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https://issues.apache.org/jira/browse/SPARK-33571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17241339#comment-17241339
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Maxim Gekk commented on SPARK-33571:
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[~simonvanderveldt] Thank you for the detailed description and your
investigation. Let me clarify a few things:
> From our testing we're seeing several issues:
> Reading parquet data with Spark 3.0.1 that was written with Spark 2.4.5. that
> contains fields of type `TimestampType` which contain timestamps before the
> above mentioned moments in time without `datetimeRebaseModeInRead` set
> doesn't raise the `SparkUpgradeException`, it succeeds without any changes to
> the resulting dataframe compares to that dataframe in Spark 2.4.5
Spark 2.4.5 writes timestamps as parquet INT96 type. The SQL config
`datetimeRebaseModeInRead` does not influence on reading such types in Spark
3.0.1, so, Spark performs rebasing always (LEGACY mode). We recently added
separate configs for INT96:
* https://github.com/apache/spark/pull/30056
* https://github.com/apache/spark/pull/30121
The changes will be released with Spark 3.1.0.
> Reading parquet data with Spark 3.0.1 that was written with Spark 2.4.5. that
> contains fields of type `TimestampType` or `DateType` which contain dates or
> timestamps before the above mentioned moments in time with
> `datetimeRebaseModeInRead` set to `LEGACY` results in the same values in the
> dataframe as when using `CORRECTED`, so it seems like no rebasing is
> happening.
For INT96, it seems it is correct behavior. We should observe different results
for TIMESTAMP_MICROS and TIMESTAMP_MILLIS types, see the SQL config
spark.sql.parquet.outputTimestampType.
The DATE case is more interesting as we must see a difference in results for
ancient dates. I will investigate this case.
> Handling of hybrid to proleptic calendar when reading and writing Parquet
> data not working correctly
> ----------------------------------------------------------------------------------------------------
>
> Key: SPARK-33571
> URL: https://issues.apache.org/jira/browse/SPARK-33571
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core
> Affects Versions: 3.0.0, 3.0.1
> Reporter: Simon
> Priority: Major
>
> The handling of old dates written with older Spark versions (<2.4.6) using
> the hybrid calendar in Spark 3.0.0 and 3.0.1 seems to be broken/not working
> correctly.
> From what I understand it should work like this:
> * Only relevant for `DateType` before 1582-10-15 or `TimestampType` before
> 1900-01-01T00:00:00Z
> * Only applies when reading or writing parquet files
> * When reading parquet files written with Spark < 2.4.6 which contain dates
> or timestamps before the above mentioned moments in time a
> `SparkUpgradeException` should be raised informing the user to choose either
> `LEGACY` or `CORRECTED` for the `datetimeRebaseModeInRead`
> * When reading parquet files written with Spark < 2.4.6 which contain dates
> or timestamps before the above mentioned moments in time and
> `datetimeRebaseModeInRead` is set to `LEGACY` the dates and timestamps should
> show the same values in Spark 3.0.1. with for example `df.show()` as they did
> in Spark 2.4.5
> * When reading parquet files written with Spark < 2.4.6 which contain dates
> or timestamps before the above mentioned moments in time and
> `datetimeRebaseModeInRead` is set to `CORRECTED` the dates and timestamps
> should show different values in Spark 3.0.1. with for example `df.show()` as
> they did in Spark 2.4.5
> * When writing parqet files with Spark > 3.0.0 which contain dates or
> timestamps before the above mentioned moment in time a
> `SparkUpgradeException` should be raised informing the user to choose either
> `LEGACY` or `CORRECTED` for the `datetimeRebaseModeInWrite`
> First of all I'm not 100% sure all of this is correct. I've been unable to
> find any clear documentation on the expected behavior. The understanding I
> have was pieced together from the mailing list
> ([http://apache-spark-user-list.1001560.n3.nabble.com/Spark-3-0-1-new-Proleptic-Gregorian-calendar-td38914.html)]
> the blog post linked there and looking at the Spark code.
> From our testing we're seeing several issues:
> * Reading parquet data with Spark 3.0.1 that was written with Spark 2.4.5.
> that contains fields of type `TimestampType` which contain timestamps before
> the above mentioned moments in time without `datetimeRebaseModeInRead` set
> doesn't raise the `SparkUpgradeException`, it succeeds without any changes to
> the resulting dataframe compares to that dataframe in Spark 2.4.5
> * Reading parquet data with Spark 3.0.1 that was written with Spark 2.4.5.
> that contains fields of type `TimestampType` or `DateType` which contain
> dates or timestamps before the above mentioned moments in time with
> `datetimeRebaseModeInRead` set to `LEGACY` results in the same values in the
> dataframe as when using `CORRECTED`, so it seems like no rebasing is
> happening.
> I've made some scripts to help with testing/show the behavior, it uses
> pyspark 2.4.5, 2.4.6 and 3.0.1. You can find them here
> [https://github.com/simonvanderveldt/spark3-rebasemode-issue]. I'll post the
> outputs in a comment below as well.
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