I guess that is what DAG adds up to with Tez
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 12 July 2016 at 14:40, Marcin Tustin <mtus...@handybook.com> wrote: > More like 2x than 10x as I recall. > > On Tue, Jul 12, 2016 at 9:39 AM, Mich Talebzadeh < > mich.talebza...@gmail.com> wrote: > >> thanks Marcin. >> >> What Is your guesstimate on the order of "faster" please? >> >> Cheers >> >> 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 12 July 2016 at 14:35, Marcin Tustin <mtus...@handybook.com> wrote: >> >>> Quick note - my experience (no benchmarks) is that Tez without LLAP >>> (we're still not on hive 2) is faster than MR by some way. I haven't dug >>> into why that might be. >>> >>> On Tue, Jul 12, 2016 at 9:19 AM, Mich Talebzadeh < >>> mich.talebza...@gmail.com> wrote: >>> >>>> sorry I completely miss your points >>>> >>>> I was NOT talking about Exadata. I was comparing Oracle 12c caching >>>> with that of Oracle TimesTen. no one mentioned Exadata here and neither >>>> storeindex etc.. >>>> >>>> >>>> so if Tez is not MR with DAG could you give me an example of how it >>>> works. No opinions but relevant to this point. I do not know much about Tez >>>> as I stated it before >>>> >>>> Case in point if Tez could do the job on its own why Tez is used in >>>> conjunction with LLAP as Martin alluded to as well in this thread. >>>> >>>> >>>> Having said that , I would be interested if you provide a working >>>> example of Hive on Tez, compared to Hive on MR. >>>> >>>> One experiment is worth hundreds of opinions >>>> >>>> >>>> >>>> >>>> >>>> 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 12 July 2016 at 13:31, Jörn Franke <jornfra...@gmail.com> wrote: >>>> >>>>> >>>>> I think the comparison with Oracle rdbms and oracle times ten is not >>>>> so good. There are times when the in-memory database of Oracle is slower >>>>> than the rdbms (especially in case of Exadata) due to the issue that >>>>> in-memory - as in Spark - means everything is in memory and everything is >>>>> always processed (no storage indexes , no bloom filters etc) which >>>>> explains >>>>> this behavior quiet well. >>>>> >>>>> Hence, I do not agree with the statement that tez is basically mr with >>>>> dag (or that llap is basically in-memory which is also not correct). This >>>>> is a wrong oversimplification and I do not think this is useful for the >>>>> community, but better is to understand when something can be used and when >>>>> not. In-memory is also not the solution to everything and if you look for >>>>> example behind SAP Hana or NoSql there is much more around this, which is >>>>> not even on the roadmap of Spark. >>>>> >>>>> Anyway, discovering good use case patterns should be done on >>>>> standardized benchmarks going beyond the select count etc >>>>> >>>>> On 12 Jul 2016, at 11:16, Mich Talebzadeh <mich.talebza...@gmail.com> >>>>> wrote: >>>>> >>>>> That is only a plan not what execution engine is doing. >>>>> >>>>> As I stated before Spark uses DAG + in-memory computing. MR is serial >>>>> on disk. >>>>> >>>>> The key is the execution here or rather the execution engine. >>>>> >>>>> In general >>>>> >>>>> The standard MapReduce as I know reads the data from HDFS, apply >>>>> map-reduce algorithm and writes back to HDFS. If there are many iterations >>>>> of map-reduce then, there will be many intermediate writes to HDFS. This >>>>> is >>>>> all serial writes to disk. Each map-reduce step is completely independent >>>>> of other steps, and the executing engine does not have any global >>>>> knowledge >>>>> of what map-reduce steps are going to come after each map-reduce step. For >>>>> many iterative algorithms