Isn't sqoop export meant for that? http://hadooped.blogspot.it/2013/06/apache-sqoop-part-3-data-transfer.html?m=1 On Aug 7, 2014 7:59 PM, "Nicholas Chammas" <nicholas.cham...@gmail.com> wrote:
> Vida, > > What kind of database are you trying to write to? > > For example, I found that for loading into Redshift, by far the easiest > thing to do was to save my output from Spark as a CSV to S3, and then load > it from there into Redshift. This is not a slow as you think, because Spark > can write the output in parallel to S3, and Redshift, too, can load data > from multiple files in parallel > <http://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-single-copy-command.html> > . > > Nick > > > On Thu, Aug 7, 2014 at 1:52 PM, Vida Ha <v...@databricks.com> wrote: > >> The use case I was thinking of was outputting calculations made in Spark >> into a SQL database for the presentation layer to access. So in other >> words, having a Spark backend in Java that writes to a SQL database and >> then having a Rails front-end that can display the data nicely. >> >> >> On Thu, Aug 7, 2014 at 8:42 AM, Nicholas Chammas < >> nicholas.cham...@gmail.com> wrote: >> >>> On Thu, Aug 7, 2014 at 11:25 AM, Cheng Lian <lian.cs....@gmail.com> >>> wrote: >>> >>>> Maybe a little off topic, but would you mind to share your motivation >>>> of saving the RDD into an SQL DB? >>> >>> >>> Many possible reasons (Vida, please chime in with yours!): >>> >>> - You have an existing database you want to load new data into so >>> everything's together. >>> - You want very low query latency, which you can probably get with >>> Spark SQL but currently not with the ease you can get it from your >>> average >>> DBMS. >>> - Tooling around traditional DBMSs is currently much more mature >>> than tooling around Spark SQL, especially in the JDBC area. >>> >>> Nick >>> >> >> >
