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 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 Ltd, its subsidiaries nor their employees accept any responsibility.
