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Scala 20140715

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    • 1. Intro to Apache Spark: Fast cluster computing engine for Hadoop Intro to Scala: Object-oriented and functional language for the Java Virtual Machine ACM SIGKDD, 7/9/2014 Roger Huang Lead System Architect rohuang@visa.com rog4096@yahoo.com @BigDataWrangler
    • 2. 2Intro to Spark: Intro to Scala | 7/9/2014 About me: Roger Huang • Visa – Digital & Mobile Products Architecture, Strategic Projects & infrastructure – Search infrastructure – Customer segmentation – Logging Framework – Splunk on Hadoop (Hunk) – Real-time monitoring – Data • PayPal – Java Infrastructure
    • 3. 3Intro to Spark: Intro to Scala | 7/9/2014 Different perspectives on an elephant Scala
    • 4. 4Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 5. 5Intro to Spark: Intro to Scala | 7/9/2014 Spark in the Hadoop ecosystem
    • 6. 6Intro to Spark: Intro to Scala | 7/9/2014 Spark Ecosystem of Software Projects • Spark [Ognen] – APIs: Scala, Python [Robert], Java • “SQL” – Shark (Hive + Spark) [Roger] – SparkSQL (alpha) • Machine Learning Library (MLlib) [Omar] – Clustering – Classification • binary classification • Linear regression – recommendations • Spark Streaming [Chance] • GraphX [Srini] • …
    • 7. 7Intro to Spark: Intro to Scala | 7/9/2014 Resilient Distributed Dataset • Fault tolerant collection of elements partitioned across the nodes of the cluster that can be operated on in parallel • Data sources for RDDs – Parallelized collections • From Scala collections – Hadoop datasets • From HDFS, any Hadoop supported storage system (Hbase, Amazon S3, …) • Text files, SequenceFile, any Hadoop InputFormat • Two types of operations – Transformation • takes an existing dataset and creates a new one – Action • takes a dataset, run a computation, and return value to driver program
    • 8. 8Intro to Spark: Intro to Scala | 7/9/2014 (Some) RDD Operations • Transformations – map(func) – filter(func) – flatMap(func) – mapPartitions(func) – mapPartitionsWithIndex(func) – sample(withReplacement, fraction, seed) – union(otherDataset) – distinct() – groupByKey() – reduceByKey(func) – sortByKey() – Join(otherDataset) – cogroup(otherDataset) – cartesian(otherDataset) • Actions – reduce(func) – collect() – count() – first() – take(n) – takeSample(withReplacement, num, seed) – saveAsTextFile(path) – saveAsSequenceFile(path) – countByKey() – foreach(func) – …
    • 9. 9Intro to Spark: Intro to Scala | 7/9/2014 Scala background • Scalable, Object oriented, functional language – Version 2.11 (4/2014) • Runs on the Java Virtual Machine • Martin Odersky – javac – Java generics • http://scala-lang.org/, REPL • http://www.scala-lang.org/api/current • http://scala-ide.org/ • http://www.scala-sbt.org/, Simple build tool • Who’s using Scala? – Twitter, LinkedIn, … • Powered by Scala – Apache Spark, Apache Kafka, Akka,…
    • 10. 10Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Hadoop/Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 11. 11Intro to Spark: Intro to Scala | 7/9/2014 Scala for the computer scientist: functional programming (FP)
    • 12. 12Intro to Spark: Intro to Scala | 7/9/2014 Scala for the computer scientist: functional programming (FP) • Math functions, e.g., f(x) = y – A function has a single responsibility – A function has no side effects – A function is referentially transparent • A function outputs the same value for the same inputs. • Functional programming – expresses computation as the evaluation and composition of mathematical functions – Avoid side effects and mutating state data
    • 13. 13Intro to Spark: Intro to Scala | 7/9/2014 Why functional programming? • Multi core processors • Concurrency – Computation as a series of independent data transformations – Parallel data transformations without side effects • Referential transparency
    • 14. 14Intro to Spark: Intro to Scala | 7/9/2014 Scala for the computer scientist: functional programming • Functions – Lambda, closure • For-comprehensions • Type inference • Pattern matching • Higher order functions – map, flatMap, foldLeft • And more …
    • 15. 15Intro to Spark: Intro to Scala | 7/9/2014 FP: functions • Anonymous function – Function without a name – lambda function • Example – scala> List(100, 200, 300) map { _ * 10/100} – res0: List[Int] = List(10, 20, 30) • Closure (Wikipedia) – Closure = A function, together with a referencing environment – a table storing a reference to each of the non-local variables of that function. – A closure allows a function to access those non-local variables even when invoked outside its immediate lexical scope.
