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https://issues.apache.org/jira/browse/SPARK-25177?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Vinod KC updated SPARK-25177:
-----------------------------
    Summary: When dataframe decimal type column having scale higher than 6, 0 
values are shown in scientific notation  (was: When dataframe decimal type 
column having a scale higher than 6, 0 values are shown in scientific notation)

> When dataframe decimal type column having scale higher than 6, 0 values are 
> shown in scientific notation
> --------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-25177
>                 URL: https://issues.apache.org/jira/browse/SPARK-25177
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.4.0
>            Reporter: Vinod KC
>            Priority: Minor
>
> If scale of decimal type is > 6 , 0 value will be shown in scientific 
> notation and hence, when the dataframe output is saved to external database, 
> it fails due to scientific notation on "0" values.
> Eg: In Spark
>  --------------
> spark.sql("create table test (a decimal(10,7), b decimal(10,6), c 
> decimal(10,8))")
> spark.sql("insert into test values(0, 0,0)")
> spark.sql("insert into test values(1, 1, 1)")
> spark.table("test").show()
> +-------------+----------+----------------+
> |         a      |           b  |               c  |
> +-------------+-----------+---------------+
> |       0E-7  |0.000000 |         0E-8  | //If scale > 6, zero is displayed 
> in scientific notation
> |1.0000000|1.000000|1.00000000|
> +-------------+------------+--------------+
>  Eg: In Postgress
> --------------
> CREATE TABLE Testdec (a DECIMAL(10,7), b DECIMAL(10,6), c DECIMAL(10,8));
> INSERT INTO Testdec VALUES (0,0,0);
> INSERT INTO Testdec VALUES (1,1,1);
> select * from Testdec;
> Result:
>           a | .          b |        c
> -----------+-----------+----------
>  0.0000000 | 0.000000 | 0.00000000
>  1.0000000 | 1.000000 | 1.00000000
> We can make spark SQL result consistent with other Databases like Postgresql
>  



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