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"""
This module contains function decorators for PySpark, which can be
used as ``Node`` decorators. See ``kedro.pipeline.node.decorate``
"""
from functools import wraps
from typing import Callable
from warnings import warn
import pandas as pd
try:
from pyspark.sql import SparkSession
except ImportError as error:
raise ImportError(
"{}: `pip install kedro[pyspark]` to get the required "
"dependencies".format(error)
)
warn(
"`kedro.contrib.decorators.pyspark` will be deprecated in future releases. "
"Please refer to Transcoding in the Kedro documentation for an alternative method.",
DeprecationWarning,
)
[docs]def pandas_to_spark(spark: SparkSession) -> Callable:
"""Inspects the decorated function's inputs and converts all pandas
DataFrame inputs to spark DataFrames.
**Note** that in the example below we have enabled
``spark.sql.execution.arrow.enabled``. For this to work, you should first
``pip install pyarrow`` and add ``pyarrow`` to ``requirements.txt``.
Enabling this option makes the convertion between pyspark <-> DataFrames
**much faster**.
Args:
spark: The spark session singleton object to use for the creation of
the pySpark DataFrames. A possible pattern you can use here is
the following:
**spark.py**
::
>>> from pyspark.sql import SparkSession
>>>
>>> def get_spark():
>>> return (
>>> SparkSession.builder
>>> .master("local[*]")
>>> .appName("kedro")
>>> .config("spark.driver.memory", "4g")
>>> .config("spark.driver.maxResultSize", "3g")
>>> .config("spark.sql.execution.arrow.enabled", "true")
>>> .getOrCreate()
>>> )
**nodes.py**
::
>>> from spark import get_spark
>>> @pandas_to_spark(get_spark())
>>> def node_1(data):
>>> data.show() # data is pyspark.sql.DataFrame
Returns:
The original function with any pandas DF inputs translated to spark.
"""
def _to_spark(arg):
if isinstance(arg, pd.DataFrame):
return spark.createDataFrame(arg)
return arg
def inputs_to_spark(node_func: Callable):
@wraps(node_func)
def _wrapper(*args, **kwargs):
return node_func(
*[_to_spark(arg) for arg in args],
**{key: _to_spark(value) for key, value in kwargs.items()}
)
return _wrapper
return inputs_to_spark
def spark_to_pandas() -> Callable:
"""Inspects the decorated function's inputs and converts all pySpark
DataFrame inputs to pandas DataFrames.
Returns:
The original function with any pySpark DF inputs translated to pandas.
"""
def _to_pandas(arg):
if "pyspark.sql.dataframe" in str(type(arg)):
return arg.toPandas()
return arg
def inputs_to_pandas(node_func: Callable):
@wraps(node_func)
def _wrapper(*args, **kwargs):
return node_func(
*[_to_pandas(arg) for arg in args],
**{key: _to_pandas(value) for key, value in kwargs.items()}
)
return _wrapper
return inputs_to_pandas