Hive sort operation on high volume skewed dataset - hadoop

Question

I am working on a big dataset of size around 3 TB on Hortonworks 2.6.5, the layout of the dataset is pretty straight forward.
The heirarchy of data is as follows -
-Country
-Warehouse
-Product
-Product Type
-Product Serial Id
We have transaction data in the above hierarchy for 30 countries each country have more than 200 warehouse, single country USA contributes around 75% of the entire data set.
Problem:
1) We have transaction data with transaction date column (trans_dt) for the above data set for each warehouse, I need to sort trans_dt in ascending order within each warehouse using Hive (1.1.2 version) MapReduce. I have created a partition at Country level and then applied DISTRIBUTE BY Warehouse SORT BY trans_dt ASC; Sorting takes around 8 hours to finish and last 6 hrs is being used at Reducer at 99% stage. I see a lot of shuffles at this stage.
2) We do lot of group by on this combination - Country,Warehouse,Product,Product Type,Product Serial Id any suggestion to optimize this operation will be very helpful.
3) How to handle Skewed dataset for USA country ?
We are using below hive properties.
SET hive.exec.compress.intermediate=true;
SET hive.intermediate.compression.codec=org.apache.hadoop.io.compress.SnappyCodec;
SET hive.intermediate.compression.type=BLOCK;
SET hive.exec.compress.output=true;
SET mapreduce.output.fileoutputformat.compress=true;
SET mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.SnappyCodec;
SET mapreduce.output.fileoutputformat.compress.type=BLOCK;
SET hive.auto.convert.join=true;
SET hive.auto.convert.join.noconditionaltask=true;
SET hive.auto.convert.join.noconditionaltask.size=10000000;
SET hive.groupby.skewindata=true;
SET hive.optimize.skewjoin.compiletime=true;
SET hive.optimize.skewjoin=true;
SET hive.optimize.bucketmapjoin=true;
SET hive.exec.parallel=true;
SET hive.cbo.enable=true;
SET hive.stats.autogather=true;
SET hive.compute.query.using.stats=true;
SET hive.stats.fetch.column.stats=true;
SET hive.stats.fetch.partition.stats=true;
SET hive.vectorized.execution.enabled=true;
SET hive.vectorized.execution.reduce.enabled=true;
SET hive.optimize.index.filter=true;
SET hive.optimize.ppd=true;
SET hive.mapjoin.smalltable.filesize=25000000;
SET hive.exec.dynamic.partition=true;
SET hive.exec.dynamic.partition.mode=nonstrict;
SET hive.exec.max.dynamic.partitions.pernode=1000;
SET mapreduce.reduce.memory.mb=10240;
SET mapreduce.reduce.java.opts=-Xmx9216m;
SET mapreduce.map.memory.mb=10240;
SET mapreduce.map.java.opts=-Xmx9216m;
SET mapreduce.task.io.sort.mb=1536;
SET hive.optimize.groupby=true;
SET hive.groupby.orderby.position.alias=true;
SET hive.multigroupby.singlereducer=true;
SET hive.merge.mapfiles=true;
SET hive.merge.smallfiles.avgsize=128000000;
SET hive.merge.size.per.task=268435456;
SET hive.map.aggr=true;
SET hive.optimize.distinct.rewrite=true;
SET mapreduce.map.speculative=false;
set hive.fetch.task.conversion = more;
set hive.fetch.task.aggr=true;
set hive.fetch.task.conversion.threshold=1024000000;

Answer 1

For US and Non US use the same query but process them independently.
Select * from Table where Country = 'US'
UNION
Select * from Table where Country <> 'US'
OR
You can process them using a script where you fire one country at the query at a time, reducing the volume of data that needs to be processed at one instance.
INSERT INTO TABLE <AggregateTable>
SELECT * FROM <SourceTable>
WHERE Country in ('${hiveconf:ProcessCountry}')