TableDataSink

laktory.models.datasinks.TableDataSink

Bases: BaseDataSink

PARAMETER	DESCRIPTION
catalog_name	Sink table catalog name TYPE: str | 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_quality_monitor	Databricks Quality Monitor TYPE: Literal[None] | VariableType DEFAULT: None
format	Storage format for data table. TYPE: Literal['PARQUET', 'DELTA'] | VariableType DEFAULT: 'DELTA'
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: TableDataSinkMetadata | 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['UPDATE', 'ERRORIFEXISTS', 'OVERWRITE', 'COMPLETE', 'APPEND', 'MERGE', 'ERROR', 'IGNORE'] | 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
schema_name	Sink table schema name TYPE: str | None | VariableType DEFAULT: None
table_name	Sink table name. Also supports fully qualified name ({catalog}.{schema}.{table}). In this case, catalog_name and schema_name arguments are ignored. TYPE: str | VariableType
table_type	Type of table. 'TABLE' and 'VIEW' are currently supported. TYPE: Literal['TABLE', 'VIEW'] | VariableType DEFAULT: 'TABLE'
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
as_source	Generate a table data source with the same properties as the sink.
create	Creates an empty table with the expected schema if it does not already exist.
purge	Delete sink data and checkpoints
read	Read dataframe from sink.
write	Write dataframe into sink.

ATTRIBUTE	DESCRIPTION
dlt_apply_changes_kwargs	Keyword arguments for dlt.apply_changes function TYPE: dict[str, str]
dlt_pre_merge_view_name	DLT view applying node transformer prior to applying CDC changes.
full_name	Table full name {catalog_name}.{schema_name}.{table_name} TYPE: str

dlt_apply_changes_kwargs property

Keyword arguments for dlt.apply_changes function

dlt_pre_merge_view_name property

DLT view applying node transformer prior to applying CDC changes.

full_name property

Table full name {catalog_name}.{schema_name}.{table_name}

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

Generate a table data source with the same properties as the sink.

PARAMETER	DESCRIPTION
as_stream	If True, sink will be 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
TableDataSource	Table Data Source

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

def as_source(
    self, as_stream=None, reader_kwargs=None, reader_methods=None
) -> TableDataSource:
    """
    Generate a table data source with the same properties as the sink.

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

    Returns
    -------
    :
        Table Data Source
    """
    source = TableDataSource(
        catalog_name=self.catalog_name,
        table_name=self.table_name,
        schema_name=self.schema_name,
        type=self.type,
        dataframe_backend=self.dataframe_backend,
    )

    if as_stream:
        source.as_stream = as_stream
    if reader_kwargs:
        source.reader_kwargs.update(reader_kwargs)
    if reader_methods:
        source.reader_methods.extend(reader_methods)

    if self.dataframe_backend_:
        source.dataframe_backend_ = self.dataframe_backend_
    source.parent = self.parent

    return source

create(df=None)

Creates an empty table with the expected schema if it does not already exist.

Returns True if the table was created, False otherwise. Schema is taken from schema_definition if set, otherwise inferred from df.

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

def create(self, df=None) -> bool:
    """
    Creates an empty table with the expected schema if it does not already exist.

    Returns True if the table was created, False otherwise.
    Schema is taken from `schema_definition` if set, otherwise inferred from `df`.
    """
    logger.info(f"Table '{self.full_name}' creation initiated.")

    # Skip for views
    if self.table_type == "VIEW":
        logger.info("Table is view. Skipping.")
        return False

    if self.exists():
        logger.info("Table exists. Skipping.")
        return False

    self._update_backend_from_df(df)
    schema = self._get_create_schema(df)

    if schema is None:
        logger.info("Schema is empty and `df` is None. Skipping table.")
        return False

    logger.info(f"Creating empty table '{self.full_name}'.")
    if self.dataframe_backend == DataFrameBackends.PYSPARK:
        from laktory import get_spark_session

        spark = get_spark_session()

        kwargs = {}
        path = self.writer_kwargs.get("path", None)
        if path:
            kwargs["path"] = path

        df_empty = spark.createDataFrame(data=[], schema=schema)
        df_empty.write.format(self.format.lower()).mode("ignore").options(
            **kwargs
        ).saveAsTable(self.full_name)

    else:
        raise NotImplementedError(
            f"Table Data Sink for '{self.dataframe_backend}' is not yet supported."
        )

    logger.info(f"Table '{self.full_name}' creation completed.")

    return True

purge()

Delete sink data and checkpoints

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

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

    if self.dataframe_backend == DataFrameBackends.PYSPARK:
        from laktory import get_spark_session

        spark = get_spark_session()

        # Remove Data
        logger.info(
            f"Dropping {self.table_type} {self.full_name}",
        )
        spark.sql(f"DROP {self.table_type} IF EXISTS {self.full_name}")

        path = self.writer_kwargs.get("path", None)
        if path:
            path = Path(path)
            if path.exists():
                is_dir = path.is_dir()
                if is_dir:
                    logger.info(f"Deleting data dir {path}")
                    shutil.rmtree(path)
                else:
                    logger.info(f"Deleting data file {path}")
                    os.remove(path)

        # Remove Checkpoint
        self._purge_checkpoint()

    else:
        raise TypeError(
            f"DataFrame backend {self.dataframe_backend} is not supported."
        )

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 df_native.isStreaming
        ):
            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.")