flytekit.types.structured.bigquery

Directory

Classes

Variables

flytekit.types.structured.bigquery.ArrowToBQEncodingHandlers

Parameters

def ArrowToBQEncodingHandlers()

Extend this abstract class, implement the encode function, and register your concrete class with the StructuredDatasetTransformerEngine class in order for the core flytekit type engine to handle dataframe libraries. This is the encoding interface, meaning it is used when there is a Python value that the flytekit type engine is trying to convert into a Flyte Literal. For the other way, see the StructuredDatasetEncoder

Properties

Methods

encode()

def encode(
    ctx: flytekit.core.context_manager.FlyteContext,
    structured_dataset: flytekit.types.structured.structured_dataset.StructuredDataset,
    structured_dataset_type: flytekit.models.types.StructuredDatasetType,
) -> flytekit.models.literals.StructuredDataset

Even if the user code returns a plain dataframe instance, the dataset transformer engine will wrap the incoming dataframe with defaults set for that dataframe type. This simplifies this function’s interface as a lot of data that could be specified by the user using the

TODO: Do we need to add a flag to indicate if it was wrapped by the transformer or by the user?

StructuredDataset wrapper class used as input to this function - that is the user facing Python class. This function needs to return the IDL StructuredDataset.

Returns: This function should return a StructuredDataset literal object. Do not confuse this with the

flytekit.types.structured.bigquery.BQToArrowDecodingHandler

Parameters

def BQToArrowDecodingHandler()

Extend this abstract class, implement the decode function, and register your concrete class with the StructuredDatasetTransformerEngine class in order for the core flytekit type engine to handle dataframe libraries. This is the decoder interface, meaning it is used when there is a Flyte Literal value, and we have to get a Python value out of it. For the other way, see the StructuredDatasetEncoder

Properties

Methods

decode()

def decode(
    ctx: flytekit.core.context_manager.FlyteContext,
    flyte_value: flytekit.models.literals.StructuredDataset,
    current_task_metadata: flytekit.models.literals.StructuredDatasetMetadata,
) -> pyarrow.lib.Table

This is code that will be called by the dataset transformer engine to ultimately translate from a Flyte Literal value into a Python instance.

of those dataframes.

Returns: This function can either return an instance of the dataframe that this decoder handles, or an iterator

flytekit.types.structured.bigquery.BQToPandasDecodingHandler

Parameters

def BQToPandasDecodingHandler()

Extend this abstract class, implement the decode function, and register your concrete class with the StructuredDatasetTransformerEngine class in order for the core flytekit type engine to handle dataframe libraries. This is the decoder interface, meaning it is used when there is a Flyte Literal value, and we have to get a Python value out of it. For the other way, see the StructuredDatasetEncoder

Properties

Methods

decode()

def decode(
    ctx: flytekit.core.context_manager.FlyteContext,
    flyte_value: flytekit.models.literals.StructuredDataset,
    current_task_metadata: flytekit.models.literals.StructuredDatasetMetadata,
) -> pandas.DataFrame

This is code that will be called by the dataset transformer engine to ultimately translate from a Flyte Literal value into a Python instance.

of those dataframes.

Returns: This function can either return an instance of the dataframe that this decoder handles, or an iterator

flytekit.types.structured.bigquery.PandasToBQEncodingHandlers

Parameters

def PandasToBQEncodingHandlers()

Extend this abstract class, implement the encode function, and register your concrete class with the StructuredDatasetTransformerEngine class in order for the core flytekit type engine to handle dataframe libraries. This is the encoding interface, meaning it is used when there is a Python value that the flytekit type engine is trying to convert into a Flyte Literal. For the other way, see the StructuredDatasetEncoder

Properties

Methods

encode()

def encode(
    ctx: flytekit.core.context_manager.FlyteContext,
    structured_dataset: flytekit.types.structured.structured_dataset.StructuredDataset,
    structured_dataset_type: flytekit.models.types.StructuredDatasetType,
) -> flytekit.models.literals.StructuredDataset

Even if the user code returns a plain dataframe instance, the dataset transformer engine will wrap the incoming dataframe with defaults set for that dataframe type. This simplifies this function’s interface as a lot of data that could be specified by the user using the

TODO: Do we need to add a flag to indicate if it was wrapped by the transformer or by the user?

StructuredDataset wrapper class used as input to this function - that is the user facing Python class. This function needs to return the IDL StructuredDataset.

Returns: This function should return a StructuredDataset literal object. Do not confuse this with the