[
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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