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https://issues.apache.org/jira/browse/SPARK-33571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17241379#comment-17241379
]
Maxim Gekk commented on SPARK-33571:
------------------------------------
I have tried to reproduce the issue on the master branch by reading the file
saved by Spark 2.4.5
(https://github.com/apache/spark/tree/master/sql/core/src/test/resources/test-data):
{code:scala}
test("SPARK-33571: read ancient dates saved by Spark 2.4.5") {
withSQLConf(SQLConf.LEGACY_PARQUET_REBASE_MODE_IN_READ.key ->
LEGACY.toString) {
val path =
getResourceParquetFilePath("test-data/before_1582_date_v2_4_5.snappy.parquet")
val df = spark.read.parquet(path)
df.show(false)
}
withSQLConf(SQLConf.LEGACY_PARQUET_REBASE_MODE_IN_READ.key ->
CORRECTED.toString) {
val path =
getResourceParquetFilePath("test-data/before_1582_date_v2_4_5.snappy.parquet")
val df = spark.read.parquet(path)
df.show(false)
}
}
{code}
The results are different in LEGACY and in CORRECTED modes:
{code}
+----------+----------+
|dict |plain |
+----------+----------+
|1001-01-01|1001-01-01|
|1001-01-01|1001-01-02|
|1001-01-01|1001-01-03|
|1001-01-01|1001-01-04|
|1001-01-01|1001-01-05|
|1001-01-01|1001-01-06|
|1001-01-01|1001-01-07|
|1001-01-01|1001-01-08|
+----------+----------+
+----------+----------+
|dict |plain |
+----------+----------+
|1001-01-07|1001-01-07|
|1001-01-07|1001-01-08|
|1001-01-07|1001-01-09|
|1001-01-07|1001-01-10|
|1001-01-07|1001-01-11|
|1001-01-07|1001-01-12|
|1001-01-07|1001-01-13|
|1001-01-07|1001-01-14|
+----------+----------+
{code}
> 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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