this is inefficient as the data between each >>>>> map-reduce pair gets written and read from the file system. >>>>> >>>>> The equivalent to parallelism in Big Data is deploying what is known >>>>> as Directed Acyclic Graph (DAG >>>>> <https://en.wikipedia.org/wiki/Directed_acyclic_graph>) algorithm. In >>>>> a nutshell deploying DAG results in a fuller picture of global >>>>> optimisation >>>>> by deploying parallelism, pipelining consecutive map steps into one and >>>>> not >>>>> writing intermediate data to HDFS. So in short this prevents writing data >>>>> back and forth after every reduce step which for me is a significant >>>>> improvement, compared to the classical MapReduce algorithm. >>>>> >>>>> Now Tez is basically MR with DAG. With Spark you get DAG + in-memory >>>>> computing. Think of it as a comparison between a classic RDBMS like Oracle >>>>> and IMDB like Oracle TimesTen with in-memory processing. >>>>> >>>>> The outcome is that Hive using Spark as execution engine is pretty >>>>> impressive. You have the advantage of Hive CBO + In-memory computing. If >>>>> you use Spark for all this (say Spark SQL) but no Hive, Spark uses its own >>>>> optimizer called Catalyst that does not have CBO yet plus in memory >>>>> computing. >>>>> >>>>> As usual your mileage varies. >>>>> >>>>> HTH >>>>> >>>>> >>>>> 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 12 July 2016 at 09:33, Markovitz, Dudu <dmarkov...@paypal.com> >>>>> wrote: >>>>> >>>>>> I don’t see how this explains the time differences. >>>>>> >>>>>> >>>>>> >>>>>> Dudu >>>>>> >>>>>> >>>>>> >>>>>> *From:* Mich Talebzadeh [mailto:mich.talebza...@gmail.com] >>>>>> *Sent:* Tuesday, July 12, 2016 10:56 AM >>>>>> *To:* user <u...@hive.apache.org> >>>>>> *Cc:* user @spark <user@spark.apache.org> >>>>>> >>>>>> *Subject:* Re: Using Spark on Hive with Hive also using Spark as its >>>>>> execution engine >>>>>> >>>>>> >>>>>> >>>>>> This the whole idea. Spark uses DAG + IM, MR is classic >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> This is for Hive on Spark >>>>>> >>>>>> >>>>>> >>>>>> hive> explain select max(id) from dummy_parquet; >>>>>> OK >>>>>> STAGE DEPENDENCIES: >>>>>> Stage-1 is a root stage >>>>>> Stage-0 depends on stages: Stage-1 >>>>>> >>>>>> STAGE PLANS: >>>>>> Stage: Stage-1 >>>>>> Spark >>>>>> Edges: >>>>>> Reducer 2 <- Map 1 (GROUP, 1) >>>>>> * DagName: >>>>>> hduser_20160712083219_632c2749-7387-478f-972d-9eaadd9932c6:1* >>>>>> Vertices: >>>>>> Map 1 >>>>>> Map Operator Tree: >>>>>> TableScan >>>>>> alias: dummy_parquet >>>>>> Statistics: Num rows: 100000000 Data size: >>>>>> 700000000 Basic stats: COMPLETE Column stats: NONE >>>>>> Select Operator >>>>>> expressions: id (type: int) >>>>>> outputColumnNames: id >>>>>> Statistics: Num rows: 100000000 Data size: >>>>>> 700000000 Basic stats: COMPLETE Column stats: NONE >>>>>> Group By Operator >>>>>> aggregations: max(id) >>>>>> mode: hash >>>>>> outputColumnNames: _col0 >>>>>> Statistics: Num rows: 1 Data size: 4 Basic >>>>>> stats: COMPLETE Column stats: NONE >>>>>> Reduce Output Operator >>>>>> sort order: >>>>>> Statistics: Num rows: 1 Data size: 4 Basic >>>>>> stats: COMPLETE Column stats: NONE >>>>>> value expressions: _col0 (type: int) >>>>>> Reducer 2 >>>>>> Reduce Operator Tree: >>>>>> Group By Operator >>>>>> aggregations: max(VALUE._col0) >>>>>> mode: mergepartial >>>>>> outputColumnNames: _col0 >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: >>>>>> COMPLETE Column stats: NONE >>>>>> File Output Operator >>>>>> compressed: false >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: >>>>>> COMPLETE Column stats: NONE >>>>>> table: >>>>>> input format: >>>>>> org.apache.hadoop.mapred.TextInputFormat >>>>>> output format: >>>>>> org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat >>>>>> serde: >>>>>> org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe >>>>>> >>>>>> Stage: Stage-0 >>>>>> Fetch Operator >>>>>> limit: -1 >>>>>> Processor Tree: >>>>>> ListSink >>>>>> >>>>>> Time taken: 2.801 seconds, Fetched: 50 row(s) >>>>>> >>>>>> >>>>>> >>>>>> And this is with setting the execution engine to MR >>>>>> >>>>>> >>>>>> >>>>>> hive> set hive.execution.engine=mr; >>>>>> Hive-on-MR is deprecated in Hive 2 and may not be available in the >>>>>> future versions. Consider using a different execution engine (i.e. spark, >>>>>> tez) or using Hive 1.X releases. >>>>>> >>>>>> >>>>>> >>>>>> hive> explain select max(id) from dummy_parquet; >>>>>> OK >>>>>> STAGE DEPENDENCIES: >>>>>> Stage-1 is a root stage >>>>>> Stage-0 depends on stages: Stage-1 >>>>>> >>>>>> STAGE PLANS: >>>>>> Stage: Stage-1 >>>>>> Map Reduce >>>>>> Map Operator Tree: >>>>>> TableScan >>>>>> alias: dummy_parquet >>>>>> Statistics: Num rows: 100000000 Data size: 700000000 >>>>>> Basic stats: COMPLETE Column stats: NONE >>>>>> Select Operator >>>>>> expressions: id (type: int) >>>>>> outputColumnNames: id >>>>>> Statistics: Num rows: 100000000 Data size: 700000000 >>>>>> Basic stats: COMPLETE Column stats: NONE >>>>>> Group By Operator >>>>>> aggregations: max(id) >>>>>> mode: hash >>>>>> outputColumnNames: _col0 >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: >>>>>> COMPLETE Column stats: NONE >>>>>> Reduce Output Operator >>>>>> sort order: >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: >>>>>> COMPLETE Column stats: NONE >>>>>> value expressions: _col0 (type: int) >>>>>> Reduce Operator Tree: >>>>>> Group By Operator >>>>>> aggregations: max(VALUE._col0) >>>>>> mode: mergepartial >>>>>> outputColumnNames: _col0 >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE >>>>>> Column stats: NONE >>>>>> File Output Operator >>>>>> compressed: false >>>>>> Statistics: Num rows: 1 Data size: 4 Basic stats: >>>>>> COMPLETE Column stats: NONE >>>>>> table: >>>>>> input format: org.apache.hadoop.mapred.TextInputFormat >>>>>> output format: >>>>>> org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat >>>>>> serde: >>>>>> org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe >>>>>> >>>>>> Stage: Stage-0 >>>>>> Fetch Operator >>>>>> limit: -1 >>>>>> Processor Tree: >>>>>> ListSink >>>>>> >>>>>> Time taken: 0.1 seconds, Fetched: 44 row(s) >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> HTH >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> 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 12 July 2016 at 08:16, Markovitz, Dudu <dmarkov...@paypal.com> >>>>>> wrote: >>>>>> >>>>>> This is a simple task – >>>>>> >>>>>> Read the files, find the local max value and combine the results >>>>>> (find the global max value). >>>>>> >>>>>> How do you explain the differences in the results? Spark reads the >>>>>> files and finds a local max 10X (+) faster than MR? >>>>>> >>>>>> Can you please attach the execution plan? >>>>>> >>>>>> >>>>>> >>>>>> Thanks >>>>>> >>>>>> >>>>>> >>>>>> Dudu >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> *From:* Mich Talebzadeh [mailto:mich.talebza...@gmail.com] >>>>>> *Sent:* Monday, July 11, 2016 11:55 PM >>>>>> *To:* user <u...