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https://issues.apache.org/jira/browse/HUDI-480?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17153133#comment-17153133
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Vinoth Chandar commented on HUDI-480:
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even just storing the row_key, if there are millions of deletes in a batch (e.g
GDPR), it will be problematic..
IMO we should focus on supporting this on MOR..
For COW, if you really want to go after this.. we can read the previous version
of the base file and deduce what key was deleted?
e.g.
f1_commit1.parquet =>. k1, k2, k3
f1_commit2.parquet => k1, k3 (record in .hoodie/commit2.commit that there was a
delete)
Now incremental pull, when you supply `option("hoodie.include.deleted.keys",
"true"), we see that the commit2 has deletes, and then read all keys from
f1_commit1.parquet as well, merge and send along with data written into
f1_commit2.parquet.. This would be a very cool thing to implement
> 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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