+1

Dongjoon

On 2026/04/07 07:18:19 Kousuke Saruta wrote:
> +1
> 
> 
> 2026年4月7日(火) 16:17 Peter Toth <[email protected]>:
> 
> > +1 (non-binding)
> >
> > On Mon, Apr 6, 2026 at 10:13 PM Jungtaek Lim <[email protected]>
> > wrote:
> >
> >> +1 (non-binding)
> >>
> >> On Tue, Apr 7, 2026 at 3:11 AM Liu Cao <[email protected]> wrote:
> >>
> >>> +1 (non-binding)
> >>>
> >>> On Mon, Apr 6, 2026 at 11:10 AM John Zhuge <[email protected]> wrote:
> >>>
> >>>> +1 (non-binding)
> >>>>
> >>>> On Mon, Apr 6, 2026 at 11:07 AM Holden Karau <[email protected]>
> >>>> wrote:
> >>>>
> >>>>> +1 (binding)
> >>>>>
> >>>>> On Mon, Apr 6, 2026 at 10:58 AM Anish Shrigondekar via dev <
> >>>>> [email protected]> wrote:
> >>>>>
> >>>>>> +1
> >>>>>>
> >>>>>> Thanks,
> >>>>>> Anish
> >>>>>>
> >>>>>> On Mon, Apr 6, 2026 at 10:57 AM DB Tsai <[email protected]> wrote:
> >>>>>>
> >>>>>>> +1 (binding)
> >>>>>>>
> >>>>>>> DB Tsai  |  https://www.dbtsai.com/  |  PGP 42E5B25A8F7A82C1
> >>>>>>>
> >>>>>>> On Apr 3, 2026, at 2:59 PM, Andreas Neumann <[email protected]> wrote:
> >>>>>>>
> >>>>>>> Hi Spark devs,
> >>>>>>>
> >>>>>>> I'd like to call a vote on the SPIP*: Auto CDC Support for Apache
> >>>>>>> Spark*
> >>>>>>> Motivation
> >>>>>>>
> >>>>>>> With the upcoming introduction of standardized CDC support
> >>>>>>> <https://issues.apache.org/jira/browse/SPARK-55668>, Spark will
> >>>>>>> soon have a unified way to produce change data feeds. However,
> >>>>>>> consuming these feeds and applying them to a target table remains a
> >>>>>>> significant challenge.
> >>>>>>>
> >>>>>>> Common patterns like SCD Type 1 (maintaining a 1:1 replica) and SCD
> >>>>>>> Type 2 (tracking full change history) often require hand-crafted,
> >>>>>>> complex MERGE logic. In distributed systems, these implementations
> >>>>>>> are frequently error-prone when handling deletions or out-of-order 
> >>>>>>> data.
> >>>>>>> Proposal
> >>>>>>>
> >>>>>>> This SPIP proposes a new "Auto CDC" flow type for Spark. It
> >>>>>>> encapsulates the complex logic for SCD types and out-of-order data,
> >>>>>>> allowing data engineers to configure a declarative flow instead of 
> >>>>>>> writing
> >>>>>>> manual MERGE statements. This feature will be available in both Python
> >>>>>>> and SQL.
> >>>>>>> Example SQL:
> >>>>>>> -- Produce a change feed
> >>>>>>> CREATE STREAMING TABLE cdc.users AS
> >>>>>>> SELECT * FROM STREAM my_table CHANGES FROM VERSION 10;
> >>>>>>>
> >>>>>>> -- Consume the change feed
> >>>>>>> CREATE FLOW flow
> >>>>>>> AS AUTO CDC INTO
> >>>>>>>   target
> >>>>>>> FROM stream(cdc_data.users)
> >>>>>>>   KEYS (userId)
> >>>>>>>   APPLY AS DELETE WHEN operation = "DELETE"
> >>>>>>>   SEQUENCE BY sequenceNum
> >>>>>>>   COLUMNS * EXCEPT (operation, sequenceNum)
> >>>>>>>   STORED AS SCD TYPE 2
> >>>>>>>   TRACK HISTORY ON * EXCEPT (city);
> >>>>>>>
> >>>>>>> *Relevant Links:*
> >>>>>>>
> >>>>>>>    - SPIP Document:
> >>>>>>>    
> >>>>>>> https://docs.google.com/document/d/1Hp5BGEYJRHbk6J7XUph3bAPZKRQXKOuV1PEaqZMMRoQ/
> >>>>>>>    - *Discussion Thread: *
> >>>>>>>    https://lists.apache.org/thread/j6sj9wo9odgdpgzlxtvhoy7szs0jplf7
> >>>>>>>    -
> >>>>>>>
> >>>>>>>    JIRA: <https://issues.apache.org/jira/browse/SPARK-55668>
> >>>>>>>    https://issues.apache.org/jira/browse/SPARK-56249
> >>>>>>>
> >>>>>>> *The vote will be open for at least 72 hours. *Please vote:
> >>>>>>>
> >>>>>>> [ ] +1: Accept the proposal as an official SPIP
> >>>>>>> [ ] +0
> >>>>>>> [ ] -1: I don't think this is a good idea because ...
> >>>>>>> Cheers -Andreas
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>
> >>>>> --
> >>>>> Twitter: https://twitter.com/holdenkarau
> >>>>> Fight Health Insurance: https://www.fighthealthinsurance.com/
> >>>>> <https://www.fighthealthinsurance.com/?q=hk_email>
> >>>>> Books (Learning Spark, High Performance Spark, etc.):
> >>>>> https://amzn.to/2MaRAG9  <https://amzn.to/2MaRAG9>
> >>>>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
> >>>>> Pronouns: she/her
> >>>>>
> >>>>
> >>>>
> >>>> --
> >>>> John Zhuge
> >>>>
> >>>
> >>>
> >>> --
> >>>
> >>> Liu Cao
> >>>
> >>>
> >>>
> 

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