[jira] [Updated] (SPARK-27790) Support ANSI SQL INTERVAL types

["Xiao Li (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-27790?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiao Li updated SPARK-27790:
----------------------------
    Labels: release-notes  (was: )

> Support ANSI SQL INTERVAL types
> -------------------------------
>
>                 Key: SPARK-27790
>                 URL: https://issues.apache.org/jira/browse/SPARK-27790
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.2.0
>            Reporter: Max Gekk
>            Assignee: Max Gekk
>            Priority: Major
>              Labels: release-notes
>             Fix For: 3.3.0
>
>
> Spark has an INTERVAL data type, but it is “broken”:
> # It cannot be persisted
> # It is not comparable because it crosses the month day line. That is there is no telling whether “1 Month 1 Day” is equal to “1 Month 1 Day” since not all months have the same number of days.
> I propose here to introduce the two flavours of INTERVAL as described in the ANSI SQL Standard and deprecate the Sparks interval type.
> * ANSI describes two non overlapping “classes”: 
> ** YEAR-MONTH, 
> ** DAY-SECOND ranges
> * Members within each class can be compared and sorted.
> * Supports datetime arithmetic
> * Can be persisted.
> The old and new flavors of INTERVAL can coexist until Spark INTERVAL is eventually retired. Also any semantic “breakage” can be controlled via legacy config settings. 
> *Milestone 1* --  Spark Interval equivalency (   The new interval types meet or exceed all function of the existing SQL Interval):
> * Add two new DataType implementations for interval year-month and day-second. Includes the JSON format and DLL string.
> * Infra support: check the caller sides of DateType/TimestampType
> * Support the two new interval types in Dataset/UDF.
> * Interval literals (with a legacy config to still allow mixed year-month day-seconds fields and return legacy interval values)
> * Interval arithmetic(interval * num, interval / num, interval +/- interval)
> * Datetime functions/operators: Datetime - Datetime (to days or day second), Datetime +/- interval
> * Cast to and from the new two interval types, cast string to interval, cast interval to string (pretty printing), with the SQL syntax to specify the types
> * Support sorting intervals.
> *Milestone 2* -- Persistence:
> * Ability to create tables of type interval
> * Ability to write to common file formats such as Parquet and JSON.
> * INSERT, SELECT, UPDATE, MERGE
> * Discovery
> *Milestone 3* --  Client support
> * JDBC support
> * Hive Thrift server
> *Milestone 4* -- PySpark and Spark R integration
> * Python UDF can take and return intervals
> * DataFrame support



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