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https://issues.apache.org/jira/browse/SPARK-30875?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wenchen Fan updated SPARK-30875:
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Description:
In Spark 3.0, we write out timestamp values as parquet TIMESTAMP_MICROS by
default, instead of INT96. This is good in general as Spark can read all kinds
of parquet timestamps, but works better with TIMESTAMP_MICROS.
However, this brings some troubles with hive compatibility. Spark can use
native parquet writer to write hive parquet tables, which may break hive
compatibility if Spark writes TIMESTAMP_MICROS.
We can switch back to INT96 by default, or fix it:
1. when using native parquet writer to write hive parquet tables, write
timestamp as INT96.
2. when creating tables in `HiveExternalCatalog.createTable`, don't claim the
parquet table is hive compatible if it has timestamp columns.
was:In Spark 3.0, we write out timestamp values as parquet
> Revisit the decision of writing parquet TIMESTAMP_MICROS by default
> -------------------------------------------------------------------
>
> Key: SPARK-30875
> URL: https://issues.apache.org/jira/browse/SPARK-30875
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Wenchen Fan
> Priority: Major
>
> In Spark 3.0, we write out timestamp values as parquet TIMESTAMP_MICROS by
> default, instead of INT96. This is good in general as Spark can read all
> kinds of parquet timestamps, but works better with TIMESTAMP_MICROS.
> However, this brings some troubles with hive compatibility. Spark can use
> native parquet writer to write hive parquet tables, which may break hive
> compatibility if Spark writes TIMESTAMP_MICROS.
> We can switch back to INT96 by default, or fix it:
> 1. when using native parquet writer to write hive parquet tables, write
> timestamp as INT96.
> 2. when creating tables in `HiveExternalCatalog.createTable`, don't claim the
> parquet table is hive compatible if it has timestamp columns.
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