Classify data using Apache Spark - scala

Question

I have the following ds:
|-- created_at: timestamp (nullable = true)
|-- channel_source_id: integer (nullable = true)
|-- movie_id: integer (nullable = true)
I would like to classify the movie_id based on some conditions:
Number of times have been played;
#Count ocurrences by ID
SELECT COUNT(createad_at) FROM logs GROUP BY movie_id
What is the range (created_at) that movie has been played;
#Returns distinct movies_id
SELECT DISTINCT(movie_id) FROM logs
#For each movie_id, I would like to retrieve the hour that has been played
#When i have the result, I could apply an filter from df to extract the intervals
SELECT created_at FROM logs WHERE movie_id = ?
Number of differents channel_source_id that have played the movie;
#Count number of channels that have played
SELECT COUNT(DISTINCT(channel_source_id)) FROM logs where movie_id = ? GROUP BY movie_id
I've written a simple table to help me on classification
Played 1 to 5 times, range between 00:00:00 - 03:59:59, 1 to 3 different channels >> Movie Type A
Played 6 to 10 times, range between 04:00:00 - 07:59:59, 4 to 5 different channels >> Movie Type B
etc
I'm using Spark to import file but i'm lost how can I perform the classification. Anyone could me give me a hand on where should I start?
def run() = {
val sqlContext = new SQLContext(sc)
val df = sqlContext.read
.format("com.databricks.spark.csv")
.options(Map("header" -> "true", "inferSchema" -> "true"))
.load("/home/plc/Desktop/movies.csv")
df.printSchema()
}