Yes, if you break it down to... tickerRDD.map(ticker => (ticker.timestamp, ticker) ).map { case(ts, ticker) => ((ts / 60000) * 60000, ticker) }.groupByKey
... as Michael alluded to, then it more naturally extends to the sliding window, since you can flatMap one Ticker to many (bucket, ticker) pairs, then group. I think this would implementing 1 minute buckets, sliding by 10 seconds: tickerRDD.flatMap(ticker => (ticker.timestamp - 60000 to ticker.timestamp by 15000).map(ts => (ts, ticker)) ).map { case(ts, ticker) => ((ts / 60000) * 60000, ticker) }.groupByKey On Tue, Jan 6, 2015 at 8:47 PM, Asim Jalis <asimja...@gmail.com> wrote: > I guess I can use a similar groupBy approach. Map each event to all the > windows that it can belong to. Then do a groupBy, etc. I was wondering if > there was a more elegant approach. > > On Tue, Jan 6, 2015 at 3:45 PM, Asim Jalis <asimja...@gmail.com> wrote: > >> Except I want it to be a sliding window. So the same record could be in >> multiple buckets. >> >>