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Data Science with Apache Spark
  • Preface
  • Contents
  • Basic Prerequisite Skills
  • Computer needed for this course
  • Spark Environment Setup
  • Dev environment setup, task list
  • JDK setup
  • Download and install Anaconda Python and create virtual environment with Python 3.6
  • Download and install Spark
  • Eclipse, the Scala IDE
  • Install findspark, add spylon-kernel for scala
  • ssh and scp client
  • Summary
  • Development environment on MacOS
  • Production Spark Environment Setup
  • VirtualBox VM
  • VirtualBox only shows 32bit on AMD CPU
  • Configure VirtualBox NAT as Network Adapter on Guest VM and Allow putty ssh Through Port Forwarding
  • Docker deployment of Spark Cluster
  • Create customized Apache Spark Docker container
  • Dockerfile
  • docker-compose and docker-compose.yml
  • Launch custom built Docker container with docker-compose
  • Entering Docker Container
  • Setup Hadoop, Hive and Spark on Linux without docker
  • Hadoop Preparation
  • Hadoop setup
  • Configure $HADOOP_HOME/etc/hadoop
  • HDFS
  • Start and stop Hadoop
  • Work with Hadoop and HDFS file system
  • Connect to Hadoop web interface port 50070 and 8088
  • Install Hive
  • hive home
  • Initialize hive schema
  • Start hive metastore service.
  • hive-site.xml
  • Hive client
  • Setup Apache Spark
  • Spark Home
  • Jupyter-notebook server
  • Python 3 Warm Up
  • Basics
  • Iterables/Collections
  • Strings
  • List
  • Tuple
  • Dictionary
  • Set
  • Conditional statement
  • for loop
  • while loop
  • Functions and methods
  • map and filter
  • map and filter takes function as input
  • lambda
  • Python Class
  • Input and if statement
  • Input from a file
  • Output to a file
  • try except
  • Python coding exercise
  • Scala Warm Up
  • Start Spylon-kernel on Jupyter-notebook
  • Type of Variable: Mutable or immutable
  • Block statement
  • Scala Data Type
  • Array in Scala
  • Methods
  • Functions
  • Anonymous function
  • Scala map and filter methods
  • Class
  • Objects
  • Trait
  • Tuple in Scala
  • List/Seq
  • Set in Scala
  • Scala Map
  • Scala if statement
  • Scala for loop
  • Scala While Loop
  • Scala Exceptions + try catch finally
  • Scala coding exercise
  • Run a program to estimate pi
  • Common Spark command line
  • Run Scala code with spark-submit
  • Python with Apache Spark using Jupyter notebook
  • Spark Core Introduction
  • Spark and Scala Version
  • Basic Spark Package
  • Resilient Distributed Datasets (RDDs)
  • RDD Operations
  • Passing Function to Spark
  • Printing elements of an RDD
  • Working with key value pair
  • RDD Transformation Functions
  • RDD Action Functions
  • SPARK SQL
  • SQL
  • Datasets and DataFrames
  • SparkSession
  • Creating DataFrames
  • Running SQL Queries Programmatically
  • Issue from running Cartesian Join Query
  • Creating Datasets
  • Interoperating with RDD
  • Untyped User-Defined Aggregate Functions
  • Generic Load/Save Functions
  • Manually specify file option
  • Run SQL on files directly
  • Save Mode
  • Saving to Persistent Tables
  • Bucketing, Sorting and Partitioning
  • Apache Arrow
  • Install Python Arrow Module PyArrow
  • Issue might happen import PyArrow
  • Enabling for Conversion to/from Pandas in Python
  • Connect to any data source the same consistent way
  • Spark SQL Implementation Example in Scala
  • Run scala code in Eclipse IDE
  • Hive Integration, run SQL or HiveQL queries on existing warehouses.
  • Example: Enrich JSON
  • Integrate Tableau Data Visualization with Hive Data Warehouse and Apache Spark SQL
  • Connect Tableau to Spark SQL running in VM with VirtualBox with NAT
  • Issues with connecting from Tableau to Spark SQL
  • SPARK Streaming
  • Discretized Streams (DStreams)
  • Transformations on DStreams
  • map(func)
  • filter(func)
  • repartition(numPartitions)
  • union(otherStream)
  • reduce(func)
  • count()
  • countByValue()
  • reduceByKey(func, [numTasks])
  • join(otherStream, [numTasks])
  • cogroup(otherStream, [numTasks])
  • transform(func)
  • updateStateByKey(func)
  • Scala Tips for updateStateByKey
  • repartition(numPartitions)
  • DStream Window Operations
  • DStream Window Transformation
