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From Elliot West <tea...@gmail.com>
Subject Re: Hive on Spark Engine versus Spark using Hive metastore
Date Thu, 04 Feb 2016 18:13:39 GMT
Related to this and for the benefit of anyone who is using Hive: The issues
around testing and some possible approaches are summarised here:

https://cwiki.apache.org/confluence/display/Hive/Unit+testing+HQL


Ultimately there are no elegant solutions to the limitations correctly
described by Koert. However if you do choose to use Hive please be aware
that there are some good options out there for providing reasonable test
coverage of your production code. They aren't perfect by any means and are
certainly not at the level we've come to expect in other development
domains, but they are usable and therefore there is no excuse for not
writing tests! :-)

Elliot.


On 3 February 2016 at 04:49, Koert Kuipers <koert@tresata.com> wrote:

> yeah but have you ever seen somewhat write a real analytical program in
> hive? how? where are the basic abstractions to wrap up a large amount of
> operations (joins, groupby's) into a single function call? where are the
> tools to write nice unit test for that?
>
> for example in spark i can write a DataFrame => DataFrame that internally
> does many joins, groupBys and complex operations. all unit tested and
> perfectly re-usable. and in hive? copy paste round sql queries? thats just
> dangerous.
>
> On Tue, Feb 2, 2016 at 8:09 PM, Edward Capriolo <edlinuxguru@gmail.com>
> wrote:
>
>> Hive has numerous extension points, you are not boxed in by a long shot.
>>
>>
>> On Tuesday, February 2, 2016, Koert Kuipers <koert@tresata.com> wrote:
>>
>>> 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 <xzhang@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 <mich@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  | 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  | 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  | 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
>>>>> <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
>>>>>
>>>>>
>>>>>
>>>>> 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:xzhang@cloudera.com]
>>>>> *Sent:* 02 February 2016 23:12
>>>>> *To:* 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 <mich@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
>>>>> <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
>>>>>
>>>>>
>>>>>
>>>>> 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.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>> --
>> Sorry this was sent from mobile. Will do less grammar and spell check
>> than usual.
>>
>
>

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