Class meeting for 10-405 Workflows For Hadoop

From Cohen Courses
Jump to navigationJump to search

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.




Also discussed

Things to Remember

  • Combiners and how/when they improve efficiency
  • What dataflow languages are, what sort of abstract operations they use, and what the complexity of these operations is.
  • How joins are implemented in dataflow (and the difference between map-side and reduce-side joins)
  • What the PageRank algorithm is
  • Common ways of representing graphs in map-reduce system
    • A list of edges
    • A list of nodes with outlinks
  • Why iteration is often expensive in pure dataflow algorithms.
  • How Spark differs from and/or is similar to other dataflow algorithms
    • Actions/transformations
    • RDDs
    • Caching
  • Definition of a similarity join/soft join.
  • How to implement k-means in a map-reduce setting with dataflow
    • Not discussed in class, but in the slide deck!