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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Using Spark on Hive with Hive also using Spark as its execution engine
Date Tue, 12 Jul 2016 13:39:34 GMT
thanks Marcin.

What Is your guesstimate on the order of "faster" please?

Cheers

Dr Mich Talebzadeh



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On 12 July 2016 at 14:35, Marcin Tustin <mtustin@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.talebzadeh@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 <jornfranke@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.talebzadeh@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
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>>> arise from relying on this email's technical content is explicitly
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>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>> On 12 July 2016 at 09:33, Markovitz, Dudu <dmarkovitz@paypal.com> wrote:
>>>
>>>> I don’t see how this explains the time differences.
>>>>
>>>>
>>>>
>>>> Dudu
>>>>
>>>>
>>>>
>>>> *From:* Mich Talebzadeh [mailto:mich.talebzadeh@gmail.com]
>>>> *Sent:* Tuesday, July 12, 2016 10:56 AM
>>>> *To:* user <user@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 <dmarkovitz@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.talebzadeh@gmail.com]
>>>> *Sent:* Monday, July 11, 2016 11:55 PM
>>>> *To:* user <user@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 <gopalv@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
>
>

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