Simon created SPARK-33571:
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Summary: 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: Spark Core
Affects Versions: 3.0.1, 3.0.0
Reporter: Simon
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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