@hive.apache.org>; user @spark <user@spark.apache.org >>>>>> > >>>>>> *Subject:* Re: Using Spark on Hive with Hive also using Spark as its >>>>>> execution engine >>>>>> >>>>>> >>>>>> >>>>>> In my test I did like for like keeping the systematic the same namely: >>>>>> >>>>>> >>>>>> >>>>>> 1. Table was a parquet table of 100 Million rows >>>>>> 2. The same set up was used for both Hive on Spark and Hive on MR >>>>>> 3. Spark was very impressive compared to MR on this particular >>>>>> test. >>>>>> >>>>>> >>>>>> >>>>>> Just to see any issues I created an ORC table in in the image of >>>>>> Parquet (insert/select from Parquet to ORC) with stats updated for >>>>>> columns >>>>>> etc >>>>>> >>>>>> >>>>>> >>>>>> These were the results of the same run using ORC table this time: >>>>>> >>>>>> >>>>>> >>>>>> hive> select max(id) from oraclehadoop.dummy; >>>>>> >>>>>> Starting Spark Job = b886b869-5500-4ef7-aab9-ae6fb4dad22b >>>>>> >>>>>> Query Hive on Spark job[1] stages: >>>>>> 2 >>>>>> 3 >>>>>> >>>>>> Status: Running (Hive on Spark job[1]) >>>>>> Job Progress Format >>>>>> CurrentTime StageId_StageAttemptId: >>>>>> SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount >>>>>> [StageCost] >>>>>> 2016-07-11 21:35:45,020 Stage-2_0: 0(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:48,033 Stage-2_0: 0(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:51,046 Stage-2_0: 1(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:52,050 Stage-2_0: 3(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:53,055 Stage-2_0: 8(+4)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:54,060 Stage-2_0: 11(+1)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:55,065 Stage-2_0: 12(+0)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:56,071 Stage-2_0: 12(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:57,076 Stage-2_0: 13(+8)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:58,081 Stage-2_0: 20(+3)/23 Stage-3_0: 0/1 >>>>>> 2016-07-11 21:35:59,085 Stage-2_0: 23/23 Finished Stage-3_0: >>>>>> 0(+1)/1 >>>>>> 2016-07-11 21:36:00,089 Stage-2_0: 23/23 Finished Stage-3_0: >>>>>> 1/1 Finished >>>>>> Status: Finished successfully in 16.08 seconds >>>>>> OK >>>>>> 100000000 >>>>>> Time taken: 17.775 seconds, Fetched: 1 row(s) >>>>>> >>>>>> >>>>>> >>>>>> Repeat with MR engine >>>>>> >>>>>> >>>>>> >>>>>> hive> set hive.execution.engine=mr; >>>>>> Hive-on-MR is deprecated in Hive 2 and may not be available in the >>>>>> future versions. Consider using a different execution engine (i.e. spark, >>>>>> tez) or using Hive 1.X releases. >>>>>> >>>>>> >>>>>> >>>>>> hive> select max(id) from oraclehadoop.dummy; >>>>>> WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available >>>>>> in the future versions. Consider using a different execution engine (i.e. >>>>>> spark, tez) or using Hive 1.X releases. >>>>>> Query ID = hduser_20160711213100_8dc2afae-8644-4097-ba33-c7bd3c304bf8 >>>>>> Total jobs = 1 >>>>>> Launching Job 1 out of 1 >>>>>> Number of reduce tasks determined at compile time: 1 >>>>>> In order to change the average load for a reducer (in bytes): >>>>>> set hive.exec.reducers.bytes.per.reducer=<number> >>>>>> In order to limit the maximum number of reducers: >>>>>> set hive.exec.reducers.max=<number> >>>>>> In order to set a constant number of reducers: >>>>>> set mapreduce.job.reduces=<number> >>>>>> Starting Job = job_1468226887011_0008, Tracking URL = >>>>>> http://rhes564:8088/proxy/application_1468226887011_0008/ >>>>>> Kill Command = /home/hduser/hadoop-2.6.0/bin/hadoop job -kill >>>>>> job_1468226887011_0008 >>>>>> Hadoop job information for Stage-1: number of mappers: 23; number of >>>>>> reducers: 1 >>>>>> 2016-07-11 21:37:00,061 Stage-1 map = 0%, reduce = 0% >>>>>> 2016-07-11 21:37:06,440 Stage-1 map = 4%, reduce = 0%, Cumulative >>>>>> CPU 16.48 sec >>>>>> 2016-07-11 21:37:14,751 Stage-1 map = 9%, reduce = 0%, Cumulative >>>>>> CPU 40.63 sec >>>>>> 2016-07-11 21:37:22,048 Stage-1 map = 13%, reduce = 0%, Cumulative >>>>>> CPU 