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https://issues.apache.org/jira/browse/SPARK-30951?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-30951:
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Labels: (was: correctness)
> Potential data loss for legacy applications after switch to proleptic
> Gregorian calendar
> ----------------------------------------------------------------------------------------
>
> Key: SPARK-30951
> URL: https://issues.apache.org/jira/browse/SPARK-30951
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Bruce Robbins
> Priority: Blocker
>
> tl;dr: We recently discovered some Spark 2.x sites that have lots of data
> containing dates before October 15, 1582. This could be an issue when such
> sites try to upgrade to Spark 3.0.
> From SPARK-26651:
> {quote}"The changes might impact on the results for dates and timestamps
> before October 15, 1582 (Gregorian)
> {quote}
> We recently discovered that some large scale Spark 2.x applications rely on
> dates before October 15, 1582.
> Two cases came up recently:
> * An application that uses a commercial third-party library to encode
> sensitive dates. On insert, the library encodes the actual date as some other
> date. On select, the library decodes the date back to the original date. The
> encoded value could be any date, including one before October 15, 1582 (e.g.,
> "0602-04-04").
> * An application that uses a specific unlikely date (e.g., "1200-01-01") as
> a marker to indicate "unknown date" (in lieu of null)
> Both sites ran into problems after another component in their system was
> upgraded to use the proleptic Gregorian calendar. Spark applications that
> read files created by the upgraded component were interpreting encoded or
> marker dates incorrectly, and vice versa. Also, their data now had a mix of
> calendars (hybrid and proleptic Gregorian) with no metadata to indicate which
> file used which calendar.
> Both sites had enormous amounts of existing data, so re-encoding the dates
> using some other scheme was not a feasible solution.
> This is relevant to Spark 3:
> Any Spark 2 application that uses such date-encoding schemes may run into
> trouble when run on Spark 3. The application may not properly interpret the
> dates previously written by Spark 2. Also, once the Spark 3 version of the
> application writes data, the tables will have a mix of calendars (hybrid and
> proleptic gregorian) with no metadata to indicate which file uses which
> calendar.
> Similarly, sites might run with mixed Spark versions, resulting in data
> written by one version that cannot be interpreted by the other. And as above,
> the tables will now have a mix of calendars with no way to detect which file
> uses which calendar.
> As with the two real-life example cases, these applications may have enormous
> amounts of legacy data, so re-encoding the dates using some other scheme may
> not be feasible.
> We might want to consider a configuration setting to allow the user to
> specify the calendar for storing and retrieving date and timestamp values
> (not sure how such a flag would affect other date and timestamp-related
> functions). I realize the change is far bigger than just adding a
> configuration setting.
> Here's a quick example of where trouble may happen, using the real-life case
> of the marker date.
> In Spark 2.4:
> {noformat}
> scala> spark.read.orc(s"$home/data/datefile").filter("dt ==
> '1200-01-01'").count
> res0: Long = 1
> scala>
> {noformat}
> In Spark 3.0 (reading from the same legacy file):
> {noformat}
> scala> spark.read.orc(s"$home/data/datefile").filter("dt ==
> '1200-01-01'").count
> res0: Long = 0
> scala>
> {noformat}
> By the way, Hive had a similar problem. Hive switched from hybrid calendar to
> proleptic Gregorian calendar between 2.x and 3.x. After some upgrade
> headaches related to dates before 1582, the Hive community made the following
> changes:
> * When writing date or timestamp data to ORC, Parquet, and Avro files, Hive
> checks a configuration setting to determine which calendar to use.
> * When writing date or timestamp data to ORC, Parquet, and Avro files, Hive
> stores the calendar type in the metadata.
> * When reading date or timestamp data from ORC, Parquet, and Avro files,
> Hive checks the metadata for the calendar type.
> * When reading date or timestamp data from ORC, Parquet, and Avro files that
> lack calendar metadata, Hive's behavior is determined by a configuration
> setting. This allows Hive to read legacy data (note: if the data already
> consists of a mix of calendar types with no metadata, there is no good
> solution).
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