[ 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" spark.sql("SELECT CAST('2019-01-01 00:00:00' AS TIMESTAMP_NTZ) AS field").write.mode("overwrite").parquet(path) spark.read.schema("field array<timestamp_ntz>").parquet(path).collect()}} Depending on the memory mode, it will throw an NPE on OnHeap mode and SEGFAULT on OffHeap mode. was: 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 > 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 > Labels: pull-request-available > > Repro: > {{val path = "/tmp/someparquetfile" > spark.sql("SELECT CAST('2019-01-01 00:00:00' AS TIMESTAMP_NTZ) AS > field").write.mode("overwrite").parquet(path) > spark.read.schema("field array<timestamp_ntz>").parquet(path).collect()}} > Depending on the memory mode, it will throw an NPE on OnHeap mode and > SEGFAULT on OffHeap mode. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org