Big Data

How to Process Data Using Spark 2

Spark 2 extends the RDDs(Resilient Distributed Dataset) in terms of a “DataFrame” Dataframe contains Row Objects thus give you power to use Spark SQL and Spark Machine Learning […]

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Mohd Naeem
  • Spark 2 extends the RDDs(Resilient Distributed Dataset) in terms of a “DataFrame”
  • Dataframe contains Row Objects thus give you power to use Spark SQL and Spark Machine Learning etc
  • It can process from any kind of relational or non-relational data source .
  • You can use commands similar to SQL like Select, Group by , Order by, register your own functions :
    • dataFrame.select(“fieldnames”) for projection
    • dataFrame.select(“fieldnames”> 20) for filtering data
    • dataFrame.groupby(“fieldnames”) for grouping and aggregating data
    • dataFrame.rdd().map(mapfunc) for get the underneath RDD
  • While running Spark 2 you have to use export SPARK_MAJOR_VERSION=2 to notify that you are using spark 2
  • The code is here – https://s3.amazonaws.com/testbucket786786/PopularMoviesSpark2.py
  • spark2codeIn the code above , you can see that a SparkSession is created and used trill programs ends and is stopped at the end.
  • Creating a DataFrame was a simple command – sparkS.createDataFrame(movieRatingRDD).cache()
  • The code used group by and avg commands to group by movieID and avg by rating
  • It also uses to join 2 dataframes by movieID
  • Here is the end result ….AmityVille 3 D rated at an average 1.16 by 6 people is the worst of all movies
  • spark2res