[jira] [Assigned] (HUDI-480) Support a querying delete data methond in incremental view

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

     [ https://issues.apache.org/jira/browse/HUDI-480?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

cdmikechen reassigned HUDI-480:
-------------------------------

    Assignee: cdmikechen

> Support a querying delete data methond in incremental view
> ----------------------------------------------------------
>
>                 Key: HUDI-480
>                 URL: https://issues.apache.org/jira/browse/HUDI-480
>             Project: Apache Hudi
>          Issue Type: Improvement
>          Components: Incremental Pull
>            Reporter: cdmikechen
>            Assignee: cdmikechen
>            Priority: Minor
>
> As we known, hudi have supported many method to query data in Spark and Hive and Presto. And it also provides a very good timeline idea to trace changes in data, and it can be used to query incremental data in incremental view.
> In old time, we just have insert and update funciton to upsert data, and now we have added new functions to delete some existing data.
> *[HUDI-328] Adding delete api to HoodieWriteClient* https://github.com/apache/incubator-hudi/pull/1004
> *[HUDI-377] Adding Delete() support to DeltaStreamer**https://github.com/apache/incubator-hudi/pull/1073
> So I think if we have delete api, should we add another method to get deleted data in incremental view?
> I've looked at the methods for generating new parquet files. I think the main idea is to combine old and new data, and then filter the data which need to be deleted, so that the deleted data does not exist in the new dataset. However, in this way, the data to be deleted will not be retained in new dataset, so that only the inserted or modified data can be found according to the existing timestamp field during data tracing in incremental view.
> If we can do it, I feel that there are two ideas to consider:
> 1. Trace the dataset in the same file at different time check points according to the timeline, compare the two datasets according to the key and filter out the deleted data. This method does not consume extra when writing, but it needs to call the analysis function according to the actual request during query, which consumes a lot.
> 2. When writing data, if there is any deleted data, we will record it. File name such as *.delete_filename_version_timestamp*. So that we can immediately give feedback according to the time. But additional processing will be done at the time of writing.
>  



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