"""SparkJDBCDataSet to load and save a PySpark DataFrame via JDBC."""
from copy import deepcopy
from typing import Any, Dict
from pyspark.sql import DataFrame, SparkSession
from kedro.io.core import AbstractDataSet, DataSetError
__all__ = ["SparkJDBCDataSet"]
[docs]class SparkJDBCDataSet(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 adding a catalog entry with
`YAML API <https://kedro.readthedocs.io/en/stable/05_data/\
01_data_catalog.html#using-the-data-catalog-with-the-yaml-api>`_:
.. code-block:: yaml
>>> 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 using Python API:
::
>>> 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())
"""
DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any]
DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any]
# pylint: disable=too-many-arguments
[docs] def __init__(
self,
url: str,
table: str,
credentials: Dict[str, Any] = None,
load_args: Dict[str, Any] = None,
save_args: Dict[str, Any] = None,
) -> None:
"""Creates a new ``SparkJDBCDataSet``.
Args:
url: A JDBC URL of the form ``jdbc:subprotocol:subname``.
table: The name of the table to load or save data to.
credentials: 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: 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/api/pyspark.sql.DataFrameWriter.jdbc.html
save_args: 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/api/pyspark.sql.DataFrameWriter.jdbc.html
Raises:
DataSetError: When either ``url`` or ``table`` is empty or
when a property is provided with a None value.
"""
if not url:
raise DataSetError(
"`url` argument cannot be empty. Please "
"provide a JDBC URL of the form "
"``jdbc:subprotocol:subname``."
)
if not table:
raise DataSetError(
"`table` argument cannot be empty. Please "
"provide the name of the table to load or save "
"data to."
)
self._url = url
self._table = table
# Handle default load and save arguments
self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS)
if load_args is not None:
self._load_args.update(load_args)
self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS)
if save_args is not None:
self._save_args.update(save_args)
# Update properties in load_args and save_args with credentials.
if credentials is not None:
# Check credentials for bad inputs.
for cred_key, cred_value in credentials.items():
if cred_value is None:
raise DataSetError(
f"Credential property `{cred_key}` cannot be None. "
f"Please provide a value."
)
load_properties = self._load_args.get("properties", {})
save_properties = self._save_args.get("properties", {})
self._load_args["properties"] = {**load_properties, **credentials}
self._save_args["properties"] = {**save_properties, **credentials}
def _describe(self) -> Dict[str, Any]:
load_args = self._load_args
save_args = self._save_args
# Remove user and password values from load and save properties.
if "properties" in load_args:
load_properties = load_args["properties"].copy()
load_properties.pop("user", None)
load_properties.pop("password", None)
load_args = {**load_args, "properties": load_properties}
if "properties" in save_args:
save_properties = save_args["properties"].copy()
save_properties.pop("user", None)
save_properties.pop("password", None)
save_args = {**save_args, "properties": save_properties}
return dict(
url=self._url, table=self._table, load_args=load_args, save_args=save_args
)
@staticmethod
def _get_spark():
return SparkSession.builder.getOrCreate()
def _load(self) -> DataFrame:
return self._get_spark().read.jdbc(self._url, self._table, **self._load_args)
def _save(self, data: DataFrame) -> None:
return data.write.jdbc(self._url, self._table, **self._save_args)