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https://issues.apache.org/jira/browse/SPARK-31030?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yuanjian Li updated SPARK-31030:
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Attachment: image-2020-03-04-10-54-05-208.png
> Backward Compatibility for Parsing Datetime
> -------------------------------------------
>
> Key: SPARK-31030
> URL: https://issues.apache.org/jira/browse/SPARK-31030
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Yuanjian Li
> Priority: Major
> Attachments: image-2020-03-04-10-52-03-503.png,
> image-2020-03-04-10-52-11-790.png, image-2020-03-04-10-54-05-208.png,
> image-2020-03-04-10-54-13-238.png
>
>
> *Background*
> In Spark version 2.4 and earlier, datetime parsing, formatting and conversion
> are performed by using the hybrid calendar ([Julian +
> Gregorian|https://docs.oracle.com/javase/7/docs/api/java/util/GregorianCalendar.html]).
>
> Since the Proleptic Gregorian calendar is de-facto calendar worldwide, as
> well as the chosen one in ANSI SQL standard, Spark 3.0 switches to it by
> using Java 8 API classes [the java.time packages that are based on[ ISO
> chronology|https://docs.oracle.com/javase/8/docs/api/java/time/chrono/IsoChronology.html]].
> The switching job is completed in SPARK-26651.
>
> *Problem*
> Switching to Java 8 datetime API breaks the backward compatibility of Spark
> 2.4 and earlier when parsing datetime. Moreover, for the build-in SQL
> expressions like to_date, to_timestamp and etc, in the existing
> implementation of Spark 3.0 will catch all the exceptions and return `null`
> when hitting the parsing errors. This will cause the silent result changes,
> which are hard to debug for end-users when the data volume is huge and
> business logics are complex.
>
> *Solution*
> To avoid unexpected result changes after the underlying datetime API switch,
> we propose the following solution.
> * Introduce the fallback mechanism: when the Java 8-based parser fails, we
> need to detect these behavior differences by falling back to the legacy
> parser, and fail with a user-friendly error message to tell users what gets
> changed and how to fix the pattern.
> * Document the Spark’s datetime patterns: The date-time formatter of Spark
> is decoupled with the Java patterns. The Spark’s patterns are mainly based on
> the [Java 7’s
> pattern|https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html]
> [for better backward compatibility] with the customized logic [caused by the
> breaking changes between[ Java
> 7|https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html]
> and[ Java
> 8|https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html]
> pattern string]. Below are the customized rules:
>
>
> ||Pattern||Java 7||Java 8|| Example||Rule||
> |u|Day number of week (1 = Monday, ..., 7 = Sunday)|Year (Different with y, u
> accept a negative value to represent BC, while y should be used together with
> G to do the same thing.)|
> |SQL|2.4|3.0|
> |to_timestamp('7', 'u')|1970-01-04 00:00:00|0007-01-01 00:00:00|
> |to_timestamp('7', 'uu')|1970-01-04 00:00:00|null|
> |to_timestamp('700', 'uuu')|null|0700-01-01 00:00:00|
> |to_timestamp('2000', 'uuuu')|null|2000-01-01 00:00:00|
> |Substitute ‘u’ to ‘e’ and use Java 8 parser to parse the string. If
> parsable, return the result; otherwise, fall back to ‘u’, and then use the
> legacy Java 7 parser to parse. When it is successfully parsed, throw an
> exception and ask users to change the pattern strings or turn on the legacy
> mode; otherwise, return NULL as what Spark 2.4 does.|
> | z| General time zone which also accepts
> RFC 822 time zones|Only accept time-zone name, e.g. Pacific Standard Time;
> PST|
> |SQL|2.4|3.0|
> |to_timestamp('2020 +0800', 'yyyy z'|2019-12-31 16:00:00|null|
> |The semantics of ‘z’ are different between Java 7 and Java 8. Here, Spark
> 3.0 follows the semantics of Java 8.
> Use Java 8 to parse the string. If parsable, return the result; otherwise,
> use the legacy Java 7 parser to parse. When it is successfully parsed, throw
> an exception and ask users to change the pattern strings or turn on the
> legacy mode; otherwise, return NULL as what Spark 2.4 does.|
>
>
>
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