Hi Arun, A Java API was actually recently added to the library. It will be available in the next release.
-Sandy On Thu, Dec 10, 2015 at 12:16 AM, Arun Verma <arun.verma...@gmail.com> wrote: > Thank you for your reply. It is a Scala and Python library. Is similar > library exists for Java? > > On Wed, Dec 9, 2015 at 10:26 PM, Sean Owen <so...@cloudera.com> wrote: > >> CC Sandy as his https://github.com/cloudera/spark-timeseries might be >> of use here. >> >> On Wed, Dec 9, 2015 at 4:54 PM, Arun Verma <arun.verma...@gmail.com> >> wrote: >> > Hi all, >> > >> > We have RDD(main) of sorted time-series data. We want to split it into >> > different RDDs according to window size and then perform some >> aggregation >> > operation like max, min etc. over each RDD in parallel. >> > >> > If window size is w then ith RDD has data from (startTime + (i-1)*w) to >> > (startTime + i*w) where startTime is time-stamp of 1st entry in main >> RDD and >> > (startTime + (i-1)*w) is greater then last entry of main RDD. >> > >> > For now, I am using DataFrame and Spark version 1.5.2. Below code is >> running >> > sequentially on the data, so execution time is high and resource >> utilization >> > is low. Code snippet is given below: >> > /* >> > * aggragator is max >> > * df - Dataframe has sorted timeseries data >> > * start - first entry of DataFrame >> > * end - last entry of DataFrame df >> > * bucketLengthSec - window size >> > * stepResults - has particular block/window output(JSON) >> > * appendResults - has output till this block/window(JSON) >> > */ >> > while (start <= end) { >> > row = df.filter(df.col("timeStamp") >> > .between(start, nextStart)) >> > .agg(max(df.col("timeStamp")), max(df.col("value"))) >> > .first(); >> > if (row.get(0) != null) { >> > stepResults = new JSONObject(); >> > stepResults.put("x", Long.parseLong(row.get(0).toString())); >> > stepResults.put("y", row.get(1)); >> > appendResults.add(stepResults); >> > } >> > start = nextStart; >> > nextStart = start + bucketLengthSec; >> > } >> > >> > >> > -- >> > Thanks and Regards, >> > Arun Verma >> > > > > -- > Thanks and Regards, > Arun Verma >
