: DataFrame DEFAULT: None

RETURNS	DESCRIPTION
bool	True if the sink was created, False if it already existed or if creation is not required.

Notes

This method is intended to be overridden by subclasses to implement sink-specific initialization logic.

Source code in laktory/models/datasinks/basedatasink.py

def create(self, df: "AnyFrame" = None) -> bool:
    """
    Initialize the sink if required.

    Some sinks (e.g., Unity Catalog or Delta tables) must exist before
    metadata can be applied or data can be written in append mode.

    Parameters
    ----------
    df : DataFrame
        Input DataFrame that may be used during sink initialization.

    Returns
    -------
    bool
        True if the sink was created, False if it already existed or if
        creation is not required.

    Notes
    -----
    This method is intended to be overridden by subclasses to implement
    sink-specific initialization logic.
    """
    return False

is_streaming(df=None)

Return True if the write should use Spark Structured Streaming.

Resolution order: 1. If a Narwhals-wrapped PySpark DataFrame is provided, read its native isStreaming attribute. 2. Fall back to self.as_stream (explicit sink configuration). 3. Fall back to the parent node's source as_stream flag. 4. Default to False (static write).

If both the DataFrame state and the configuration are set and they disagree, a TypeError is raised to surface the misconfiguration early.

PARAMETER	DESCRIPTION
df	Optional Narwhals DataFrame or LazyFrame. Must be passed before calling .to_native() so that the Narwhals implementation attribute is still available. DEFAULT: None

Source code in laktory/models/datasinks/basedatasink.py

def is_streaming(self, df=None) -> bool:
    """
    Return `True` if the write should use Spark Structured Streaming.

    Resolution order:
    1. If a Narwhals-wrapped PySpark DataFrame is provided, read its native
       ``isStreaming`` attribute.
    2. Fall back to ``self.as_stream`` (explicit sink configuration).
    3. Fall back to the parent node's source ``as_stream`` flag.
    4. Default to ``False`` (static write).

    If both the DataFrame state and the configuration are set and they
    disagree, a ``TypeError`` is raised to surface the misconfiguration
    early.

    Parameters
    ----------
    df:
        Optional Narwhals DataFrame or LazyFrame. Must be passed before
        calling ``.to_native()`` so that the Narwhals ``implementation``
        attribute is still available.
    """
    # Check if DataFrame is streaming
    df_is_streaming = None
    if df is not None:
        df = nw.from_native(df)
        dataframe_backend = DataFrameBackends(df.implementation)
        if dataframe_backend == DataFrameBackends.PYSPARK:
            df_is_streaming = df.to_native().isStreaming

    # Check if configured as stream from writer or source
    configured_as_stream = self.as_stream
    if configured_as_stream is None:
        node = self.parent_pipeline_node
        if node is not None and node.sources:
            configured_as_stream = node.has_streaming_source

    # Resolve conflict
    if df_is_streaming is not None and configured_as_stream is not None:
        if df_is_streaming != configured_as_stream:
            if df_is_streaming:
                raise TypeError(
                    "Sink configured as static, but received dataframe is streaming."
                )
            else:
                raise TypeError(
                    "Sink configured as stream, but received dataframe is not streaming."
                )

    is_streaming = df_is_streaming or configured_as_stream or False

    return is_streaming

purge()

Delete sink data and checkpoints

Source code in laktory/models/datasinks/basedatasink.py

def purge(self):
    """
    Delete sink data and checkpoints
    """
    raise NotImplementedError()

read(as_stream=None, reader_kwargs=None, reader_methods=None)

Read dataframe from sink.

PARAMETER	DESCRIPTION
as_stream	If True, dataframe read as stream. DEFAULT: None
reader_kwargs	Keyword arguments passed to the dataframe backend reader. DEFAULT: None
reader_methods	DataFrame backend reader methods. DEFAULT: None

RETURNS	DESCRIPTION
AnyFrame	DataFrame

Source code in laktory/models/datasinks/basedatasink.py

def read(self, as_stream=None, reader_kwargs=None, reader_methods=None):
    """
    Read dataframe from sink.

