[ 
https://issues.apache.org/jira/browse/SPARK-46092?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Johan Lasperas updated SPARK-46092:
-----------------------------------
    Description: 
While the parquet readers don't support reading parquet values into larger 
Spark types, it's possible to trigger an overflow when creating a Parquet row 
group filter that will then incorrectly skip row groups and bypass the 
exception in the reader,

Repro:
{code:java}
Seq(0).toDF("a").write.parquet(path)
spark.read.schema("a LONG").parquet(path).where(s"a < 
${Long.MaxValue}").collect(){code}
This succeeds and returns no results. This should either fail if the Parquet 
reader doesn't support the upcast from int to long or produce result `[0]` if 
it does.

  was:
While the parquet readers don't support reading parquet values into larger 
Spark types, it's possible to trigger an overflow when creating a Parquet row 
group filter that will then incorrectly skip row groups and bypass the 
exception in the reader,

Repro:

```

Seq(0).toDF("a").write.parquet(path)
spark.read.schema("a LONG").parquet(path).where(s"a < 
${Long.MaxValue}").collect()

```

This succeeds and returns no results. This should either fail if the Parquet 
reader doesn't support the upcast from int to long or produce result `[0]` if 
it does.


> Overflow in Parquet row group filter creation causes incorrect results
> ----------------------------------------------------------------------
>
>                 Key: SPARK-46092
>                 URL: https://issues.apache.org/jira/browse/SPARK-46092
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.5.0
>            Reporter: Johan Lasperas
>            Priority: Major
>
> While the parquet readers don't support reading parquet values into larger 
> Spark types, it's possible to trigger an overflow when creating a Parquet row 
> group filter that will then incorrectly skip row groups and bypass the 
> exception in the reader,
> Repro:
> {code:java}
> Seq(0).toDF("a").write.parquet(path)
> spark.read.schema("a LONG").parquet(path).where(s"a < 
> ${Long.MaxValue}").collect(){code}
> This succeeds and returns no results. This should either fail if the Parquet 
> reader doesn't support the upcast from int to long or produce result `[0]` if 
> it does.



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