Class Hierarchy

• collections.Iterable
• pyspark.mllib.util.MLUtils: Helper methods to load, save and pre-process data used in MLlib.
• pyspark.sql.SQLContext: Main entry point for SparkSQL functionality.
• pyspark.storagelevel.StorageLevel: Flags for controlling the storage of an RDD.
• object: The most base type
  • pyspark.mllib.recommendation.ALS
  • pyspark.accumulators.Accumulator: A shared variable that can be accumulated, i.e., has a commutative and associative "add" operation.
  • pyspark.accumulators.AccumulatorParam: Helper object that defines how to accumulate values of a given type.
  • pyspark.broadcast.Broadcast: A broadcast variable created with SparkContext.broadcast().
  • pyspark.mllib.clustering.KMeans
  • pyspark.mllib.clustering.KMeansModel: A clustering model derived from the k-means method.
  • pyspark.mllib.regression.LabeledPoint: The features and labels of a data point.
  • pyspark.mllib.regression.LassoWithSGD
  • pyspark.mllib.regression.LinearModel: A linear model that has a vector of coefficients and an intercept.
  • pyspark.mllib.regression.LinearRegressionWithSGD
  • pyspark.mllib.classification.LogisticRegressionWithSGD
  • pyspark.mllib.recommendation.MatrixFactorizationModel: A matrix factorisation model trained by regularized alternating least-squares.
  • pyspark.mllib.classification.NaiveBayes
  • pyspark.mllib.classification.NaiveBayesModel: Model for Naive Bayes classifiers.
  • pyspark.rdd.RDD: A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
  • pyspark.mllib.regression.RidgeRegressionWithSGD
  • pyspark.mllib.classification.SVMWithSGD
  • pyspark.serializers.Serializer
  • pyspark.conf.SparkConf: Configuration for a Spark application.
  • pyspark.context.SparkContext: Main entry point for Spark functionality.
  • pyspark.files.SparkFiles: Resolves paths to files added through SparkContext.addFile().
  • pyspark.mllib.linalg.SparseVector: A simple sparse vector class for passing data to MLlib.
  • pyspark.statcounter.StatCounter
  • pyspark.mllib.linalg.Vectors: Factory methods for working with vectors.
  • dict: dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list.