Hi Team,
Actually I figured out something ..
While Hive Java UDF executed on hive it is giving output with 10 decimal
precision but in spark same udf is giving results rounded off to 6 decimal
precision... How do I stop that? Its the same java udf jar files used in
both hive and spark..
[image: Inline image 1]
On Thu, Feb 2, 2017 at 3:33 PM, Alex <siri8...@gmail.com> wrote:
> Hi As shown below same query when ran back to back showing inconsistent
> results..
>
> testtable1 is Avro Serde table...
>
> [image: Inline image 1]
>
>
>
> hc.sql("select * from testtable1 order by col1 limit 1").collect;
> res14: Array[org.apache.spark.sql.Row] = Array([1570,3364,201607,Y,APJ,
> PHILIPPINES,8518944,null,null,null,null,-15.992583,0.0,-15.
> 992583,null,null,MONTH_ITEM_GROUP])
>
> scala> hc.sql("select * from testtable1 order by col1 limit 1").collect;
> res15: Array[org.apache.spark.sql.Row] = Array([1570,485888,20163,N,
> AMERICAS,BRAZIL,null,null,null,null,null,6019.299999999999,17198.0,6019.
> 299999999999,null,null,QUARTER_GROUP])
>
> scala> hc.sql("select * from testtable1 order by col1 limit 1").collect;
> res16: Array[org.apache.spark.sql.Row] = Array([1570,3930,201607,Y,APJ,INDIA
> SUB-CONTINENT,8741220,null,null,null,null,-208.485216,0.
> 0,-208.485216,null,null,MONTH_ITEM_GROUP])
>
>
