kedro_datasets.spark.SparkJDBCDataset

class kedro_datasets.spark.SparkJDBCDataset(*, url, table, credentials=None, load_args=None, save_args=None, metadata=None)

SparkJDBCDataset loads data from a database table accessible via JDBC URL url and connection properties and saves the content of a PySpark DataFrame to an external database table via JDBC. It uses pyspark.sql.DataFrameReader and pyspark.sql.DataFrameWriter internally, so it supports all allowed PySpark options on jdbc.

Example usage for the YAML API:

weather:
  type: spark.SparkJDBCDataset
  table: weather_table
  url: jdbc:postgresql://localhost/test
  credentials: db_credentials
  load_args:
    properties:
      driver: org.postgresql.Driver
  save_args:
    properties:
      driver: org.postgresql.Driver

Example usage for the Python API:

 import pandas as pd
 from kedro_datasets.spark import SparkJDBCDataset
 from pyspark.sql import SparkSession

 spark = SparkSession.builder.getOrCreate()
 data = spark.createDataFrame(
...     pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
... )
 url = "jdbc:postgresql://localhost/test"
 table = "table_a"
 connection_properties = {"driver": "org.postgresql.Driver"}
 dataset = SparkJDBCDataset(
...     url=url,
...     table=table,
...     credentials={"user": "scott", "password": "tiger"},
...     load_args={"properties": connection_properties},
...     save_args={"properties": connection_properties},
... )

 dataset.save(data)
 reloaded = dataset.load()

 assert data.toPandas().equals(reloaded.toPandas())

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.
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__(*, url, table, credentials=None, load_args=None, save_args=None, metadata=None)

Creates a new SparkJDBCDataset.

Parameters:

• url (str) – A JDBC URL of the form jdbc:subprotocol:subname.

• table (str) – The name of the table to load or save data to.

• credentials (Optional[dict[str, Any]]) – A dictionary of JDBC database connection arguments. Normally at least properties user and password with their corresponding values. It updates properties parameter in load_args and save_args in case it is provided.

• load_args (Optional[dict[str, Any]]) – Provided to underlying PySpark jdbc function along with the JDBC URL and the name of the table. To find all supported arguments, see here: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.jdbc.html

• save_args (Optional[dict[str, Any]]) – Provided to underlying PySpark jdbc function along with the JDBC URL and the name of the table. To find all supported arguments, see here: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.jdbc.html

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Raises:

DatasetError – When either url or table is empty or when a property is provided with a None value.

exists()

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)

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()

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()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

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()

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.