OK thanks. These are my new ENV settings based upon the availability of
resources
export SPARK_EXECUTOR_CORES=12 ##, Number of cores for the workers (Default: 1).
export SPARK_EXECUTOR_MEMORY=5G ## , Memory per Worker (e.g. 1000M, 2G)
(Default: 1G)
export SPARK_DRIVER_MEMORY=2G ## , Memory for Master (e.g. 1000M, 2G) (Default:
512 Mb)
These are the new runs after these settings:
Spark on Hive (3 consecutive runs)
spark-sql> select * from dummy where id in (1, 5, 100000);
1 0 0 63
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi 1
xxxxxxxxxx
5 0 4 31
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA 5
xxxxxxxxxx
100000 99 999 188
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe 100000
xxxxxxxxxx
Time taken: 47.987 seconds, Fetched 3 row(s)
Around 48 seconds
Hive on Spark 1.3.1
0: jdbc:hive2://rhes564:10010/default> select * from dummy where id in (1, 5,
100000);
INFO :
Query Hive on Spark job[2] stages:
INFO : 2
INFO :
Status: Running (Hive on Spark job[2])
INFO : Job Progress Format
CurrentTime StageId_StageAttemptId:
SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount
[StageCost]
INFO : 2016-02-03 16:20:50,315 Stage-2_0: 0(+18)/18
INFO : 2016-02-03 16:20:53,369 Stage-2_0: 0(+18)/18
INFO : 2016-02-03 16:20:56,478 Stage-2_0: 0(+18)/18
INFO : 2016-02-03 16:20:58,530 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:01,570 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:04,680 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:07,767 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:10,877 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:13,941 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:17,019 Stage-2_0: 1(+17)/18
INFO : 2016-02-03 16:21:20,090 Stage-2_0: 3(+15)/18
INFO : 2016-02-03 16:21:21,138 Stage-2_0: 6(+12)/18
INFO : 2016-02-03 16:21:22,145 Stage-2_0: 10(+8)/18
INFO : 2016-02-03 16:21:23,150 Stage-2_0: 14(+4)/18
INFO : 2016-02-03 16:21:24,154 Stage-2_0: 17(+1)/18
INFO : 2016-02-03 16:21:26,161 Stage-2_0: 18/18 Finished
INFO : Status: Finished successfully in 36.88 seconds
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| dummy.id | dummy.clustered | dummy.scattered | dummy.randomised |
dummy.random_string | dummy.small_vc | dummy.padding |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| 1 | 0 | 0 | 63 |
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi | 1 |
xxxxxxxxxx |
| 5 | 0 | 4 | 31 |
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA | 5 |
xxxxxxxxxx |
| 100000 | 99 | 999 | 188 |
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe | 100000 |
xxxxxxxxxx |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
3 rows selected (37.161 seconds)
Around 37 seconds
Interesting results
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
Sybase ASE 15 Gold Medal Award 2008
A Winning Strategy: Running the most Critical Financial Data on ASE 15
http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15",
ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
http://talebzadehmich.wordpress.com <http://talebzadehmich.wordpress.com/>
NOTE: The information in this email is proprietary and confidential. This
message is for the designated recipient only, if you are not the intended
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From: Xuefu Zhang [mailto:xzh...@cloudera.com]
Sent: 03 February 2016 12:47
To: user@hive.apache.org
Subject: Re: Hive on Spark Engine versus Spark using Hive metastore
In YARN or standalone mode, you can set spark.executor.cores to utilize all
cores on the node. You can also set spark.executor.memory to allocate memory
for Spark to use. Once you do this, you may only have two executors to run your
map tasks, but each core in each executor can take up one task, increasing
parallelism. With this, the eventually limit may come down to the bandwidth of
your disks in the cluster.
Having said that, a two-node cluster isn't really big enough to do performance
benchmark. Nevertheless, you still need to configure properly to make full use
of the cluster.
--Xuefu
On Wed, Feb 3, 2016 at 1:25 AM, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Hi Jeff,
I only have a two node cluster. Is there anyway one can simulate additional
parallel runs in such an environment thus having more than two maps?
thanks
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
Sybase ASE 15 Gold Medal Award 2008
A Winning Strategy: Running the most Critical Financial Data on ASE 15
http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15",
ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
http://talebzadehmich.wordpress.com <http://talebzadehmich.wordpress.com/>
NOTE: The information in this email is proprietary and confidential. This
message is for the designated recipient only, if you are not the intended
recipient, you should destroy it immediately. Any information in this message
shall not be understood as given or endorsed by Peridale Technology Ltd, its
subsidiaries or their employees, unless expressly so stated. It is the
responsibility of the recipient to ensure that this email is virus free,
therefore neither Peridale Technology Ltd, its subsidiaries nor their employees
accept any responsibility.
