[GitHub] [iceberg] openinx commented on pull request #2372: Spark: add position delete row reader

[GitBox <gi...@apache.org>]

openinx commented on pull request #2372:
URL: https://github.com/apache/iceberg/pull/2372#issuecomment-809976337


   In my original mind,  there are two kinds of compaction:  
   
   a.  convert all equality deletes into position deletes.  As whether should we eliminate the duplicate position deletes at the same time, the difference for me is:  if the duplicate pos-deletes is removed during rewrite, the user's reading efficiency will be higher; if not, the reading efficiency will be worse. Generally speaking, I think it is a trade-off problem in performance optimization.  Both of them seems to be acceptable to me. 
   
   b. Eliminate all deletes (include pos-deletes and equality-deletes). It is very suitable for the situation where delete has a high proportion in the whole table.  On the one hand, we can save a lot of unnecessary storage, and on the other hand, we can avoid a lot of inefficient joins when reading data.  [This](https://github.com/apache/iceberg/pull/2303/files#diff-605d0d98a73f67629cddbceb9a566e8655844a3cdf46b4dbcebd0e19102e82b4R128) is more simpler to implement compared to the case.a. 
   
   After reading @rdblue 's [comment](https://github.com/apache/iceberg/pull/2372#issuecomment-809823407) , what makes me feel the most valuable is:  we can use the abstraction of meta-column to achieve code unification of case.a, case.b, and the normal read path.  Saying if we have an `iterable=Iterable<Row>` with `_is_deleted` flag inside each row: 
   
   For case.a,   we could just use `Iterables.transform(Iterables.filter(iterable, row -> row.isDeleted()), row -> (row.file(), row.pos()))`  to generate all the pos-deletes.
   
   For case.b,  we could just use `Iterables.filter(iterable, row -> !row.isDeleted())` to get all remaining rows.
   
   For the normal read path, it's same to the case.b.
   
   This implementation greatly reduces the complexity of various paths, I think we can try this kind of code implementation.
   
   
   
   
   
   


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