1) Apache Phoenix <https://phoenix.apache.org> + Mondrian <http://community.pentaho.com/projects/mondrian/> 2) Apache Spark <http://spark.apache.org/>
On Thu, Nov 5, 2015 at 2:49 PM, Jörn Franke <jornfra...@gmail.com> wrote: > First it depends on what you want to do exactly. Second, Hive > 1.2, Tez > as an Execution Engine (I recommend >= 0.8) and Orc as storage format can > be pretty quick depending on your use case. Additionally you may want to > employ compression which is a performance boost once you understand how > storage indexes and bloom filter work. Additionally , you need to think > about how you sort the data. Cf. also > > https://snippetessay.wordpress.com/2015/07/25/hive-optimizations-with-indexes-bloom-filters-and-statistics/ > > However, you have to rethink how you define your technical data model. A > lot of prejoinend data in a big flat table can be more performant when > using storage indexes and bloom filters than using standard indexes and > dimensional modeling. > > Besides besides tez you can also use other execution engine in your > session (eg Spark) if this makes sense. > > Finally you have to review how yarn manages resources including > preemption, fair vs capacity scheduler etc. > > Btw the same holds also for relational database appliances, such as > Exadata. The standard approach dimensional modeling + standard indexes > there is often not anymore the most performant. > > > > > On 05 Nov 2015, at 20:04, Andrés Ivaldi <iaiva...@gmail.com> wrote: > > > > Hello, > > I was looking for Hive as OLAP alternative, but I've read that is quite > slow for that, does anybody have experiences about? or a Hive altenative > for OLAP? Killin is not an option becouse we need dynamic OLAP like ROLAP > > > > Regards, > > > > -- > > Ing. Ivaldi Andres >
