partitioning to speed up queries

[Gordon Benjamin <go...@gmail.com>]

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