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https://issues.apache.org/jira/browse/SPARK-20427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17366173#comment-17366173
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Gejun Shen edited comment on SPARK-20427 at 6/20/21, 12:09 PM:
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When you use custom scheme, problem solved according to my test.
was (Author: sgejun):
When you use custom scheme, I believe precision would also lost.
For example,
123456789012345678901234567890123456789111, it will become something like
1.2531261878596697E40. Is this really what we want?
> Issue with Spark interpreting Oracle datatype NUMBER
> ----------------------------------------------------
>
> Key: SPARK-20427
> URL: https://issues.apache.org/jira/browse/SPARK-20427
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Alexander Andrushenko
> Assignee: Yuming Wang
> Priority: Major
> Fix For: 2.3.0
>
>
> In Oracle exists data type NUMBER. When defining a filed in a table of type
> NUMBER the field has two components, precision and scale.
> For example, NUMBER(p,s) has precision p and scale s.
> Precision can range from 1 to 38.
> Scale can range from -84 to 127.
> When reading such a filed Spark can create numbers with precision exceeding
> 38. In our case it has created fields with precision 44,
> calculated as sum of the precision (in our case 34 digits) and the scale (10):
> "...java.lang.IllegalArgumentException: requirement failed: Decimal precision
> 44 exceeds max precision 38...".
> The result was, that a data frame was read from a table on one schema but
> could not be inserted in the identical table on other schema.
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