Awesome news ! It will be great if there are any examples or usecases to look at ?
We are looking into shark/ooyala job server to give in memory sql analytics, model serving/scoring features for dashboard apps... Does this feature has different usecases than shark or more cleaner as hive dependency is gone? Thanks. Deb On Mar 21, 2014 10:13 AM, "Matei Zaharia" <matei.zaha...@gmail.com> wrote: > Congrats Michael and all for getting this so far. Spark SQL and Catalyst > will make it much easier to use structured data in Spark, and open the door > for some very cool extensions later. > > Matei > > On Mar 20, 2014, at 11:15 PM, Heiko Braun <ike.br...@googlemail.com> > wrote: > > > Congrats! That's a really impressive and useful addition to spark. I > just recently discovered a similar feature in pandas and really enjoyed > using it. > > > > Regards, Heiko > > > > > > > > > >> Am 21.03.2014 um 02:11 schrieb Reynold Xin <r...@databricks.com>: > >> > >> Hi All, > >> > >> I'm excited to announce a new module in Spark (SPARK-1251). After an > >> initial review we've merged this as Spark as an alpha component to be > >> included in Spark 1.0. This new component adds some exciting features, > >> including: > >> > >> - schema-aware RDD programming via an experimental DSL > >> - native Parquet support > >> - support for executing SQL against RDDs > >> > >> The pull request itself contains more information: > >> https://github.com/apache/spark/pull/146 > >> > >> You can also find the documentation for this new component here: > >> > http://people.apache.org/~pwendell/catalyst-docs/sql-programming-guide.html > >> > >> > >> This contribution was lead by Michael Ambrust with work from several > other > >> contributors who I'd like to highlight here: Yin Huai, Cheng Lian, Andre > >> Schumacher, Timothy Chen, Henry Cook, and Mark Hamstra. > >> > >> > >> - Reynold > >
