the query that you are using would have to be analyzed to know how much it 
could be optimized.
The small tables should be able to be handled with a map-join, depending on 
hive version, that may be happening automatically.
Hive will be doing the joins in stages.
You could manually implement the stages to assist in optimization and 
troubleshooting....once you know how long each join stage is taking, you can 
figure out where things are getting out of hand.
Depending on the data, you might be able to partition it or bucket it, to help 
with join optimization.
And ultimately, depending on the size and complexity of the query compared to 
the size/capacity of your cluster, it could take hours for the query to 
run....do the tasks finish?  If you have one or two long running tasks, while 
everything else has completed, but if the job is chugging through the stages 
and tasks aren't failing, you may just need more resources.


From: Sanka, Himabindu [mailto:[email protected]]
Sent: Wednesday, March 23, 2016 7:50 PM
To: [email protected]
Subject: Issue joining 21 HUGE Hive tables

Hi Team,

I need some inputs from you. I have a requirement for my project where I have 
to join 21 hive external tables.

Out of which 6 tables are HUGE  having 500 million records of data. Other 15 
tables are smaller ones around 100 to 1000 records each.

When I am doing inner joins/ left outer joins its taking hours to run the query.

Please let me know some optimization techniques or any other eco system 
components that performs better than HIVE.


Regards,
Hima



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