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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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Configure $HADOOP_HOME/etc/hadoop

Configure $HADOOP_HOME/etc/hadoop

cd $HADOOP_HOME/etc/hadoop

Setup slave, place hostname of the slave nodes are, in this case, same hostname because slave is on the same machine.

vi slaves:

localhost
master.hadoop.lan

The first to edit is core-site.xml file. This file contains information about the port number used by Hadoop instance, file system allocated memory, data store memory limit and the size of Read/Write buffers.

$ vi etc/hadoop/core-site.xml

Add the following properties between <configuration> ... </configuration> tags. Use localhost or your machine FQDN, such as hadoop.master.lan for hadoop instance.

<property>
<name>fs.defaultFS</name>
<value>hdfs://master.hadoop.lan:9000/</value>
</property>

Next open and edit hdfs-site.xml file. The file contains information about the value of replication data, namenode path and datanode path for local file systems.

$ vi etc/hadoop/hdfs-site.xml

Here add the following properties between <configuration> ... </configuration> tags. On this guide we’ll use /mnt/common/hdfs/ directory to store our hadoop file system.

Replace the dfs.data.dir and dfs.name.dir values accordingly.

<property>
<name>dfs.data.dir</name>
<value>file:///mnt/common/hdfs/datanode</value>
</property>
<property>
<name>dfs.name.dir</name>
<value>file:///mnt/common/hdfs/namenode</value>
</property>

Because we’ve specified /mnt/common/hdfs/ as our hadoop file system storage, we need to create those two directories (datanode and namenode) from root account and grant all permissions to hadoop account, or whatever the user name that has installed hadoop by executing the below commands.

su - root
mkdir -p /mnt/common/hdfs/namenode
mkdir -p /mnt/common/hdfs/datanode

In my case, the user that has installed hadoop is bigdata2

chown -R bigdata2:bigdata2 /mnt/common/hdfs/
ls -al /opt/ #Verify permissions
exit

Exit root account to turn back to bigdata2 user

Please replace user bigdata2 with your user name, do NOT copy bigdata2 user name in this slide.

Next, create the mapred-site.xml file to specify that we are using yarn MapReduce framework.

$ vi etc/hadoop/mapred-site.xml

Add the following excerpt to mapred-site.xml file:

<?xml version="1.0"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property> </configuration>

Now, edit yarn-site.xml file with the below statements enclosed between <configuration> ... </configuration> tags:

$ vi etc/hadoop/yarn-site.xml

Add the following excerpt to yarn-site.xml file:

<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>

You should see something like below:

(base) bigdata2@bigdata2:~/hadoop/hadoop-2.7.7/etc/hadoop$ cat yarn-site.xml

<?xml version="1.0"?>
<!--
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

http://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,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. See accompanying LICENSE file.
-->
<configuration>
<!-- Site specific YARN configuration properties -->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>

set JAVA home variable for Hadoop environment by editing the below line from hadoop-env.sh file.

$ vi $HADOOP_HOME/etc/hadoop/hadoop-env.sh

Edit the following line to point to your Java system path.

export JAVA_HOME=/home/bigdata2/java/jdk1.8.0_202

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Last updated 5 years ago

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