Source code for kedro.contrib.decorators.pyspark

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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
[docs]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