[ 
https://issues.apache.org/jira/browse/SPARK-45604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Zamil Majdy updated SPARK-45604:
--------------------------------
    Description: 
Repro:

```
val path = "/tmp/someparquetfile"
val df = sql("SELECT MAP('key', CAST('2019-01-01 00:00:00' AS TIMESTAMP_NTZ)) 
AS field")

df.write.mode("overwrite").parquet(path)
spark.read.schema("field map<string, 
array<timestamp_ntz>>").parquet(path).collect()
```

Depending on the memory mode is used, it will produced NPE on on-heap mode, and 
segfault on off-heap

  was:
Repro:

```
val path = "/tmp/zamil/timestamp"
val df = sql("SELECT MAP('key', CAST('2019-01-01 00:00:00' AS TIMESTAMP_NTZ)) 
AS field")

df.write.mode("overwrite").parquet(path)
spark.read.schema("field map<string, 
array<timestamp_ntz>>").parquet(path).collect()
```

Depending on the memory mode is used, it will produced NPE on on-heap mode, and 
segfault on off-heap


> Converting timestamp_ntz to array<timestamp_ntz> can cause NPE or SEGFAULT on 
> parquet vectorized reader
> -------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-45604
>                 URL: https://issues.apache.org/jira/browse/SPARK-45604
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 3.5.0
>            Reporter: Zamil Majdy
>            Priority: Major
>
> Repro:
> ```
> val path = "/tmp/someparquetfile"
> val df = sql("SELECT MAP('key', CAST('2019-01-01 00:00:00' AS TIMESTAMP_NTZ)) 
> AS field")
> df.write.mode("overwrite").parquet(path)
> spark.read.schema("field map<string, 
> array<timestamp_ntz>>").parquet(path).collect()
> ```
> Depending on the memory mode is used, it will produced NPE on on-heap mode, 
> and segfault on off-heap



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