Source code for kedro.contrib.decorators.decorators

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"""
This module contains function decorators, which can be used as ``Node``
decorators. See ``kedro.pipeline.node.decorate``
"""
import logging
from functools import wraps
from time import sleep
from typing import Callable

import pandas as pd
from pyspark.sql import SparkSession


[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} ) 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} ) return _wrapper return inputs_to_pandas
[docs]def retry( exceptions: Exception = Exception, n_times: int = 1, delay_sec: float = 0 ) -> Callable: """ Catches exceptions from the wrapped function at most n_times and then bundles them and propagates them. **Make sure your function does not mutate the arguments** Args: exceptions: The superclass of exceptions to catch. By default catch all exceptions. n_times: At most let the function fail n_times. The bundle the errors and propagate them. By default retry only once. delay_sec: Delay between failure and next retry in seconds Returns: The original function with retry functionality. """ def _retry(func: Callable): @wraps(func) def _wrapper(*args, **kwargs): counter = n_times errors = [] while counter >= 0: try: return func(*args, **kwargs) # pylint: disable=broad-except except exceptions as exc: errors.append(exc) if counter != 0: sleep(delay_sec) counter -= 1 if errors: log = logging.getLogger(__name__) log.error( "Function `%s` failed %i times. Errors:\n", func.__name__, n_times ) log.error("\n".join([str(err) for err in errors])) log.error("Raising last exception") raise errors[-1] return _wrapper return _retry