  • countByWindow(windowLength, slideInterval)
  • reduceByWindow(func, windowLength, slideInterval)
  • reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])
  • reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks])
  • countByValueAndWindow(windowLength, slideInterval, [numTasks])
  • window(windowLength, slideInterval)
  • Window DStream print(n)
  • saveAsTextFiles(prefix, [suffix])
  • saveAsObjectFiles(prefix, [suffix])
  • saveAsHadoopFiles(prefix, [suffix])
  • foreachRDD(func)
  • Build Twitter Scala API Library for Spark Streaming using sbt
  • Spark Streaming with Twitter, you can get public tweets by using Twitter API.
  • Spark streaming use case with Python
  • Spark Graph Computing
  • Spark Graph Computing Continue
  • Graphx
  • Package org.apache.spark.graphx
  • Edge Class
  • EdgeContext Class
  • EdgeDirection Class
  • EdgeRDD Class
  • EdgeTriplet Class
  • Graph Class
  • GraphLoader Object
  • GraphOps Class
  • GraphXUtils Object
  • PartitionStrategy Trait
  • Pregel Object
  • TripletFields Class
  • VertexRDD Class
  • Package org.apache.spark.graphx.impl
  • AggregatingEdgeContext Class
  • EdgeRDDImpl Class
  • Class GraphImpl<VD,ED>
  • Class VertexRDDImpl<VD>
  • Package org.apache.spark.graphx.lib
  • Class ConnectedComponents
  • Class LabelPropagation
  • Class PageRank
  • Class ShortestPaths
  • Class StronglyConnectedComponents
  • Class SVDPlusPlus
  • Class SVDPlusPlus.Conf
  • Class TriangleCount
  • Package org.apache.spark.graphx.util
  • Class BytecodeUtils
  • Class GraphGenerators
  • Graphx Example 1
  • Graphx Example 2
  • Graphx Example 3
  • Spark Graphx Describes Organization Chart Easy and Fast
  • Page Rank with Apache Spark Graphx
  • bulk synchronous parallel with Google Pregel Graphx Implementation Use Cases
  • Tree and Graph Traversal with and without Spark Graphx
  • Graphx Graph Traversal with Pregel Explained
  • Spark Machine Learning
  • Binary Classification
  • Multiclass Classification
  • Regression
  • Correlation
  • Image Data Source
  • ML DataFrame is SQL DataFrame
  • ML Transformer
  • ML Estimator
  • ML Pipeline
  • Transformer/Estimator Parameters
  • Extracting, transforming and selecting features
  • TF-IDF
  • Word2Vec
  • FeatureHasher
  • Tokenizer
  • CountVectorizer
  • StopWordRemover
  • n-gram
  • Binarizer
  • PCA
  • PolynomialExpansion
  • StringIndexer
  • Discrete Cosine Transform (DCT)
  • One-hot encoding
  • StandardScaler
  • IndexToString
  • VectorIndexer
  • Interaction
  • Normalizer
  • MinMaxScaler
  • MaxAbScaler
  • Bucketizer
  • ElementwiseProduct
  • SQLTransformer
  • VectorAssembler
  • VectorSizeHint
  • QuantileDiscretizer
  • Imputer
  • VectorSlicer
  • RFormula
  • ChiSqSelector
  • Locality Sensitive Hashing
  • MinHash for Jaccard Distance
  • Classification and Regression
  • LogisticRegression
  • OneVsRest
  • Naive Bayes classifiers
  • Decision trees
  • Random forests
  • Gradient-boosted trees (GBTs)
  • Multilayer perceptron classifier
  • Linear Support Vector Machine
  • Linear Regression
  • Generalized linear regression
  • Isotonic regression
  • Decision Tree Regression
  • Random Forest Regression
  • Gradient-boosted tree regression
  • Survival regression
  • Clustering
  • k-means
  • Latent Dirichlet allocation or LDA
  • Bisecting k-means
  • A Gaussian Mixture Model
  • Collaborative filtering
  • Frequent Pattern Mining
  • FP-Growth
  • PrefixSpan
  • ML Tuning: model selection and hyperparameter tuning
  • Model selection (a.k.a. hyperparameter tuning)
  • Cross-Validation
  • Train-Validation Split
  • Spark Machine Learning Applications
  • Apache Spark SQL & Machine Learning on Genetic Variant Classifications
  • Data Visualization with Vegas Viz and Scala with Spark ML
  • Apache Spark Machine Learning with Dremio Data Lake Engine
  • Dremio Data Lake Engine Apache Arrow Flight Connector with Spark Machine Learning
  • Neural Network with Apache Spark Machine Learning Multilayer Perceptron Classifier
  • Setup TensorFlow, Keras, Theano, Pytorch/torchvision on the CentOS VM
  • Virus Xray Image Classification with Tensorflow Keras Python and Apache Spark Scala
  • Appendix -- Video Presentations
  • References
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Graph Class

