I think the TEZ engine is much more maintained with respect to optimizations related to Orc , hive , vectorizing, querying than the mr engine. It will be definitely better to use it. Mr is also deprecated in hive 2.0. For me it does not make sense to use mr with hive larger than 1.1.
As I said, order by might be inefficient to use (not sure if this has changed). You may want to use sort by. That being said there are many optimizations methods. > On 29 Jun 2016, at 00:27, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > > That is a good point. > > The ORC table property is as follows > > TBLPROPERTIES ( "orc.compress"="SNAPPY", > "orc.stripe.size"="268435456", > "orc.row.index.stride"="10000") > > which puts each stripe at 256MB > > Just to clarify this is spark running on Hive tables. I don't think the use > of TEZ, MR or Spark as execution engines is going to make any difference? > > This is the same query with Hive on MR > > select a.prod_id from sales2 a, sales_staging b where a.prod_id = b.prod_id > order by a.prod_id; > > 2016-06-28 23:23:51,203 Stage-1 map = 0%, reduce = 0% > 2016-06-28 23:23:59,480 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 7.32 > sec > 2016-06-28 23:24:08,771 Stage-1 map = 55%, reduce = 0%, Cumulative CPU 18.21 > sec > 2016-06-28 23:24:11,860 Stage-1 map = 58%, reduce = 0%, Cumulative CPU 22.34 > sec > 2016-06-28 23:24:18,021 Stage-1 map = 62%, reduce = 0%, Cumulative CPU 30.33 > sec > 2016-06-28 23:24:21,101 Stage-1 map = 64%, reduce = 0%, Cumulative CPU 33.45 > sec > 2016-06-28 23:24:24,181 Stage-1 map = 66%, reduce = 0%, Cumulative CPU 37.5 > sec > 2016-06-28 23:24:27,270 Stage-1 map = 69%, reduce = 0%, Cumulative CPU 42.0 > sec > 2016-06-28 23:24:30,349 Stage-1 map = 70%, reduce = 0%, Cumulative CPU 45.62 > sec > 2016-06-28 23:24:33,441 Stage-1 map = 73%, reduce = 0%, Cumulative CPU 49.69 > sec > 2016-06-28 23:24:36,521 Stage-1 map = 75%, reduce = 0%, Cumulative CPU 52.92 > sec > 2016-06-28 23:24:39,605 Stage-1 map = 77%, reduce = 0%, Cumulative CPU 56.78 > sec > 2016-06-28 23:24:42,686 Stage-1 map = 80%, reduce = 0%, Cumulative CPU 60.36 > sec > 2016-06-28 23:24:45,767 Stage-1 map = 81%, reduce = 0%, Cumulative CPU 63.68 > sec > 2016-06-28 23:24:48,842 Stage-1 map = 83%, reduce = 0%, Cumulative CPU 66.92 > sec > 2016-06-28 23:24:51,918 Stage-1 map = 100%, reduce = 0%, Cumulative CPU > 70.18 sec > 2016-06-28 23:25:52,354 Stage-1 map = 100%, reduce = 0%, Cumulative CPU > 127.99 sec > 2016-06-28 23:25:57,494 Stage-1 map = 100%, reduce = 67%, Cumulative CPU > 134.64 sec > 2016-06-28 23:26:57,847 Stage-1 map = 100%, reduce = 67%, Cumulative CPU > 141.01 sec > > which basically sits at 67% all day > > > > > > Dr Mich Talebzadeh > > LinkedIn > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > http://talebzadehmich.wordpress.com > > Disclaimer: Use it at your own risk. Any and all responsibility for any loss, > damage or destruction of data or any other property which may arise from > relying on this email's technical content is explicitly disclaimed. The > author will in no case be liable for any monetary damages arising from such > loss, damage or destruction. > > >> On 28 June 2016 at 23:07, Jörn Franke <jornfra...@gmail.com> wrote: >> >> >> Bzip2 is splittable for text files. >> >> Btw in Orc the question of splittable does not matter because each stripe is >> compressed individually. >> >> Have you tried tez? As far as I recall (at least it was in the first version >> of Hive) mr uses for order by a single reducer which is a bottleneck. >> >> Do you see some errors in the log file? >> >>> On 28 Jun 2016, at 23:53, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: >>> >>> Hi, >>> >>> >>> I have a simple join between table sales2 a compressed (snappy) ORC with 22 >>> million rows and another simple table sales_staging under a million rows >>> stored as a text file with no compression. >>> >>> The join is very simple >>> >>> val s2 = HiveContext.table("sales2").select("PROD_ID") >>> val s = HiveContext.table("sales_staging").select("PROD_ID") >>> >>> val rs = >>> s2.join(s,"prod_id").orderBy("prod_id").sort(desc("prod_id")).take(5).foreach(println) >>> >>> >>> Now what is happening is it is sitting on SortMergeJoin operation on >>> ZippedPartitionRDD as shown in the DAG diagram below >>> >>> >>> <image.png> >>> >>> >>> And at this rate only 10% is done and will take for ever to finish :( >>> >>> Stage 3:==> (10 + 2) / >>> 200] >>> >>> Ok I understand that zipped files cannot be broken into blocks and >>> operations on them cannot be parallelized. >>> >>> Having said that what are the alternatives? Never use compression and live >>> with it. I emphasise that any operation on the compressed table itself is >>> pretty fast as it is a simple table scan. However, a join between two >>> tables on a column as above suggests seems to be problematic? >>> >>> Thanks >>> >>> P.S. the same is happening using Hive with MR >>> >>> select a.prod_id from sales2 a inner join sales_staging b on a.prod_id = >>> b.prod_id order by a.prod_id; >>> >>> Dr Mich Talebzadeh >>> >>> LinkedIn >>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> >>> http://talebzadehmich.wordpress.com >>> >>> Disclaimer: Use it at your own risk. Any and all responsibility for any >>> loss, damage or destruction of data or any other property which may arise >>> from relying on this email's technical content is explicitly disclaimed. >>> The author will in no case be liable for any monetary damages arising from >>> such loss, damage or destruction. >
