Transform Function

Parameters

When specifying a transform function, all of its parameters are automatically populated with the appropriate dataframes of type polars.LazyFrame.

# input is of type LazyFrame
def transform(input):
    ...

Return Value

A transform function must return a value. The return type can be one of the following:

• polars.LazyFrame

• polars.DataFrame

• pandas.DataFrame

The recommended return type is polars.LazyFrame.

A LazyFrame can be optimized by the query planner and can leverage the streaming engine to perform out-of-core computations. This results in significantly faster execution compared to the immediate mode of a polars.DataFrame, and is orders of magnitude faster than pandas.DataFrame.

Additionally, DataSpace uses the query plan of the LazyFrame to deduce column lineage, something that’s only possible when returning a LazyFrame.

Metadata

Input parameters also expose a special attribute called ds_meta. This attribute contains metadata about the DataSnapshot being used. The following fields are available through this attribute:

Attribute	Type	Description
transform_id	str	The Transform ID of the dataframe
artifact_dir	str	The directory path where the artifacts are stored
data_snapshot_id	str	The DataSnapshot ID of the dataframe
build_id	str	The Build ID of the dataframe
row_count	int	Number of rows
column_count	int	Number of columns
file_size	int	The size of the parquet file
creation_date	str	The date when the dataset was created
columns	list	The columns of the dataframe

This is useful when pulling artifacts from upstream transforms. In this case, you can specify the upstream transform and call the artifact_dir:

def transform(excel_ingest):
    artifact_path = excel_ingest.ds_meta.artifact_dir

Environment Variables

DataSpace injects certain system environment variables to communicate with the runner on where to store certain files.

Name	Description
TRANSFORM_ID	The transformId of the current transform
ARTIFACT_FOLDER	The artifact folder of the current build. Should be used to persist artifacts after build
META_FOLDER	The meta folder of the current build. Will be populated with metadata about the dataset if generated
DATASET_FOLDER	The dataset folder of the current build. Will be populated with the generated parquet file