    Parameters
    ----------
    as_stream:
        If `True`, dataframe read as stream.
    reader_kwargs:
        Keyword arguments passed to the dataframe backend reader.
    reader_methods:
        DataFrame backend reader methods.

    Returns
    -------
    AnyFrame
        DataFrame
    """
    return self.as_source(
        as_stream=as_stream,
        reader_kwargs=reader_kwargs,
        reader_methods=reader_methods,
    ).read()

write(df=None, view_definition=None, mode=None)

Write dataframe into sink.

PARAMETER	DESCRIPTION
df	Input dataframe. TYPE: AnyFrame DEFAULT: None
mode	Write mode overwrite of the sink default mode. TYPE: str DEFAULT: None
view_definition	View definition for table data sinks of VIEW type TYPE: str DEFAULT: None

Source code in laktory/models/datasinks/basedatasink.py

def write(
    self,
    df: AnyFrame = None,
    view_definition: str = None,
    mode: str = None,
) -> None:
    """
    Write dataframe into sink.

    Parameters
    ----------
    df:
        Input dataframe.
    mode:
        Write mode overwrite of the sink default mode.
    view_definition:
        View definition for table data sinks of `VIEW` type
    """

    logger.info("Write initiated.")

    if getattr(self, "table_type", None) == "VIEW":
        if view_definition is None:
            raise ValueError(f"`view_definition` for '{self._id}' is `None`")

        from laktory.models.dataframe.dataframeexpr import DataFrameExpr

        if not isinstance(view_definition, DataFrameExpr):
            view_definition = DataFrameExpr(expr=view_definition)

        if self.dataframe_backend == DataFrameBackends.PYSPARK:
            self._write_spark_view(view_definition)
        elif self.dataframe_backend == DataFrameBackends.POLARS:
            self._write_polars_view(view_definition)
        else:
            raise ValueError(
                f"DataFrame backend '{self.dataframe_backend}' is not supported"
            )
        return

    if not isinstance(df, (nw.DataFrame, nw.LazyFrame)):
        df = nw.from_native(df)
    self._update_backend_from_df(df)

    # Custom Writer
    if self.custom_writer:
        df_native = df.to_native()

        # Special Treatment for Spark Streaming
        if (
            self.dataframe_backend == DataFrameBackends.PYSPARK
            and self.is_streaming(df=df)
        ):
            if self.checkpoint_path is None:
                raise ValueError(
                    f"Checkpoint location not specified for sink '{self._id}'"
                )
            # Build context before the foreachBatch lambda so that _parent
            # references are captured while intact. Inside foreachBatch on
            # Databricks, the lambda closure is serialized via cloudpickle
            # and _parent attributes may not survive the round-trip.
            from laktory.models.laktorycontext import LaktoryContext

            _context = LaktoryContext(
                node=self.parent_pipeline_node,
                pipeline=self.parent_pipeline,
                sink=self,
            )
            query = (
                df_native.writeStream.foreachBatch(
                    lambda batch_df, _: self.custom_writer.execute(
                        batch_df, context=_context
                    )
                )
                .trigger(availableNow=True)
                .options(checkpointLocation=self.checkpoint_path)
                .start()
            )
            query.awaitTermination()

        else:
            self.custom_writer.execute(df)

        logger.info("Write completed.")
        return

    if mode is None:
        mode = self.mode

    self._validate_mode(mode, df)
    self._validate_format()

    if mode and mode.lower() == "merge":
        self.merge_cdc_options.execute(source=df)
        logger.info("Write completed.")
        return

    if self.dataframe_backend == DataFrameBackends.PYSPARK:
        self._write_spark(df=df, mode=mode)
    elif self.dataframe_backend == DataFrameBackends.POLARS:
        self._write_polars(df=df, mode=mode)
    else:
        raise ValueError(
            f"DataFrame backend '{self.dataframe_backend}' is not supported"
        )

    logger.info("Write completed.")