Nice, I’ll check it out. At first glance, writing Parquet files seems to be a bit complicated.
On 15.09.2014, at 13:54, andy petrella <andy.petre...@gmail.com> wrote: > nope. > It's an efficient storage for genomics data :-D > > aℕdy ℙetrella > about.me/noootsab > > > > On Mon, Sep 15, 2014 at 1:52 PM, Marius Soutier <mps....@gmail.com> wrote: > So you are living the dream of using HDFS as a database? ;) > > On 15.09.2014, at 13:50, andy petrella <andy.petre...@gmail.com> wrote: > >> I'm using Parquet in ADAM, and I can say that it works pretty fine! >> Enjoy ;-) >> >> aℕdy ℙetrella >> 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 >>> >>> >>> >>> 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 >>> >>> >>> >>> >> >> > >
