Class Hierarchy

• SocketServer.BaseServer: Base class for server classes.
• collections.Iterable
• pyspark.mllib.util.MLUtils: Helper methods to load, save and pre-process data used in MLlib.
• pyspark.mllib.random.RandomRDDs: Generator methods for creating RDDs comprised of i.i.d samples from some distribution.
• 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.sql.DataType: Spark SQL DataType
  • pyspark.mllib.tree.DecisionTree: Learning algorithm for a decision tree model for classification or regression.
  • pyspark.mllib.tree.DecisionTreeModel: A decision tree model for classification or regression.
  • 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.stat.MultivariateStatisticalSummary: Trait for multivariate statistical summary of a data matrix.
  • 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.stat.Statistics
  • pyspark.mllib.linalg.Vectors: Factory methods for working with vectors.
  • tuple: tuple() -> empty tuple tuple(iterable) -> tuple initialized from iterable's items
  • type: type(object) -> the object's type type(name, bases, dict) -> a new type