58.88 sec >>>>>> 2016-07-11 21:37:30,412 Stage-1 map = 17%, reduce = 0%, Cumulative >>>>>> CPU 80.72 sec >>>>>> 2016-07-11 21:37:37,707 Stage-1 map = 22%, reduce = 0%, Cumulative >>>>>> CPU 103.43 sec >>>>>> 2016-07-11 21:37:45,999 Stage-1 map = 26%, reduce = 0%, Cumulative >>>>>> CPU 125.93 sec >>>>>> 2016-07-11 21:37:54,300 Stage-1 map = 30%, reduce = 0%, Cumulative >>>>>> CPU 147.17 sec >>>>>> 2016-07-11 21:38:01,538 Stage-1 map = 35%, reduce = 0%, Cumulative >>>>>> CPU 166.56 sec >>>>>> 2016-07-11 21:38:08,807 Stage-1 map = 39%, reduce = 0%, Cumulative >>>>>> CPU 189.29 sec >>>>>> 2016-07-11 21:38:17,115 Stage-1 map = 43%, reduce = 0%, Cumulative >>>>>> CPU 211.03 sec >>>>>> 2016-07-11 21:38:24,363 Stage-1 map = 48%, reduce = 0%, Cumulative >>>>>> CPU 235.68 sec >>>>>> 2016-07-11 21:38:32,638 Stage-1 map = 52%, reduce = 0%, Cumulative >>>>>> CPU 258.27 sec >>>>>> 2016-07-11 21:38:40,916 Stage-1 map = 57%, reduce = 0%, Cumulative >>>>>> CPU 278.44 sec >>>>>> 2016-07-11 21:38:49,206 Stage-1 map = 61%, reduce = 0%, Cumulative >>>>>> CPU 300.35 sec >>>>>> 2016-07-11 21:38:58,524 Stage-1 map = 65%, reduce = 0%, Cumulative >>>>>> CPU 322.89 sec >>>>>> 2016-07-11 21:39:07,889 Stage-1 map = 70%, reduce = 0%, Cumulative >>>>>> CPU 344.8 sec >>>>>> 2016-07-11 21:39:16,151 Stage-1 map = 74%, reduce = 0%, Cumulative >>>>>> CPU 367.77 sec >>>>>> 2016-07-11 21:39:25,456 Stage-1 map = 78%, reduce = 0%, Cumulative >>>>>> CPU 391.82 sec >>>>>> 2016-07-11 21:39:33,725 Stage-1 map = 83%, reduce = 0%, Cumulative >>>>>> CPU 415.48 sec >>>>>> 2016-07-11 21:39:43,037 Stage-1 map = 87%, reduce = 0%, Cumulative >>>>>> CPU 436.09 sec >>>>>> 2016-07-11 21:39:51,292 Stage-1 map = 91%, reduce = 0%, Cumulative >>>>>> CPU 459.4 sec >>>>>> 2016-07-11 21:39:59,563 Stage-1 map = 96%, reduce = 0%, Cumulative >>>>>> CPU 477.92 sec >>>>>> 2016-07-11 21:40:05,760 Stage-1 map = 100%, reduce = 0%, Cumulative >>>>>> CPU 491.72 sec >>>>>> 2016-07-11 21:40:10,921 Stage-1 map = 100%, reduce = 100%, >>>>>> Cumulative CPU 499.37 sec >>>>>> MapReduce Total cumulative CPU time: 8 minutes 19 seconds 370 msec >>>>>> Ended Job = job_1468226887011_0008 >>>>>> MapReduce Jobs Launched: >>>>>> Stage-Stage-1: Map: 23 Reduce: 1 Cumulative CPU: 499.37 sec HDFS >>>>>> Read: 403754774 HDFS Write: 10 SUCCESS >>>>>> Total MapReduce CPU Time Spent: 8 minutes 19 seconds 370 msec >>>>>> OK >>>>>> 100000000 >>>>>> Time taken: 202.333 seconds, Fetched: 1 row(s) >>>>>> >>>>>> >>>>>> >>>>>> So in summary >>>>>> >>>>>> >>>>>> >>>>>> Table MR/sec Spark/sec >>>>>> >>>>>> Parquet 239.532 14.38 >>>>>> >>>>>> ORC 202.333 17.77 >>>>>> >>>>>> >>>>>> >>>>>> Still I would use Spark if I had a choice and I agree that on VLT >>>>>> (very large tables), the limitation in available memory may be the >>>>>> overriding factor in using Spark. >>>>>> >>>>>> >>>>>> >>>>>> HTH >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> 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 11 July 2016 at 19:25, Gopal Vijayaraghavan <gop...@apache.org> >>>>>> wrote: >>>>>> >>>>>> >>>>>> > Status: Finished successfully in 14.12 seconds >>>>>> > OK >>>>>> > 100000000 >>>>>> > Time taken: 14.38 seconds, Fetched: 1 row(s) >>>>>> >>>>>> That might be an improvement over MR, but that still feels far too >>>>>> slow. >>>>>> >>>>>> >>>>>> Parquet numbers are in general bad in Hive, but that's because the >>>>>> Parquet >>>>>> reader gets no actual love from the devs. The community, if it wants >>>>>> to >>>>>> keep using Parquet heavily needs a Hive dev to go over to Parquet-mr >>>>>> and >>>>>> cut a significant number of memory copies out of the reader. >>>>>> >>>>>> The Spark 2.0 build for instance, has