From: Xuefu Zhang [mailto:xzh...@cloudera.com <mailto:xzh...@cloudera.com> ]
Sent: 03 February 2016 02:39
To: user@hive.apache.org <mailto:user@hive.apache.org>
Subject: Re: Hive on Spark Engine versus Spark using Hive metastore
Yes, regardless what spark mode you're running in, from Spark AM webui, you
should be able to see how many task are concurrently running. I'm a little
surprised to see that your Hive configuration only allows 2 map tasks to run in
parallel. If your cluster has the capacity, you should parallelize all the
tasks to achieve optimal performance. Since I don't know your Spark SQL
configuration, I cannot tell how much parallelism you have over there. Thus,
I'm not sure if your comparison is valid.
--Xuefu
On Tue, Feb 2, 2016 at 5:08 PM, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Hi Jeff,
In below
…. You should be able to see the resource usage in YARN resource manage URL.
Just to be clear we are talking about Port 8088/cluster?
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
Sybase ASE 15 Gold Medal Award 2008
A Winning Strategy: Running the most Critical Financial Data on ASE 15
http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15",
ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
http://talebzadehmich.wordpress.com <http://talebzadehmich.wordpress.com/>
NOTE: The information in this email is proprietary and confidential. This
message is for the designated recipient only, if you are not the intended
recipient, you should destroy it immediately. Any information in this message
shall not be understood as given or endorsed by Peridale Technology Ltd, its
subsidiaries or their employees, unless expressly so stated. It is the
responsibility of the recipient to ensure that this email is virus free,
therefore neither Peridale Technology Ltd, its subsidiaries nor their employees
accept any responsibility.
From: Koert Kuipers [mailto:ko...@tresata.com <mailto:ko...@tresata.com> ]
Sent: 03 February 2016 00:09
To: user@hive.apache.org <mailto:user@hive.apache.org>
Subject: Re: Hive on Spark Engine versus Spark using Hive metastore
uuuhm with spark using Hive metastore you actually have a real programming
environment and you can write real functions, versus just being boxed into some
version of sql and limited udfs?
On Tue, Feb 2, 2016 at 6:46 PM, Xuefu Zhang <xzh...@cloudera.com
<mailto:xzh...@cloudera.com> > wrote:
When comparing the performance, you need to do it apple vs apple. In another
thread, you mentioned that Hive on Spark is much slower than Spark SQL.
However, you configured Hive such that only two tasks can run in parallel.
However, you didn't provide information on how much Spark SQL is utilizing.
Thus, it's hard to tell whether it's just a configuration problem in your Hive
or Spark SQL is indeed faster. You should be able to see the resource usage in
YARN resource manage URL.
--Xuefu
On Tue, Feb 2, 2016 at 3:31 PM, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Thanks Jeff.
Obviously Hive is much more feature rich compared to Spark. Having said that in
certain areas for example where the SQL feature is available in Spark, Spark
seems to deliver faster.
This may be:
1. Spark does both the optimisation and execution seamlessly
2. Hive on Spark has to invoke YARN that adds another layer to the process
Now I did some simple tests on a 100Million rows ORC table available through
Hive to both.
Spark 1.5.2 on Hive 1.2.1 Metastore
spark-sql> select * from dummy where id in (1, 5, 100000);
1 0 0 63
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi 1
xxxxxxxxxx
5 0 4 31
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA 5
xxxxxxxxxx
100000 99 999 188
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe 100000
xxxxxxxxxx
Time taken: 50.805 seconds, Fetched 3 row(s)
spark-sql> select * from dummy where id in (1, 5, 100000);
1 0 0 63
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi 1
xxxxxxxxxx
5 0 4 31
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA 5
xxxxxxxxxx
100000 99 999 188
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe 100000
xxxxxxxxxx
Time taken: 50.358 seconds, Fetched 3 row(s)
spark-sql> select * from dummy where id in (1, 5, 100000);
1 0 0 63
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi 1
xxxxxxxxxx
5 0 4 31
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA 5
xxxxxxxxxx
100000 99 999 188
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe 100000
xxxxxxxxxx
Time taken: 50.563 seconds, Fetched 3 row(s)
So three runs returning three rows just over 50 seconds
Hive 1.2.1 on spark 1.3.1 execution engine
0: jdbc:hive2://rhes564:10010/default> select * from dummy where id in (1, 5,
100000);
INFO :
Query Hive on Spark job[4] stages:
INFO : 4
INFO :
Status: Running (Hive on Spark job[4])
INFO : Status: Finished successfully in 82.49 seconds
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| dummy.id <http://dummy.id> | dummy.clustered | dummy.scattered |
dummy.randomised | dummy.random_string |
dummy.small_vc | dummy.padding |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| 1 | 0 | 0 | 63 |
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi | 1 |
xxxxxxxxxx |
| 5 | 0 | 4 | 31 |
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA | 5 |
