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https://issues.apache.org/jira/browse/SPARK-20555?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiao Li reassigned SPARK-20555:
-------------------------------
Assignee: Gabor Feher
> Incorrect handling of Oracle's decimal types via JDBC
> -----------------------------------------------------
>
> Key: SPARK-20555
> URL: https://issues.apache.org/jira/browse/SPARK-20555
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Gabor Feher
> Assignee: Gabor Feher
> Fix For: 2.1.2, 2.2.0
>
>
> When querying an Oracle database, Spark maps some Oracle numeric data types
> to incorrect Catalyst data types:
> 1. DECIMAL(1) becomes BooleanType
> In Orcale, a DECIMAL(1) can have values from -9 to 9.
> In Spark now, values larger than 1 become the boolean value true.
> 2. DECIMAL(3,2) becomes IntegerType
> In Oracle, a DECIMAL(2) can have values like 1.23
> In Spark now, digits after the decimal point are dropped.
> 3. DECIMAL(10) becomes IntegerType
> In Oracle, a DECIMAL(10) can have the value 9999999999 (ten nines), which is
> more than 2^31
> Spark throws an exception: "java.sql.SQLException: Numeric Overflow"
> I think the best solution is to always keep Oracle's decimal types. (In
> theory we could introduce a FloatType in some case of #2, and fix #3 by only
> introducing IntegerType for DECIMAL(9). But in my opinion, that would end up
> complicated and error-prone.)
> Note: I think the above problems were introduced as part of
> https://github.com/apache/spark/pull/14377
> The main purpose of that PR seems to be converting Spark types to correct
> Oracle types, and that part seems good to me. But it also adds the inverse
> conversions. As it turns out in the above examples, that is not possible.
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