    • 16. 16Intro to Spark: Intro to Scala | 7/9/2014 FP: functions • applyPercentage is an example of a closure – scala> var percentage = 10 – percentage: Int = 10 – scala> val applyPercentage = (amount: Int) => amount * percentage / 100 – applyPercentage: Int => Int = <function1> – scala> percentage = 20 – percentage: Int = 20 – scala> List (100, 200, 300) map applyPercentage – res1: List[Int] = List(20, 40, 60) – scala>
    • 17. 17Intro to Spark: Intro to Scala | 7/9/2014 FP: functions • Anonymous function • Closure
    • 18. 18Intro to Spark: Intro to Scala | 7/9/2014 FP: Higher order functions scala> :load Person.scala Loading Person.scala... defined class Person scala> val jd = new Person("John", "Doe", 17) jd: Person = Person@372a6e85 scala> val rh = new Person("Roger", "Huang", 34) rh: Person = Person@611c4041 scala> val people = Array(jd, rh) people: Array[Person] = Array(Person@372a6e85, Person@611c4041) scala> val (minors, adults) = people partition (_.age < 18) minors: Array[Person] = Array(Person@372a6e85) adults: Array[Person] = Array(Person@611c4041) scala>
    • 19. 19Intro to Spark: Intro to Scala | 7/9/2014 FP: Higher order functions • HOF – takes a function as an argument – Returns a function
    • 20. 20Intro to Spark: Intro to Scala | 7/9/2014 FP: Higher order functions: map • Creates a new collection from an existing collection by applying a function • Anonymous function scala> List(1, 2, 3 ) map { (x: Int) => x + 1 } res0: List[Int] = List(2, 3, 4) • Function literal scala> List(1, 2, 3) map { _ + 1 } res1: List[Int] = List(2, 3, 4) • Passing an existing function scala> def addOne(num: Int) = num + 1 addOne: (num: Int)Int scala> List(1, 2, 3) map addOne res2: List[Int] = List(2, 3, 4)
    • 21. 21Intro to Spark: Intro to Scala | 7/9/2014 FP: Higher order functions: map
    • 22. 22Intro to Spark: Intro to Scala | 7/9/2014 FP: Higher order functions: flatmap
    • 23. 23Intro to Spark: Intro to Scala | 7/9/2014 FP: for-comprehension • Syntax – for ( <generator> | <guard> ) <expression> [yield] <expression> • Types – Imperative form. Does not return a value. scala> val aList = List(1, 2, 3) aList: List[Int] = List(1, 2, 3) scala> val bList = List(4, 5, 6) bList: List[Int] = List(4, 5, 6) scala> for { a <- aList; if (a < 2); b <- bList; if (b < 7) } println( a + b ) 5 6 7
    • 24. 24Intro to Spark: Intro to Scala | 7/9/2014 FP: for-comprehension • Syntax – for ( <generator> | <guard> ) <expression> [yield] <expression> • Types – Functional form (a.k.a., sequence comprehension) . Returns/yields a value scala> for { a <- aList; b <- bList} yield a + b res0: List[Int] = List(5, 6, 7, 6, 7, 8, 7, 8, 9) scala> res0.take(1) res1: List[Int] = List(5) scala> for { a <- aList; if (a < 2); b <- bList } yield a + b res2: List[Int] = List(5, 6, 7) scala>
    • 25. 25Intro to Spark: Intro to Scala | 7/9/2014 FP: for-comprehension
    • 26. 26Intro to Spark: Intro to Scala | 7/9/2014 FP: foldLeft • scala> val numbers = 1.to(10) • numbers: scala.collection.immutable.Range.Inclusive = Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) • scala> def add( a:Int, b:Int ): Int = { a + b } • add: (a: Int, b: Int)Int • scala> numbers.foldLeft(0){ add } • res0: Int = 55 • scala> numbers.foldLeft(0){ (acc, b) => acc + b } • res1: Int = 55 • scala>
    • 27. 27Intro to Spark: Intro to Scala | 7/9/2014 FP: foldLeft
    • 28. 28Intro to Spark: Intro to Scala | 7/9/2014 FP: find the last item in an array • scala> val ns = Array(20, 40, 60) • ns: Array[Int] = Array(20, 40, 60) • scala> ns.foldLeft(ns.head) {(acc, b) => b} • res0: Int = 60 • scala>
    • 29. 29Intro to Spark: Intro to Scala | 7/9/2014 FP: reverse an array w/ foldLeft • scala> val ns = Array(20, 40, 60) • ns: Array[Int] = Array(20, 40, 60) • scala> ns.foldLeft( Array[Int]() ) { (acc, b) => b +: acc} • res1: Array[Int] = Array(60, 40, 20) • scala>
    • 30. 30Intro to Spark: Intro to Scala | 7/9/2014 FP: reverse an array w/ foldLeft
    • 31. 31Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 32. 32Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Java / OO developer: • Interoperable w/ Java • Case classes • Mixins with traits
    • 33. 33Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Java / OO developer: • case class – Implements equals(), hashCode(), toString() – Can be used in Pattern Matching
    • 34. 34Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Java / OO developer: • http://docs.oracle.com/javase/8/docs/api/java/util/stream/Str eam.html • map – <R> Stream<R> map(Function<? super T,? extends R> mapper)Returns a stream consisting of the results of applying the given function to the elements of this stream.This is an intermediate operation. • flatMap – <R> Stream<R> flatMap(Function<? super T,? extends Stream<? extends R>> mapper)Returns a stream consisting of the results of replacing each element of this stream with the contents of a mapped stream produced by applying the provided mapping function to each element. Each mapped stream is closed after its contents have been placed into this stream. (If a mapped stream is null an empty stream is used, instead.)This is an intermediate operation. `
    • 35. 35Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 36. 36Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Spark developer • ResilientDistributedDataset (RDD) • A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable, partitioned collection of elements that can be operated on in parallel. This class contains the basic operations available on all RDDs, such as map, filter, and persist. • http://spark.apache.org/docs/latest/api/scala/index.html#org.apa che.spark.rdd.RDD
    • 37. 37Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 38. 38Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Big Data developer • Spark – Programming API in Scala – Implemented in Scala • Scalding – Scala DSL on top of Cascading – data processing API and processing query planner used for defining, sharing, and executing data-processing workflows – Abstractions: tuples, pipes, source/sink taps • Algebird • Summingbird – Library that lets you write MapReduce programs that look like native Scala or Java collection transformations – Execute them on a number of well-known distributed MapReduce platforms, including Storm and Scalding.
    • 39. 39Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Hadoop/Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 40. 40Intro to Spark: Intro to Scala | 7/9/2014 Scala for the Big Data scientist / mathematician • Monoid – If you want to “attach” operations such as +, -, *, / or <= to data objects (e.g., Bloom filters), then you want to provide monoid forms of those data objects – Consists of • A set of objects • Binary operation that satisfies the monoid axioms • Monad – If you want to create a data processing pipeline that transforms the state of a data object – composition
    • 41. 41Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Hadoop/Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 42. 42Intro to Spark: Intro to Scala | 7/9/2014 Scala for the system architect • Concurrency • Problem: – Threads – Shared mutable state – Locks, • Solution: – message passing concurrency w/ Actors – Future, Promise • Abstractions – Actor • an object that processes a message • encapsulates state (state not shared) – ActorRef – Message, usually sent asynchronously – Mailbox – ActorSystem
    • 43. 43Intro to Spark: Intro to Scala | 7/9/2014 Scala for the system architect: Akka • Fault tolerance – Supervision – Strategies • Resume, restart, stop, escalate, … • Scale out: remote actors – Via configuration
    • 44. 44Intro to Spark: Intro to Scala | 7/9/2014 Scala for the system architect • Parallel collections – scala> import scala.collection.parallel.immutable._ – import scala.collection.parallel.immutable._ – scala> ParVector(10, 20, 30, 40, 50, 60, 70, 80, 90) .map { x => – | println( Thread.currentThread.getName); x / 2 } – ForkJoinPool-1-worker-13 – ForkJoinPool-1-worker-1 – ForkJoinPool-1-worker-1 – ForkJoinPool-1-worker-9 – ForkJoinPool-1-worker-11 – ForkJoinPool-1-worker-5 – ForkJoinPool-1-worker-3 – ForkJoinPool-1-worker-15 – ForkJoinPool-1-worker-7 – res0: scala.collection.parallel.immutable.ParVector[Int] = ParVector(5, 10, 15, – 20, 25, 30, 35, 40, 45) – scala>
    • 45. 45Intro to Spark: Intro to Scala | 7/9/2014 Sequential collections
    • 46. 46Intro to Spark: Intro to Scala | 7/9/2014 Parallel collections
    • 47. 47Intro to Spark: Intro to Scala | 7/9/2014 Outline • Spark – Hadoop eco system • Scala – Background • Why Scala? – For the computer scientist – For the Java / OO programmer – For the Spark developer – For the Big Data developer – For the Big Data scientist / mathematician – For the system architect
    • 48. 48Intro to Spark: Intro to Scala | 7/9/2014 Different perspectives on an elephant Scala
    • 49. 49Intro to Spark: Intro to Scala | 7/9/2014 Spark in the Hadoop ecosystem
    • 50. 50Intro to Spark: Intro to Scala | 7/9/2014 References • http://scala-lang.org/ • Scala in Action, Nilanjan Raychaudhuri • Grokking Functional Programming, Aslam Khan • Michael Noll
    • 51. Intro to Apache Spark: Fast cluster computing engine for Hadoop Intro to Scala: Object-oriented and functional language for the Java Virtual Machine ACM SIGKDD, 7/9/2014 Roger Huang Lead System Architect Digital & Mobile Products Architecture rohuang@visa.com rog4096@yahoo.com