content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
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  """Implementation of the Spark Dataframe Materializer."""
  
  import os.path
  from typing import Any, ClassVar, Dict, Tuple, Type
  
  from pyspark.sql import DataFrame, SparkSession
  
  from zenml.enums import ArtifactType
  from zenml.materializers.base_materializer import BaseMaterializer
  from zenml.metadata.metadata_types import MetadataType
  
  DEFAULT_FILEPATH = "data"
  
  
  class SparkDataFrameMaterializer(BaseMaterializer):
      """Materializer to read/write Spark dataframes."""
  
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (DataFrame,)
      ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
  
      def load(self, data_type: Type[Any]) -> DataFrame:
          """Reads and returns a spark dataframe.
  
          Args:
              data_type: The type of the data to read.
  
          Returns:
              A loaded spark dataframe.
          """
          # Create the Spark session
          spark = SparkSession.builder.getOrCreate()
  
          # Read the data
          path = os.path.join(self.uri, DEFAULT_FILEPATH)
          return spark.read.parquet(path)
  
      def save(self, df: DataFrame) -> None:
          """Writes a spark dataframe.
  
          Args:
              df: A spark dataframe object.
          """
          # Write the dataframe to the artifact store
          path = os.path.join(self.uri, DEFAULT_FILEPATH)
          df.write.parquet(path)
  
      def extract_metadata(self, df: DataFrame) -> Dict[str, "MetadataType"]:
          """Extract metadata from the given `DataFrame` object.
  
          Args:
              df: The `DataFrame` object to extract metadata from.
  
          Returns:
              The extracted metadata as a dictionary.
          """
          return {
              "shape": (df.count(), len(df.columns)),
          }
  