Apache 2.0 Spark with Scala
  • Introduction
  • Introduction
    • Introduction
  • Section 1: Getting Started
    • 1. Warning about Java 9 and Spark2.3!
    • 2. Introduction, and Getting Set Up
    • 3. [Activity] Create a Histogram of Real Movie Ratings with Spark!
  • Section 2: Scala Crash Course
    • 4. [Activity] Scala Basics, Part 1
    • 5. [Exercise] Scala Basics, Part 2
    • 6. [Exercise] Flow Control in Scala
    • 7. [Exercise] Functions in Scala
    • 8. [Exercise] Data Structures in Scala
  • Section 3: Spark Basics and Simple Examples
    • 9. Introduction to Spark
    • 10. Introducing RDD's
    • 11. Ratings Histogram Walkthrough
    • 12. Spark Internals
    • 13. Key /Value RDD's, and the Average Friends by Age example
    • 14. [Activity] Running the Average Friends by Age Example
    • 15. Filtering RDD's, and the Minimum Temperature by Location Example
    • 16. [Activity] Running the Minimum Temperature Example, and Modifying it for Maximum
    • 17. [Activity] Counting Word Occurences using Flatmap()
    • 18. [Activity] Improving the Word Count Script with Regular Expressions
    • 19. [Activity] Sorting the Word Count Results
    • 20. [Exercise] Find the Total Amount Spent by Customer
    • 21. [Exercise] Check your Results, and Sort Them by Total Amount Spent
    • 22. Check Your Results and Implementation Against Mine
  • Section 4: Advanced Examples of Spark Programs
    • 23. [Activity] Find the Most Popular Movie
    • 24. [Activity] Use Broadcast Variables to Display Movie Names
    • 25. [Activity] Find the Most Popular Superhero in a Social Graph
    • 26. Superhero Degrees of Seperation: Introducing Breadth-First Search
    • 27. Superhero Degrees of Seperation: Accumulators, and Implementing BFS in Spark
    • 28. Superhero Degrees of Seperation: Review the code, and run it!
    • 29. Item-Based Collaborative Filtering in Spark, cache(), and persist()
    • 30. [Activity] Running the Similiar Movies Script using Spark's Cluster Manager
    • 31. [Exercise] Improve the Quality of Similiar Movies
  • Section 5: Running Spark on a Cluster
    • 32. [Activity] Using spark-submit to run Spark driver scripts
    • 33. [Activity] Packaging driver scripts with SBT
    • 34. Introducing Amazon Elastic MapReduce
    • 35. Creating Similar Movies from One Million Ratings on EMR
    • 36. Partitioning
    • 37. Best Practices for Running on a Cluster
    • 38. Troubleshooting, and Managing Dependencies
  • Section 6: SparkSQL, DataFrames, and DataSets
    • 39. Introduction to SparkSQL
    • 40. [Activity] Using SparkSQL
    • 41. [Activity] Using DataFrames and DataSets
    • 42. [Activity] Using DataSets instead of RDD's
  • Section 7: Machine Learning with MLLib
    • 43. Introducing MLLib
    • 44. [Activity] Using MLLib to Produce Movie Recommendations
    • 45. [Activity] Using DataFrames with MLLib
    • 46. [Activity] Using DataFrames with MLLib
  • Section 8: Intro to Spark Streaming
    • 47. Spark Streaming Overview
    • 48. [Activity] Set up a Twitter Developer Account, and Stream Tweets
    • 49. Structured Streaming
  • Section 9: Intro to GraphX
    • 50. GraphX, Pregel, and Breadth-First-Search with Pregel.
    • 51. [Activity] Superhero Degrees of Seperation using GraphX
  • Section 10: You Made It! Where to Go from Here.
    • 52. Learning More, and Career Tips
    • 53. Bonus Lecture: Discounts on my other "Big Data" / Data Science Courses.
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  • FRAMING BFS AS A SPARK PROBLEM
  • Implementing BFS In Spark
  • Map Function To Convert Marvel-Graph.txt To BFS Nodes
  • Iteratively Process The RDD
  • A BFS Iteration As A Map And Reduce Job
  • How Do We Know When We'Re Done?
  • Off To The Code
  1. Section 4: Advanced Examples of Spark Programs

27. Superhero Degrees of Seperation: Accumulators, and Implementing BFS in Spark

FRAMING BFS AS A SPARK PROBLEM

Implementing BFS In Spark

  • Represent each line as a node with connections, a color a distance.

  • For example:

      5983 1165 3836 4361 1282
  • becomes

      (5983, (1165, 3836, 4361, 1282), 9999, WHITE)
  • Our initial condition is that a node is infinitely distant (9999) and white

Map Function To Convert Marvel-Graph.txt To BFS Nodes

    def convertToBFS(line: String): BFSNode = {
        val fields = line.split("\\s+")
        val heroID = fields(0).toInt

        var connections: ArrayBuffer(Int) = ArrayBuffer()
        for (connections <- 1 to (fields.length - 1)){
            connections += fields(connection).toInt
        }

        var color: String = "WHITE"
        var distance: Int = 9999

        if (heroID == startCharacterID){
            color = "GRAY"
            distance = 0
        }

    }

Iteratively Process The RDD

  • Just like each step of our BFS example...

  • Go through, looking for gray nodes to expand

  • Color nodes we're done with black

  • Update the distances as we go

A BFS Iteration As A Map And Reduce Job

  • The mapper:

    • Creates new nodes for each connection of gray nodes, with a distance incremented by one, color gray, and no connections

    • Colors the gray node we just processed black

    • Copies the node itself into the results.

  • The reducer:

    • Combines together all nodes for the same hero ID

    • Preserves the shortest distance, and the darkest color found.

    • Preserves the list of connections from the original node.

How Do We Know When We'Re Done?

  • An accmulator allows many executors to increment a shared variable

  • For example:

      var hitCOunter: LongAccumulator("Hit Counter")
  • sets up a shared accumulator named "Hit Counter" with an initial value of 0.

  • For each iteration, if the character we're interested in is hit, we increment the hitCounter accumulator

  • After each iteration, we check if hitCounter is greater than one- if so, we're done.

Off To The Code

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Last updated 6 years ago