abstract class Graph[VD, ED] extends Serializable

The Graph abstractly represents a graph with arbitrary objects associated with vertices and edges. The graph provides basic operations to access and manipulate the data associated with vertices and edges as well as the underlying structure. Like Spark RDDs, the graph is a functional data-structure in which mutating operations return new graphs.

VD the vertex attribute type

ED the edge attribute type

Abstract methods:

abstract def cache(): Graph[VD, ED]
Caches the vertices and edges associated with this graph at the previously-specified target storage levels, which default to MEMORY_ONLY.

abstract def checkpoint(): Unit
Mark this Graph for checkpointing.

abstract val edges: EdgeRDD[ED]
An RDD containing the edges and their associated attributes.

abstract def getCheckpointFiles: Seq[String]
Gets the name of the files to which this Graph was checkpointed.

abstract def groupEdges(merge: (ED, ED) ⇒ ED): Graph[VD, ED]
Merges multiple edges between two vertices into a single edge.

abstract def isCheckpointed: Boolean
Return whether this Graph has been checkpointed or not.

abstract def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) ⇒ Iterator[ED2])(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]
Transforms each edge attribute using the map function, passing it a whole partition at a time.

abstract def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2], tripletFields: TripletFields)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]
Transforms each edge attribute a partition at a time using the map function, passing it the adjacent vertex attributes as well.

abstract def mapVertices[VD2](map: (VertexId, VD) => VD2)(implicit arg0: ClassTag[VD2], eq: =:=[VD, VD2] = null): Graph[VD2, ED]
Transforms each vertex attribute in the graph using the map function.

abstract def mask[VD2, ED2](other: Graph[VD2, ED2])(implicit arg0: ClassTag[VD2], arg1: ClassTag[ED2]): Graph[VD, ED]
Restricts the graph to only the vertices and edges that are also in other, but keeps the attributes from this graph.

abstract def outerJoinVertices[U, VD2](other: RDD[(VertexId, U)])(mapFunc: (VertexId, VD, Option[U]) ⇒ VD2)(implicit arg0: ClassTag[U], arg1: ClassTag[VD2], eq: =:=[VD, VD2] = null): Graph[VD2, ED]
Joins the vertices with entries in the table RDD and merges the results using mapFunc.

abstract def partitionBy(partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED]
Repartitions the edges in the graph according to partitionStrategy.

abstract def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
Repartitions the edges in the graph according to partitionStrategy.

abstract def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
Caches the vertices and edges associated with this graph at the specified storage level, ignoring any target storage levels previously set.

abstract reverse: def Graph[VD, ED]
Reverses all edges in the graph.

abstract def subgraph(epred: (EdgeTriplet[VD, ED]) ⇒ Boolean = x => true, vpred: (VertexId, VD) ⇒ Boolean = (v, d) => true): Graph[VD, ED]
Restricts the graph to only the vertices and edges satisfying the predicates.

abstract val triplets: RDD[EdgeTriplet[VD, ED]]
An RDD containing the edge triplets, which are edges along with the vertex data associated with the adjacent vertices.

abstract def unpersist(blocking: Boolean = true): Graph[VD, ED]
Uncaches both vertices and edges of this graph.

abstract def unpersistVertices (blocking: Boolean = true): Graph[VD, ED]
Uncaches only the vertices of this graph, leaving the edges alone.

abstract vertices val VertexRDD[VD]
An RDD containing the vertices and their associated attributes

Concrete methods:

def aggregateMessages[A](sendMsg: (EdgeContext[VD, ED, A]) ⇒ Unit, mergeMsg: (A, A) ⇒ A, tripletFields: TripletFields = TripletFields.All)(implicit arg0: ClassTag[A]): VertexRDD[A]

Aggregates values from the neighboring edges and vertices of each vertex.

def mapEdges[ED2](map: (Edge[ED]) ⇒ ED2)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

Transforms each edge attribute in the graph using the map function.

def mapTriplets[ED2](map: (EdgeTriplet[VD, ED]) ⇒ ED2, tripletFields: TripletFields)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well.

def mapTriplets[ED2](map: (EdgeTriplet[VD, ED]) ⇒ ED2)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well.

val ops: GraphOps[VD, ED]

The associated GraphOps object.

Reference:

PreviousEdgeTriplet ClassNextGraphLoader Object

Last updated 5 years ago

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https://github.com/apache/spark/blob/v2.4.5/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala