kedro_datasets.spark.SparkJDBCDataset

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

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 data set's output already exists by calling the provided _exists() method.

from_config(name, config[, load_version, ...])

Create a data set 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.

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)[source]

Creates a new SparkJDBCDataset.

Parameters:
Raises:

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

exists()

Checks whether a data set’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 data set instance using the configuration provided.

Parameters:
  • name (str) – Data set name.

  • config (dict[str, Any]) – Data set config dictionary.

  • load_version (str | None) – Version string to be used for load operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

  • save_version (str | None) – Version string to be used for save operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

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

load()

Loads data by delegation to the provided load method.

Return type:

TypeVar(_DO)

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 (TypeVar(_DI)) – 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