You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2020/12/04 07:29:00 UTC
[jira] [Resolved] (SPARK-33571) Handling of hybrid to proleptic
calendar when reading and writing Parquet data not working correctly
[ https://issues.apache.org/jira/browse/SPARK-33571?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-33571.
----------------------------------
Fix Version/s: 3.1.0
Resolution: Fixed
Fixed in https://github.com/apache/spark/pull/30596
> 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
> Fix For: 3.1.0
>
>
> 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.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org