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
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On 12 July 2016 at 08:16, Markovitz, Dudu <[email protected]> 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:[email protected]]
> *Sent:* Monday, July 11, 2016 11:55 PM
> *To:* user <[email protected]>; user @spark <[email protected]>
> *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 <[email protected]> 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
>
>
>
>
>
>
>
>
>
>
>
>
