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From "Mich Talebzadeh" <m...@peridale.co.uk>
Subject Using spark in tandem with Hive
Date Tue, 01 Dec 2015 16:40:38 GMT
What if we decide to use spark with Hive. I look to hear similar views

 

My test bed comprised

 

1.    Spark version 1.5.2

2.    Hive version 1.2.1

3.    Hadoop version 2.6

 

 

I made Spark to use Hive metastore. So using spark-sql I can pretty do
whatever one can do with HiveQL 

 

I created and populated an ORC table in spark-sql.. It took 90 seconds to
create and populate the table with 1.7 million rows

 

spark-sql> select count(1) from tt;

1767886

Time taken: 5.169 seconds, Fetched 1 row(s)

 

Now let me try to do the said operation on the same table with HCL and MR

 

hive> use asehadoop;

OK

Time taken: 0.639 seconds

hive> select count(1) from tt;

Query ID = hduser_20151201162717_e3102633-f501-413b-b9cb-384ac50880ac

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_1448969636093_0001, Tracking URL =
http://rhes564:8088/proxy/application_1448969636093_0001/

Kill Command = /home/hduser/hadoop-2.6.0/bin/hadoop job  -kill
job_1448969636093_0001

Hadoop job information for Stage-1: number of mappers: 1; number of
reducers: 1

2015-12-01 16:27:27,154 Stage-1 map = 0%,  reduce = 0%

2015-12-01 16:27:35,427 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU
2.65 sec

2015-12-01 16:27:41,611 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU
3.71 sec

MapReduce Total cumulative CPU time: 3 seconds 710 msec

Ended Job = job_1448969636093_0001

MapReduce Jobs Launched:

Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 3.71 sec   HDFS Read:
520151 HDFS Write: 8 SUCCESS

Total MapReduce CPU Time Spent: 3 seconds 710 msec

OK

1767886

Time taken: 25.635 seconds, Fetched: 1 row(s)

 

So 5 seconds in Spark versus 25 seconds in Hive

 

On a point query Hive does not seem to return the correct timing?

 

hive> select * from tt where data_object_id = 10;

Time taken: 0.063 seconds, Fetched: 72 row(s)

 

Whereas in Spark I get

 

spark-sql>  select * from tt where data_object_id = 10;

Time taken: 9.002 seconds, Fetched 72 row(s)

 

9 seconds looks far more plausible to me than 0.063 seonds. Or in an
unlikely event Spark returns elapsed time, whereas Hive returns execution
time?

 

Thanks

 

Mich Talebzadeh

 

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>
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

 

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