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Posted to issues@spark.apache.org by "Zamil Majdy (Jira)" <ji...@apache.org> on 2023/10/19 07:48:00 UTC

[jira] [Updated] (SPARK-45604) Converting timestamp_ntz to array can cause NPE or SEGFAULT on parquet vectorized reader

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

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

 

```
spark.conf.set("spark.databricks.photon.enabled", "false")

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

  was:
Repro:

 

{{```}}
spark.conf.set("spark.databricks.photon.enabled", "false")

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:
>  
> ```
> spark.conf.set("spark.databricks.photon.enabled", "false")
> 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



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