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https://issues.apache.org/jira/browse/HUDI-480?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17149186#comment-17149186
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vinoyang edited comment on HUDI-480 at 7/1/20, 7:28 AM:
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[~vinoth] For the hard deletion, can we log the row key list as the metadata of
a commit?
was (Author: yanghua):
For the hard deletion, can we log the row key list as the metadata of a commit?
> 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
> 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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