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Posted to issues@spark.apache.org by "Vinod KC (JIRA)" <ji...@apache.org> on 2018/08/21 14:59:00 UTC
[jira] [Updated] (SPARK-25177) When dataframe decimal type column
having scale higher than 6, 0 values are shown in scientific notation
[ https://issues.apache.org/jira/browse/SPARK-25177?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
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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