xxxxxxxxxx |
| 100000 | 99 | 999 | 188 |
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe | 100000 |
xxxxxxxxxx |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
3 rows selected (82.66 seconds)
0: jdbc:hive2://rhes564:10010/default> select * from dummy where id in (1, 5,
100000);
INFO : Status: Finished successfully in 76.67 seconds
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| dummy.id <http://dummy.id> | dummy.clustered | dummy.scattered |
dummy.randomised | dummy.random_string |
dummy.small_vc | dummy.padding |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| 1 | 0 | 0 | 63 |
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi | 1 |
xxxxxxxxxx |
| 5 | 0 | 4 | 31 |
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA | 5 |
xxxxxxxxxx |
| 100000 | 99 | 999 | 188 |
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe | 100000 |
xxxxxxxxxx |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
3 rows selected (76.835 seconds)
0: jdbc:hive2://rhes564:10010/default> select * from dummy where id in (1, 5,
100000);
INFO : Status: Finished successfully in 80.54 seconds
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| dummy.id <http://dummy.id> | dummy.clustered | dummy.scattered |
dummy.randomised | dummy.random_string |
dummy.small_vc | dummy.padding |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
| 1 | 0 | 0 | 63 |
rMLTDXxxqXOZnqYRJwInlGfGBTxNkAszBGEUGELqTSRnFjRGbi | 1 |
xxxxxxxxxx |
| 5 | 0 | 4 | 31 |
vDsFoYAOcitwrWNXCxPHzIIIxwKpTlrsVjFFKUDivytqJqOHGA | 5 |
xxxxxxxxxx |
| 100000 | 99 | 999 | 188 |
abQyrlxKzPTJliMqDpsfDTJUQzdNdfofUQhrKqXvRKwulZAoJe | 100000 |
xxxxxxxxxx |
+-----------+------------------+------------------+-------------------+-----------------------------------------------------+-----------------+----------------+--+
3 rows selected (80.718 seconds)
Three runs returning the same rows in 80 seconds.
It is possible that My Spark engine with Hive is 1.3.1 which is out of date and
that causes this lag.
There are certain queries that one cannot do with Spark. Besides it does not
recognize CHAR fields which is a pain.
spark-sql> CREATE TEMPORARY TABLE tmp AS
> SELECT t.calendar_month_desc, c.channel_desc, SUM(s.amount_sold) AS
TotalSales
> FROM sales s, times t, channels c
> WHERE s.time_id = t.time_id
> AND s.channel_id = c.channel_id
> GROUP BY t.calendar_month_desc, c.channel_desc
> ;
Error in query: Unhandled clauses: TEMPORARY 1, 2,2, 7
.
You are likely trying to use an unsupported Hive feature.";
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
Sybase ASE 15 Gold Medal Award 2008
A Winning Strategy: Running the most Critical Financial Data on ASE 15
http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15",
ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
http://talebzadehmich.wordpress.com <http://talebzadehmich.wordpress.com/>
NOTE: The information in this email is proprietary and confidential. This
message is for the designated recipient only, if you are not the intended
recipient, you should destroy it immediately. Any information in this message
shall not be understood as given or endorsed by Peridale Technology Ltd, its
subsidiaries or their employees, unless expressly so stated. It is the
responsibility of the recipient to ensure that this email is virus free,
therefore neither Peridale Technology Ltd, its subsidiaries nor their employees
accept any responsibility.
From: Xuefu Zhang [mailto:xzh...@cloudera.com <mailto:xzh...@cloudera.com> ]
Sent: 02 February 2016 23:12
To: user@hive.apache.org <mailto:user@hive.apache.org>
Subject: Re: Hive on Spark Engine versus Spark using Hive metastore
I think the diff is not only about which does optimization but more on feature
parity. Hive on Spark offers all functional features that Hive offers and these
features play out faster. However, Spark SQL is far from offering this parity
as far as I know.
On Tue, Feb 2, 2016 at 2:38 PM, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Hi,
My understanding is that with Hive on Spark engine, one gets the Hive optimizer
and Spark query engine
With spark using Hive metastore, Spark does both the optimization and query
engine. The only value add is that one can access the underlying Hive tables
from spark-sql etc
Is this assessment correct?
Thanks
Dr Mich Talebzadeh
LinkedIn
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
Sybase ASE 15 Gold Medal Award 2008
A Winning Strategy: Running the most Critical Financial Data on ASE 15
http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15",
ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
http://talebzadehmich.wordpress.com <http://talebzadehmich.wordpress.com/>
NOTE: The information in this email is proprietary and confidential. This
message is for the designated recipient only, if you are not the intended
recipient, you should destroy it immediately. Any information in this message
shall not be understood as given or endorsed by Peridale Technology Ltd, its
subsidiaries or their employees, unless expressly so stated. It is the
responsibility of the recipient to ensure that this email is virus free,
therefore neither Peridale Technology Ltd, its subsidiaries nor their employees
accept any responsibility.
