[
https://issues.apache.org/jira/browse/SPARK-28724?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ShuMing Li updated SPARK-28724:
-------------------------------
Description:
Maybe this is a bug in `Scala` when convert `BigDecimal` to `Long`, however
Spark should keep the result correct when query the sqls below:
{code:java}
spark-sql> select cast(20190801002382000052000000017638 as int);
-1493203738
spark-sql> select cast(20190801002382000052000000017638 as bigint);
4671677505944388838
{code}
After this patch, the result will throw AnalysisException :
{code:java}
spark-sql> select cast(20190801002382000052000000017638 as bigint);
Error in query: Decimal 20190801002382000052000000017638 does not fit in range
[-9223372036854775808, 9223372036854775807] for type Long;{code}
For `toFloat/toDouble`, the result is reasonable:
{code:java}
spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
double);
Error in query: DecimalType can only support precision up to 38 == SQL ==
select cast(201908010023820000520000000176380000000000000000.0 as double)
spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
float); Error in query: DecimalType can only support precision up to 38 == SQL
== select cast(201908010023820000520000000176380000000000000000.0 as float)
{code}
was:
Maybe this is a bug in `Scala` when convert `BigDecimal` to `Long`, however
Spark should keep the result correct when query the sqls below:
{code:java}
spark-sql> select cast(20190801002382000052000000017638 as int);
-1493203738
spark-sql> select cast(20190801002382000052000000017638 as bigint);
4671677505944388838
{code}
After this patch, the result is below:
{code:java}
spark-sql> select cast(20190801002382000052000000017638 as bigint);
java.lang.ArithmeticException: Decimal 20190801002382000052000000017638 does
not fit in range [-9223372036854775808, 9223372036854775807] for type Long
at org.apache.spark.sql.types.Decimal.toLong(Decimal.scala:219){code}
For `toFloat/toDouble`, the result is reasonable:
{code:java}
spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
double);
Error in query: DecimalType can only support precision up to 38 == SQL ==
select cast(201908010023820000520000000176380000000000000000.0 as double)
spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
float); Error in query: DecimalType can only support precision up to 38 == SQL
== select cast(201908010023820000520000000176380000000000000000.0 as float)
{code}
> Throw error message when cast out range decimal to long
> -------------------------------------------------------
>
> Key: SPARK-28724
> URL: https://issues.apache.org/jira/browse/SPARK-28724
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: ShuMing Li
> Priority: Minor
>
> Maybe this is a bug in `Scala` when convert `BigDecimal` to `Long`, however
> Spark should keep the result correct when query the sqls below:
> {code:java}
> spark-sql> select cast(20190801002382000052000000017638 as int);
> -1493203738
> spark-sql> select cast(20190801002382000052000000017638 as bigint);
> 4671677505944388838
> {code}
> After this patch, the result will throw AnalysisException :
> {code:java}
> spark-sql> select cast(20190801002382000052000000017638 as bigint);
> Error in query: Decimal 20190801002382000052000000017638 does not fit in
> range [-9223372036854775808, 9223372036854775807] for type Long;{code}
>
> For `toFloat/toDouble`, the result is reasonable:
> {code:java}
> spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
> double);
> Error in query: DecimalType can only support precision up to 38 == SQL ==
> select cast(201908010023820000520000000176380000000000000000.0 as double)
> spark-sql> select cast(201908010023820000520000000176380000000000000000.0 as
> float); Error in query: DecimalType can only support precision up to 38 ==
> SQL == select cast(201908010023820000520000000176380000000000000000.0 as
> float)
> {code}
>
>
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