DATA SCIENCE USING SPARK: AN INTRODUCTION

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1 DATA SCIENCE USING SPARK: AN INTRODUCTION

2 TOPICS COVERED Introduction to Spark Getting Started with Spark Programming in Spark Data Science with Spark What next? 2

DATA SCIENCE PROCESS Exploratory Data Analysis Real World Raw data is collected Data is processed Clean Data Machine Learning Algorithms Statistical Models Build

3 DATA SCIENCE PROCESS Exploratory Data Analysis Real World Raw data is collected Data is processed Clean Data Machine Learning Algorithms Statistical Models Build Data Product Communicate visualizations Report Findings Make Decisions Source: Doing Data Science by Rachel Schutt & Cathy O Neil 3

4 DATA SCIENCE & DATA MINING Distinctions are blurred Business Analytics Data Science Knowledge Discovery Data Mining Visual Data Mining Domain Knowledge (Structured) Data Mining (Unstructured) Data Mining Big Data Engineering Natural Language Processing Text Mining Web Mining Statistics Machine Learning Database Management Management Science 4

5 WHAT DO WE NEED TO SUPPORT DATA SCIENCE WORK? Data Input /Output Ability to read data in multiple formats Ability to read data from multiple sources Ability to deal with Big Data (Volume, Velocity, Veracity and Variety) Data Transformations Easy to describe and perform transformations on rows and columns of data Requires abstraction of data and a dataflow paradigm Model Development Library of Data Science Algorithms Ability to import / export models from other sources Data Science pipelines / workflow Development Analytics Applications Development Seamless integration with programming languages / IDEs 5

6 INTRODUCTION TO SPARK S H A R A N K A L W A N I 6

7 WHAT IS SPARK? A distributed computing platform designed to be Fast General Purpose A general engine that allows combination of multiple types of computations Batch Interactive Iterative SQL Queries Text Processing Machine learning 7

8 Fast/Speed Computations in memory Faster than MR even for disk computations Generality Designed for a wide range of workloads Single Engine to combine batch, interactive, iterative, streaming algorithms. Has rich high-level libraries and simple native APIs in Java, Scala and Python. Reduces the management burden of maintaining separate tools. 8

9 SPARK UNIFIED STACK 9

10 CLUSTER MANAGERS Can run on a variety of cluster managers Hadoop YARN - Yet Another Resource Negotiator is a cluster management technology and one of the key features in Hadoop 2. Apache Mesos - abstracts CPU, memory, storage, and other compute resources away from machines, enabling fault-tolerant and elastic distributed systems. Spark Standalone Scheduler provides an easy way to get started on an empty set of machines. Spark can leverage existing Hadoop infrastructure 10

11 SPARK HISTORY Started in 2009 as a research project in UC Berkeley RAD lab which became AMP Lab. Spark researchers found that Hadoop MapReduce was inefficient for iterative and interactive computing. Spark was designed from the beginning to be fast for interactive, iterative with support for in-memory storage and fault-tolerance. Apart from UC Berkeley, Databricks, Yahoo! and Intel are major contributors. Spark was open sourced in March 2010 and transformed into Apache Foundation project in June

12 SPARK VS HADOOP Hadoop MapReduce Mostly suited for batch jobs Difficulty to program directly in MR Batch doesn t compose well for large apps Specialized systems needed as a workaround Spark Handles batch, interactive, and real-time within a single framework Native integration with Java, Python, Scala Programming at a higher level of abstraction More general than MapReduce C O N F I D E N T I A L A N D P R O P R I E T A R Y 12

13 GETTING STARTED WITH SPARK 13

14 GETTING STARTED WITH SPARK..NOT COVERED TODAY! There are multiple ways of using Spark Certified Spark Distributions Datastax Enterprise (Cassandra + Spark) HortonWorks HDP MAPR Local/Standalone Databricks cloud Amazon AWS EC2 14

15 LOCAL MODE Install Java JDK 6/7 on MacOSX or Windows html Install Python 2.7 using Anaconda (only on Windows) Download Apache Spark from Databricks, unzip the downloaded file to a convenient location Connect to the newly created spark-training directory Run the interactive Scala shell (REPL)./spark/bin/spark-shell val data = 1 to 1000 val distdata = sc.parallelize(data) val filtereddata = distdata.filter(s => s<25) filtereddata.collect() 15

16 DATABRICKS CLOUD A hosted data platform powered by Apache Spark Features Exploration and Visualization Managed Spark Clusters Production Pipelines Support for 3 rd party apps (Tableau, Pentaho, Qlik View) Databricks Cloud Trail Demo 16

