kedro_datasets.spark.SparkDataset¶

class kedro_datasets.spark.SparkDataset(*, filepath, file_format='parquet', load_args=None, save_args=None, version=None, credentials=None, metadata=None)[source]¶

SparkDataset loads and saves Spark dataframes.

Example usage for the YAML API:

weather:
  type: spark.SparkDataset
  filepath: s3a://your_bucket/data/01_raw/weather/*
  file_format: csv
  load_args:
    header: True
    inferSchema: True
  save_args:
    sep: '|'
    header: True

weather_with_schema:
  type: spark.SparkDataset
  filepath: s3a://your_bucket/data/01_raw/weather/*
  file_format: csv
  load_args:
    header: True
    schema:
      filepath: path/to/schema.json
  save_args:
    sep: '|'
    header: True

weather_cleaned:
  type: spark.SparkDataset
  filepath: data/02_intermediate/data.parquet
  file_format: parquet

Example usage for the Python API:

 from pyspark.sql import SparkSession
 from pyspark.sql.types import IntegerType, Row, StringType, StructField, StructType

 from kedro_datasets.spark import SparkDataset

 schema = StructType(
...     [StructField("name", StringType(), True), StructField("age", IntegerType(), True)]
... )

 data = [("Alex", 31), ("Bob", 12), ("Clarke", 65), ("Dave", 29)]

 spark_df = SparkSession.builder.getOrCreate().createDataFrame(data, schema)

 dataset = SparkDataset(filepath=tmp_path / "test_data")
 dataset.save(spark_df)
 reloaded = dataset.load()

 assert Row(name="Bob", age=12) in reloaded.take(4)

Attributes

`DEFAULT_LOAD_ARGS`
`DEFAULT_SAVE_ARGS`

Methods

`exists`()	Checks whether a dataset's output already exists by calling the provided _exists() method.
`from_config`(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
`load`()	Loads data by delegation to the provided load method.
`release`()	Release any cached data.
`resolve_load_version`()	Compute the version the dataset should be loaded with.
`resolve_save_version`()	Compute the version the dataset should be saved with.
`save`(data)	Saves data by delegation to the provided save method.
`to_config`()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}¶

DEFAULT_SAVE_ARGS: dict[str, Any] = {}¶

__init__(*, filepath, file_format='parquet', load_args=None, save_args=None, version=None, credentials=None, metadata=None)[source]¶

Creates a new instance of SparkDataset.

Parameters:

filepath (str) – Filepath in POSIX format to a Spark dataframe. When using Databricks specify filepath``s starting with ``/dbfs/.
file_format (str) – File format used during load and save operations. These are formats supported by the running SparkContext include parquet, csv, delta. For a list of supported formats please refer to Apache Spark documentation at https://spark.apache.org/docs/latest/sql-programming-guide.html
load_args (Optional[dict[str, Any]]) – Load args passed to Spark DataFrameReader load method. It is dependent on the selected file format. You can find a list of read options for each supported format in Spark DataFrame read documentation: https://spark.apache.org/docs/latest/api/python/getting_started/quickstart_df.html
save_args (Optional[dict[str, Any]]) – Save args passed to Spark DataFrame write options. Similar to load_args this is dependent on the selected file format. You can pass mode and partitionBy to specify your overwrite mode and partitioning respectively. You can find a list of options for each format in Spark DataFrame write documentation: https://spark.apache.org/docs/latest/api/python/getting_started/quickstart_df.html
version (Optional[Version]) – If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.
credentials (Optional[dict[str, Any]]) – Credentials to access the S3 bucket, such as key, secret, if filepath prefix is s3a:// or s3n://. Optional keyword arguments passed to hdfs.client.InsecureClient if filepath prefix is hdfs://. Ignored otherwise.
metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()[source]¶

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:: bool
Returns:: Flag indicating whether the output already exists.
Raises:: DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)[source]¶

Create a dataset instance using the configuration provided.

Parameters:

name (str) – Data set name.
config (dict[str, Any]) – Data set config dictionary.
load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.
save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()[source]¶

Loads data by delegation to the provided load method.

Return type:: DataFrame
Returns:: Data returned by the provided load method.
Raises:: DatasetError – When underlying load method raises error.

release()[source]¶

Release any cached data.

Raises:: DatasetError – when underlying release method raises error.
Return type:: None

resolve_load_version()[source]¶

Compute the version the dataset should be loaded with.

Return type:: Optional[str]

resolve_save_version()[source]¶

Compute the version the dataset should be saved with.

Return type:: Optional[str]

save(data)[source]¶

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame) – the value to be saved by provided save method.

Raises:

DatasetError – when underlying save method raises error.
FileNotFoundError – when save method got file instead of dir, on Windows.
NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()[source]¶

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:: dict[str, Any]
Returns:: A dictionary containing the dataset’s type and initialization arguments.