scala - How to map on a Spark Dataset partition elements? -


i have list of event log entries this: (user_id, timestamp). entries stored on hive table, , partitioned date of timestamp.

now, want create sessions out of events. session collection of events belong single user. if there gap in user activity of 30 minutes, assume there's new session. have method looks this:

def sessionize(events: list[trackingevent]): map[integer, list[usersession]] = {     val eventsbyuser = events.sortwith((a, b) => a.timestamp.before(b.timestamp)).groupby(_.userid)     val sessionsbyuser: mutablemap[integer, list[usersession]] = mutablemap()     ((userid, eventlist) <- eventsbyuser) {         val sessions: mutablelist[usersession] = mutablelist()         (event <- eventlist) {             sessions.lastoption match {                 case none => sessions += usersession.fromevent(event)                 case some(lastsession) if event.belongstosession(lastsession) => lastsession.includeevent(event)                 case some(_) => sessions += usersession.fromevent(event)             }             sessionsbyuser(userid) = sessions.tolist         }     }     sessionsbyuser.tomap }

the problem code needs all events of single day work, should fine, because files partitioned this. nevertheless, spark still doing lot of shuffling. there better way this?

thanks!


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