kedro.extras.datasets.spark.SparkJDBCDataSet

class kedro.extras.datasets.spark.SparkJDBCDataSet(url, table, credentials=None, load_args=None, save_args=None)[source]

Bases: kedro.io.core.AbstractDataSet

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:

import pandas as pd

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'}
data_set = SparkJDBCDataSet(
    url=url, table=table, credentials={'user': 'scott',
                                       'password': 'tiger'},
    load_args={'properties': connection_properties},
    save_args={'properties': connection_properties})

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

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

Attributes

SparkJDBCDataSet.DEFAULT_LOAD_ARGS

SparkJDBCDataSet.DEFAULT_SAVE_ARGS

Methods

SparkJDBCDataSet.exists()

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

SparkJDBCDataSet.from_config(name, config[, …])

Create a data set instance using the configuration provided.

SparkJDBCDataSet.load()

Loads data by delegation to the provided load method.

SparkJDBCDataSet.release()

Release any cached data.

SparkJDBCDataSet.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)[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 (Optional[str]) – 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 (Optional[str]) – 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

Any

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

Raises

DataSetError – when underlying save method raises error.

Return type

None