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https://issues.apache.org/jira/browse/SPARK-20427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17107376#comment-17107376
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Rafael edited comment on SPARK-20427 at 5/18/20, 7:27 AM:
----------------------------------------------------------
Hey guys,
I encountered an issue related to precision issues.
Now the code expects the Decimal type we need to have in JDBC metadata
precision and scale.
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L402-L414]
I found out that in the OracleDB it is valid to have Decimal without these
data. When I do a query read metadata for such column I'm getting
DATA_PRECISION = Null, and DATA_SCALE = Null.
Then when I run the `spark-sql` I'm getting such error:
{code:java}
java.lang.IllegalArgumentException: requirement failed: Decimal precision 45
exceeds max precision 38
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:114)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:465)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$3$$anonfun$12.apply(JdbcUtils.scala:407)
{code}
Do you have a work around how spark-sql can work with such cases?
UPDATE:
Solved with the custom scheme.
was (Author: kyrdan):
Hey guys,
I encountered an issue related to precision issues.
Now the code expects the Decimal type we need to have in JDBC metadata
precision and scale.
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L402-L414]
I found out that in the OracleDB it is valid to have Decimal without these
data. When I do a query read metadata for such column I'm getting
DATA_PRECISION = Null, and DATA_SCALE = Null.
Then when I run the `spark-sql` I'm getting such error:
{code:java}
java.lang.IllegalArgumentException: requirement failed: Decimal precision 45
exceeds max precision 38
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:114)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:465)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$3$$anonfun$12.apply(JdbcUtils.scala:407)
{code}
Do you have a work around how spark-sql can work with such cases?
> 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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