[jira] [Updated] (HUDI-9672) Disable skipping clustering for spark incremental query to solve data duplication

["Shuo Cheng (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HUDI-9672?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Shuo Cheng updated HUDI-9672:
-----------------------------
    Summary: Disable skipping clustering for spark incremental query to solve data duplication   (was: Fix data loss for spark incremental query with skip clustering enabled)

> Disable skipping clustering for spark incremental query to solve data duplication 
> ----------------------------------------------------------------------------------
>
>                 Key: HUDI-9672
>                 URL: https://issues.apache.org/jira/browse/HUDI-9672
>             Project: Apache Hudi
>          Issue Type: Bug
>          Components: spark-sql
>            Reporter: Shuo Cheng
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 1.1.0
>
>
> Considering the following ingestion case for spark:
>  * MOR table + upsert/insert
> 1st insert: ("1", "a1", "10", "000"). -> fg1-001.parquet, contains 1 rows
> 2st insert: ("2", "a1", "11", "001"). -> fg1-002.parquet, contains 2 rows
> 3st insert: ("3", "a1", "12", "002"). -> fg1-003.parquet, contains 3 rows
> 4st insert: ("4", "a1", "13", "003"). -> fg1-004.parquet, contains 4 rows
> clustering -> fg2-005.parquet, contains 4 rows
> 5st insert: ("5", "a1", "14", "004"). -> fg2-006.parquet, contains 5 rows
> During upsert/insert operation, we opportunistically expand existing small files on storage, instead of writing new files to keep number of files to an optimum. So each file generated for current commit will contains all datas for previous commits.
> If we fire an incremental query with: START_OFFSET = 002 and skipping cluster enabled.
> There will two file spilts to read:
>  * fg1 latest file slice: fg1-004.parquet
>  * fg2 latest file slice: fg2-006.parquet
> The final read results will have duplicates for rows with key: "2", "3", "4".
> expected        actual
> [2,a1,11,001]   [2,a1,11,001]
> [3,a1,12,002]  [2,a1,11,001]
> [4,a1,13,003]  [3,a1,12,002]
> [5,a1,14,004]  [3,a1,12,002]
>                         [4,a1,13,003]
>                         [4,a1,13,003]
>                         [5,a1,14,004] 
>  
> We currently *disable skipping clustering* for spark incremental query before proper solution is proposed.



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