This is what I am getting in the container log for mr 2016-06-28 23:25:53,808 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: Writing to temp file: FS hdfs://rhes564:9000/tmp/hive/hduser/71a6beee-ac0d-423a-a14e-6ce51667a441/hive_2016-06-28_23-23-42_929_6384631032208608956-1/_task_tmp.-mr-10004/_tmp.000000_0 2016-06-28 23:25:53,808 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: New Final Path: FS hdfs://rhes564:9000/tmp/hive/hduser/71a6beee-ac0d-423a-a14e-6ce51667a441/hive_2016-06-28_23-23-42_929_6384631032208608956-1/_tmp.-mr-10004/000000_0 2016-06-28 23:25:53,836 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 1 2016-06-28 23:25:53,837 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 10 2016-06-28 23:25:53,837 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 100 2016-06-28 23:25:53,844 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 1000 2016-06-28 23:25:53,875 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 10000 2016-06-28 23:25:53,954 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 100000 2016-06-28 23:25:55,072 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 1000000 2016-06-28 23:26:56,236 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 10000000 2016-06-28 23:27:58,499 WARN [ResponseProcessor for block BP-1648199869-50.140.197.217-1462266926537:blk_1074784072_1043287] org.apache.hadoop.hdfs.DFSClient: Slow ReadProcessor read fields took 35556ms (threshold=30000ms); ack: seqno: 6815 status: SUCCESS status: SUCCESS downstreamAckTimeNanos: 35566795000, targets: [50.140.197.217:50010, 50.140.197.216:50010] 2016-06-28 23:31:38,437 INFO [main] org.apache.hadoop.hive.ql.exec.FileSinkOperator: FS[15]: records written - 100000000 2016-06-28 23:35:27,631 WARN [ResponseProcessor for block BP-1648199869-50.140.197.217-1462266926537:blk_1074784086_1043301] org.apache.hadoop.hdfs.DFSClient: *Slow ReadProcessor read fields took 31118ms (threshold=30000ms);* ack: seqno: 36303 status: SUCCESS status: SUCCESS downstreamAckTimeNanos: 31128701000, targets: [50.140.197.217:50010, 50.140.197.216:50010]
Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <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 23:27, 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 > <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 >> <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. >> >> >> >> >
