SparkTable resource

SparkTableResource

SparkTableResource(
    paths,
    dataframes=None,
    spark_client=None,
    capture_schema_only=False,
    columns_description=None,
)

Bases: Resource

SparkTable resource reference wrapper.

This feature is currently in Beta and is subject to change. To activate this feature please contact your sales representative.

This resource wraps tables accessible using a spark session with optional metadata and versioning. You pass it as an argument of your Vectice Dataset wrapper before logging it to an iteration.

from vectice import SparkTableResource

db_resource = SparkTableResource(
    spark_client=spark,
    paths="my_table",
)

Vectice does not store your actual dataset. Instead, it stores your dataset's metadata, which is information about your dataset. These details are captured by resources tailored to the specific environment in use, such as: local (FileResource), Bigquery, S3, GCS...

Parameters:

Name	Type	Description	Default
paths	str | list[str]	The paths to retrieve the tables. Should be either the table name or the location of the table.	required
dataframes	Optional	The dataframes allowing vectice to optionally compute more metadata about this resource such as columns stats. (Support Pandas, Spark)	None
spark_client	Optional	The spark session allowing vectice to capture the table metadata.	None
capture_schema_only	Optional	A boolean parameter indicating whether to capture only the schema or both the schema and column statistics of the dataframes.	False
columns_description	Optional	A dictionary or path to a csv file to map the column's name to a specific description. Dictionary should follow the format { "column_name": "Description", ... }	None