I'm using Parquet in ADAM, and I can say that it works pretty fine! Enjoy ;-)
aℕdy ℙetrella about.me/noootsab [image: aℕdy ℙetrella on about.me] <http://about.me/noootsab> On Mon, Sep 15, 2014 at 1:41 PM, Marius Soutier <mps....@gmail.com> wrote: > Thank you guys, I’ll try Parquet and if that’s not quick enough I’ll go > the usual route with either read-only or normal database. > > On 13.09.2014, at 12:45, andy petrella <andy.petre...@gmail.com> wrote: > > however, the cache is not guaranteed to remain, if other jobs are launched > in the cluster and require more memory than what's left in the overall > caching memory, previous RDDs will be discarded. > > Using an off heap cache like tachyon as a dump repo can help. > > In general, I'd say that using a persistent sink (like Cassandra for > instance) is best. > > my .2¢ > > > aℕdy ℙetrella > about.me/noootsab > [image: aℕdy ℙetrella on about.me] > > <http://about.me/noootsab> > > On Sat, Sep 13, 2014 at 9:20 AM, Mayur Rustagi <mayur.rust...@gmail.com> > wrote: > >> You can cache data in memory & query it using Spark Job Server. >> Most folks dump data down to a queue/db for retrieval >> You can batch up data & store into parquet partitions as well. & query it >> using another SparkSQL shell, JDBC driver in SparkSQL is part 1.1 i >> believe. >> -- >> Regards, >> Mayur Rustagi >> Ph: +1 (760) 203 3257 >> http://www.sigmoidanalytics.com >> @mayur_rustagi >> >> >> On Fri, Sep 12, 2014 at 2:54 PM, Marius Soutier <mps....@gmail.com> >> wrote: >> >>> Hi there, >>> >>> I’m pretty new to Spark, and so far I’ve written my jobs the same way I >>> wrote Scalding jobs - one-off, read data from HDFS, count words, write >>> counts back to HDFS. >>> >>> Now I want to display these counts in a dashboard. Since Spark allows to >>> cache RDDs in-memory and you have to explicitly terminate your app (and >>> there’s even a new JDBC server in 1.1), I’m assuming it’s possible to keep >>> an app running indefinitely and query an in-memory RDD from the outside >>> (via SparkSQL for example). >>> >>> Is this how others are using Spark? Or are you just dumping job results >>> into message queues or databases? >>> >>> >>> Thanks >>> - Marius >>> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> >> > >
