Cassandra Schemas

  create KEYSPACE spark_demo WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'};
  create table spark_demo.raw_files (filename text,line int, contents text, PRIMARY KEY(filename,line));
  create table spark_demo.users (id int PRIMARY KEY );
  create table spark_demo.movies (id int PRIMARY KEY, name text, year int);
  create table spark_demo.ratings (id int PRIMARY KEY, user_id int, movie_id int, rating float );

connect_to_cassandra.sc

sc.stop

import com.datastax.spark.connector._
import org.apache.spark.SparkContext, org.apache.spark.SparkContext._
import org.apache.spark.SparkConf

val conf = new SparkConf(true).set("spark.cassandra.connection.host", “localhost")
val sc = new SparkContext(conf)

val test_spark_rdd = sc.cassandraTable(“spark_demo”, “raw_files”)
test_spark_rdd.first

data_loader.py

# This script takes the files from the Movie Lens project (http://grouplens.org/datasets/movielens/)
# reads them in line by line and saves them to a table for raw data in Cassandra

from cassandra import cluster

my_cluster = cluster.Cluster(['localhost'])
session = my_cluster.connect('spark_demo')

raw_movies = open( './ml-1m/movies.dat', 'rb')
for idx,line in enumerate(raw_movies):
        session.execute('INSERT INTO raw_files (line,filename,contents) VALUES (%s,%s,%s)', (idx, 'movies.dat', line))

raw_ratings = open( './ml-1m/ratings.dat', 'rb')
for idx,line in enumerate(raw_ratings):
        if (idx % 10000 ) == 0:
                print "{}".format(idx)
        session.execute_async('INSERT INTO raw_files (line,filename,contents) VALUES (%s,%s,%s)', (idx, 'ratings.dat',line))

raw_users = open('./ml-1m/users.dat', 'rb')
for idx,line in enumerate(raw_users):
        session.execute('INSERT INTO raw_files (line,filename,contents) VALUES (%s,%s,%s)', (idx, 'users.dat', line))

etl.sc

// users.dat structure is: UserID::Gender::Age::Occupation::Zip-code
// movies.dat structure is: MovieID::Title::Genres
// ratings.dat structure is: UserID::MovieID::Rating::Timestamp

case class RawFileData(Filename: String, Line: Int, Contents: String )
case class User(Id: Int, Gender: String, Age: Int, Occupation: Int, ZipCode: String )
case class Movie(Id: Int, Title: String, Genres: String)
case class Rating(Id: Int, UserId: Int, MovieId: Int, Rating: Float)

val raw_files = sc.cassandraTable[RawFileData]("spark_demo", "raw_files" )

val users = raw_files.filter( raw => raw.Filename == "users.dat" ).
map(raw => raw.Contents.trim).map( raw => raw.split("::")).
map(raw=>User(raw(0).toInt, raw(1), raw(2).toInt, raw(3).toInt, raw(4)))

users.saveToCassandra("spark_demo", "users")

val movies = raw_files.filter( raw => raw.Filename == "movies.dat" ).
map(raw => raw.Contents.trim).map( raw => raw.split("::")).
map(raw=>Movie(raw(0).toInt,raw(1),raw(2)))

movies.saveToCassandra("spark_demo", "movies")

val ratings = raw_files.filter( raw => raw.Filename == "ratings.dat" ).
map(raw => raw.Contents.trim).map( raw => raw.split("::")).
map(raw=>Rating(raw(0).toInt,raw(1).toInt,raw(2).toInt,raw(3).toFloat))

ratings.saveToCassandra("spark_demo", "ratings")