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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  • RUNNING ON A CLUSTER
  • Specifying Memory Per Executor
  • Specifying A Cluster Manager
  • Running On A Cluster
  1. Section 5: Running Spark on a Cluster

37. Best Practices for Running on a Cluster

RUNNING ON A CLUSTER

Specifying Memory Per Executor

  • Just use an empty, default SparkConf in your drive - this way, we'll use the defaults EMR sets up instead, as well as any command-line options you pass into spark-submit from your master node

  • If executors start failing, you may need to adjust the memory each executor has. For example:

      spark-submit --executor-memory 1g MovieSimilarities1M.py 260
  (from the master node of our cluster.)

Specifying A Cluster Manager

  • Can use -master yarn to run on a YARN cluster

  • EMR sets this up by default

Running On A Cluster

  • Get your scripts & data someplace where EMR can access them easily

    • AWS's S3 is a good choice - just use s3n://URL's when specifying file paths, and make sure your file permissions make them accessible

  • Spin up an EMR cluster for Spark using the AWS console

    • Billing starts now!!

  • Get the external DNS name for the master node, and log into it using the "hadoop" user account and your private key file

  • Copy your driver program's JAR and any files it needs

    • ie, using aws s3 cp s3://bucket-name/filename ./

  • Run spark-submit and watch the output!

  • REMEMMBER TO TERMINATE YOUR CLUSTER WHEN YOU'RE DONE

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Last updated 6 years ago