Source code for kedro.contrib.io.pyspark.spark_data_set

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"""``AbstractDataSet`` implementation to access Spark data frames using
``pyspark``
"""

import pickle
from typing import Any, Dict, Optional

from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.utils import AnalysisException

from kedro.contrib.io import DefaultArgumentsMixIn
from kedro.io import AbstractDataSet


[docs]class SparkDataSet(DefaultArgumentsMixIn, AbstractDataSet): """``SparkDataSet`` loads and saves Spark data frames. Example: :: >>> from pyspark.sql import SparkSession >>> from pyspark.sql.types import (StructField, StringType, >>> IntegerType, StructType) >>> >>> from kedro.contrib.io.pyspark import SparkDataSet >>> >>> schema = StructType([StructField("name", StringType(), True), >>> StructField("age", IntegerType(), True)]) >>> >>> data = [('Alex', 31), ('Bob', 12), ('Clarke', 65), ('Dave', 29)] >>> >>> spark_df = SparkSession.builder.getOrCreate()\ >>> .createDataFrame(data, schema) >>> >>> data_set = SparkDataSet(filepath="test_data") >>> data_set.save(spark_df) >>> reloaded = data_set.load() >>> >>> reloaded.take(4) """ def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, file_format=self._file_format, load_args=self._load_args, save_args=self._save_args, )
[docs] def __init__( self, filepath: str, file_format: str = "parquet", load_args: Optional[Dict[str, Any]] = None, save_args: Optional[Dict[str, Any]] = None, ) -> None: """Creates a new instance of ``SparkDataSet``. Args: filepath: path to a Spark data frame. file_format: file format used during load and save operations. These are formats supported by the running SparkContext include parquet, csv. For a list of supported formats please refer to Apache Spark documentation at https://spark.apache.org/docs/latest/sql-programming-guide.html load_args: Load args passed to Spark DataFrameReader load method. It is dependent on the selected file format. You can find a list of read options for each supported format in Spark DataFrame read documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame save_args: Save args passed to Spark DataFrame write options. Similar to load_args this is dependent on the selected file format. You can pass ``mode`` and ``partitionBy`` to specify your overwrite mode and partitioning respectively. You can find a list of options for each format in Spark DataFrame write documentation: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame """ self._filepath = filepath self._file_format = file_format super().__init__(load_args, save_args)
@staticmethod def _get_spark(): return SparkSession.builder.getOrCreate() def _load(self) -> DataFrame: return self._get_spark().read.load( self._filepath, self._file_format, **self._load_args ) def _save(self, data: DataFrame) -> None: data.write.save(self._filepath, self._file_format, **self._save_args) def _exists(self) -> bool: try: self._get_spark().read.load(self._filepath, self._file_format) except AnalysisException as exception: if exception.desc.startswith("Path does not exist:"): return False raise return True def __getstate__(self): raise pickle.PicklingError("PySpark datasets can't be serialized")