17 DATABRICKS CLOUD Workspace Tables Clusters 17

18 DATABRICKS CLOUD Notebooks Python Scala SQL Visualizations Markup Comments Collaboration 18

19 DATABRICKS CLOUD Tables Hive tables SQL Dbfs S3 CSV Databases 19

20 AMAZON EC2 Launch a Linux instance on EC2 and setup EC2 Keys 20

21 AMAZON EC2 Setup an EC2 pair from the AWS console 21

22 AMAZON EC2 Spark binary ships with a spark-ec2 script to manage clusters on EC2 Launching Spark cluster on EC2./spark-ec2 -k <keypair> -i <key-file> -s <num-slaves> launch <cluster-name> Running Applications./spark-ec2 -k <keypair> -i <key-file> login <cluster-name> Terminating a cluster./spark-ec2 destroy <cluster-name> Accessing data in S3 s3n://<bucket>/path 22

23 PROGRAMMING IN SPARK S H A R A N K A L W A N I 23

24 Spark Cluster Mesos YARN Standalone 24

25 Scala Scalable Language Scala is a multi-paradigm programming language with focus on the functional programming paradigm. In functional programming functions are used and they use variables that are immutable. Every operator, variable and function is an object. Scala generates bytecode that runs on the top of any JVM and can also use any of the java libraries. Spark is completely written in Scala. Spark SQL, GraphX, Spark Streaming etc. are libraries written in Scala. Scala Crash Course by Holden lintool.github.io/sparktutorial/slides/day1_scala_crash_course.pdf 25

26 Spark Model Write programs in terms of transformations on distributed datasets Resilient Distributed Datasets (RDDs) Read-only collections of objects that can be stored in memory or disk across a cluster Partitions are automatically rebuilt on failure Parallel functional transformations ( map, filter,..) Familiar Scala collections API for distributed data and computation Lazy transformations 26

27 Spark Core RDD Resilient Distributed Dataset A primary abstraction in Spark a fault-tolerant collection of elements that can be operated on in parallel. Two Types Parallelized Scala collections Hadoop datasets Transformations and Actions can be performed on RDDs. Transformations Operate on an RDD and return a new RDD. Are Lazily Evaluated Actions Return a value after running a computation on a RDD. The DAG is evaluated only when an action takes place. 27

28 Spark Shell Interactive Queries and prototyping Local, YARN, Mesos Static type checking and auto complete 28

29 Spark compared to Java (native Hadoop) 29

30 Spark compared to Java (native Hadoop) 30

31 Spark Streaming Real time computation similar to Storm Input distributed to memory for fault tolerance Streaming input in to sliding windows of RDDs Kafka, Flume, Kinesis, HDFS 31

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33 Spark Streaming 33

34 DATA SCIENCE USING SPARK 34

35 WHAT DO WE NEED TO SUPPORT DATA SCIENCE WORK? Data Input /Output Ability to read data in multiple formats Ability to read data from multiple sources Ability to deal with Big Data (Volume, Velocity, and Variety) Data Transformations Easy to describe and perform transformations on rows and columns of data Requires abstraction of data and a dataflow paradigm Model Development Library of Data Science Algorithms Ability to import / export models from other sources Data Science pipelines / workflow Development Analytics Applications Development Seamless integration with programming languages / IDEs 35

36 WHY SPARK FOR DATA SCIENCE? FAST Distributed In-memory Platform Scalable Small to Big Data; Well integrated into the Big Data Ecosystem SPARK is HOT Expressive Simple, higher level abstractions for describing computations Flexible Extendible, Multiple language bindings (Scala, Java, Python, R) C O N F I D E N T I A L A N D P R O P R I E T A R Y 36

37 Traditional Data Science Tools Matlab SAS RapidMiner R SPSS And many others. Designed to work on single machines Proprietary & Expensive 37

38 What is available in Spark? Analytics Workflows (ML Pipeline) Library of Algorithms(MLlib, R packages, Mahout?, Graph Algorithms) Extensions to RDD (SchemaRDD, RRDD, RDPG, DStreams) Basic RDD (Transformations & Actions) 38

39 DATA TYPES FOR DATA SCIENCE (MLLIB) Single Machine Data Types Local Vector Labeled Point Local Matrix Distributed Data Types (supported by RDDs) Distributed Matrix RowMatrix IndexedRowMatrix CoordinateMatrix 39

40 Schema RDDs 40

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42 R to Spark Dataflow Local Worker Spark Executor tasks broadcast vars R pacakges R R Spark Context (ref. in R) Java Spark Context Worker Spark Executor tasks broadcast vars R pacakges R 42

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44 MESOS SHARK SPARK 44

An Introduction to Apache Spark

An Introduction to Apache Spark 1 History Developed in 2009 at UC Berkeley AMPLab. Open sourced in 2010. Spark becomes one of the largest big-data projects with more 400 contributors in 50+ organizations