- dev list & + user list Shark is not officially supported anymore so you are better off moving to Spark SQL. Shark doesnt support Hive partitioning logic anyways, it has its version of partitioning on in-memory blocks but is independent of whether you partition your data in hive or not.
Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi <https://twitter.com/mayur_rustagi> On Fri, Nov 7, 2014 at 3:31 AM, Gordon Benjamin <gordon.benjami...@gmail.com > wrote: > Hi All, > > I'm using Spark/Shark as the foundation for some reporting that I'm doing > and have a customers table with approximately 3 million rows that I've > cached in memory. > > I've also created a partitioned table that I've also cached in memory on a > per day basis > > FROM > customers_cached > INSERT OVERWRITE TABLE > part_customers_cached > PARTITION(createday) > SELECT id,email,dt_cr, to_date(dt_cr) as createday where > dt_cr>unix_timestamp('2013-01-01 00:00:00') and > dt_cr<unix_timestamp('2013-12-31 23:59:59'); > set exec.dynamic.partition=true; > > set exec.dynamic.partition.mode=nonstrict; > > however when I run the following basic tests I get this type of performance > > [localhost:10000] shark> select count(*) from part_customers_cached where > createday >= '2014-08-01' and createday <= '2014-12-06'; > 37204 > Time taken (including network latency): 3.131 seconds > > [localhost:10000] shark> SELECT count(*) from customers_cached where > dt_cr>unix_timestamp('2013-08-01 00:00:00') and > dt_cr<unix_timestamp('2013-12-06 23:59:59'); > 37204 > Time taken (including network latency): 1.538 seconds > > I'm running this on a cluster with one master and two slaves and was hoping > that the partitioned table would be noticeably faster but it looks as > though the partitioning has slowed things down... Is this the case, or is > there some additional configuration that I need to do to speed things up? > > Best Wishes, > > Gordon >