a custom Parquet reader for >>>>>> SparkSQL >>>>>> which does this. SPARK-12854 does for Spark+Parquet what Hive 2.0 >>>>>> does for >>>>>> ORC (actually, it looks more like hive's VectorizedRowBatch than >>>>>> Tungsten's flat layouts). >>>>>> >>>>>> But that reader cannot be used in Hive-on-Spark, because it is not a >>>>>> public reader impl. >>>>>> >>>>>> >>>>>> Not to pick an arbitrary dataset, my workhorse example is a TPC-H >>>>>> lineitem >>>>>> at 10Gb scale with a single 16 box. >>>>>> >>>>>> hive(tpch_flat_orc_10)> select max(l_discount) from lineitem; >>>>>> Query ID = gopal_20160711175917_f96371aa-2721-49c8-99a0-f7c4a1eacfda >>>>>> Total jobs = 1 >>>>>> Launching Job 1 out of 1 >>>>>> >>>>>> >>>>>> Status: Running (Executing on YARN cluster with App id >>>>>> application_1466700718395_0256) >>>>>> >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> VERTICES MODE STATUS TOTAL COMPLETED RUNNING >>>>>> PENDING FAILED KILLED >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> Map 1 .......... llap SUCCEEDED 13 13 0 >>>>>> 0 0 0 >>>>>> Reducer 2 ...... llap SUCCEEDED 1 1 0 >>>>>> 0 0 0 >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> VERTICES: 02/02 [==========================>>] 100% ELAPSED TIME: >>>>>> 0.71 s >>>>>> >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> Status: DAG finished successfully in 0.71 seconds >>>>>> >>>>>> Query Execution Summary >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> OPERATION DURATION >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> Compile Query 0.21s >>>>>> Prepare Plan 0.13s >>>>>> Submit Plan 0.34s >>>>>> Start DAG 0.23s >>>>>> Run DAG 0.71s >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> >>>>>> Task Execution Summary >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> VERTICES DURATION(ms) CPU_TIME(ms) GC_TIME(ms) INPUT_RECORDS >>>>>> OUTPUT_RECORDS >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> Map 1 604.00 0 0 59,957,438 >>>>>> 13 >>>>>> Reducer 2 105.00 0 0 13 >>>>>> 0 >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> >>>>>> LLAP IO Summary >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> VERTICES ROWGROUPS META_HIT META_MISS DATA_HIT DATA_MISS >>>>>> ALLOCATION >>>>>> USED TOTAL_IO >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> Map 1 6036 0 146 0B 68.86MB >>>>>> 491.00MB >>>>>> 479.89MB 7.94s >>>>>> >>>>>> --------------------------------------------------------------------------- >>>>>> ------------------- >>>>>> >>>>>> OK >>>>>> 0.1 >>>>>> Time taken: 1.669 seconds, Fetched: 1 row(s) >>>>>> hive(tpch_flat_orc_10)> >>>>>> >>>>>> >>>>>> This is running against a single 16 core box & I would assume it would >>>>>> take <1.4s to read twice as much (13 tasks is barely touching the load >>>>>> factors). >>>>>> >>>>>> It would probably be a bit faster if the cache had hits, but in >>>>>> general >>>>>> 14s to read a 100M rows is nearly a magnitude off where Hive 2.2.0 is. >>>>>> >>>>>> Cheers, >>>>>> Gopal >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >>> Want to work at Handy? Check out our culture deck and open roles >>> <http://www.handy.com/careers> >>> Latest news <http://www.handy.com/press> at Handy >>> Handy just raised $50m >>> <http://venturebeat.com/2015/11/02/on-demand-home-service-handy-raises-50m-in-round-led-by-fidelity/> >>> led >>> by Fidelity >>> >>> >> > > Want to work at Handy? Check out our culture deck and open roles > <http://www.handy.com/careers> > Latest news <http://www.handy.com/press> at Handy > Handy just raised $50m > <http://venturebeat.com/2015/11/02/on-demand-home-service-handy-raises-50m-in-round-led-by-fidelity/> > led > by Fidelity > >
