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  1. Cassandra
  2. CASSANDRA-11475

MV code refactor

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      While working on CASSANDRA-5546 I run into a problem with TTLs on MVs, which looking more closely is a bug of the MV code. But one thing leading to another I reviewed a good portion of the MV code and found the following correction problems:

      • If a base row is TTLed then even if an update remove that TTL the view entry remained TTLed and expires, leading to an inconsistency.
      • Due to calling the wrong ctor for LivenessInfo, when a TTL was set on the base table, the view entry was living twice as long as the TTL. Again leading to a temporary inconsistency.
      • When reading existing data to compute view updates, all deletion informations are completely ignored (the code uses a PartitionIterator instead of an UnfilteredPartitionIterator). This is a serious issue since it means some deletions could be totally ignored as far as views are concerned especially when messages are delivered to a replica out of order. I'll note that while the 2 previous points are relatively easy to fix, I didn't find an easy and clean way to fix this one on the current code.

      Further, I think the MV code in general has inefficiencies/code complexities that should be avoidable:

      • TemporalRow.Set is buffering both everything read and a pretty much complete copy of the updates. That's a potentially high memory requirement. We shouldn't have to copy the updates and we shouldn't buffer all reads but rather work incrementally.
      • TemporalRow/TemporalRow.Set/TemporalCell classes are somewhat re-inventing the wheel. They are really just storing both an update we're doing and the corresponding existing data, but we already have Row/Partition/Cell for that. In practice, those Temporal* class generates a lot of allocations that we could avoid.
      • The code from CASSANDRA-10060 to avoid multiple reads of the base table with multiple views doesn't work when the update has partition/range tombstones because the code uses TemporalRow.Set.setTombstonedExisting() to trigger reuse, but the TemporalRow.Set.withNewViewPrimaryKey() method is used between view and it does not preseve the hasTombstonedExisting flag. But that oversight, which is trivial to fix, is kind of a good thing since if you fix it, you're left with a correction problem.
        The read done when there is a partition deletion depends on the view itself (if there is clustering filters in particular) and so reusing that read for other views is wrong. Which makes that whole reuse code really dodgy imo: the read for existing data is in View.java, suggesting that it depends on the view (which again, it does at least for partition deletion), but it shouldn't if we're going to reuse the result across multiple views.
      • Even ignoring the previous point, we still potentially read the base table twice if the update mix both row updates and partition/range deletions, potentially re-reading the same values.
      • It's probably more minor but the reading code is using QueryPager, which is probably an artifact of the initial version of the code being pre-8099, but it's not necessary anymore (the reads are local and locally we're completely iterator based), adding, especially when we do page. I'll note that despite using paging, the current code still buffers everything in TemporalRow.Set anyway .

      Overall, I suspect trying to fix the problems above (particularly the fact that existing deletion infos are ignored) is only going to add complexity with the current code and we'd still have to fix the inefficiencies. So I propose a refactor of that code which does 2 main things:

      1. it removes all of TemporalRow and related classes. Instead, it directly uses the existing Row (with all its deletion infos) and the update being applied to it and compute the updates for the view from that. I submit that this is more clear/simple, but this also avoid copying every cell of both the existing and update data as a TemporalCell. We can also reuse codes like Rows.merge and Rows.diff to make the handling of deletions relatively painless.
      2. instead of dealing with each view one at a time, re-iterating over all updates each time, it iterates over each individual updates once and deal with each view for that update. This makes it more clear that the reads has to care about every view involved, but more importantly allow to deal with the read data incrementally, never buffering it all.

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              • Assignee:
                slebresne Sylvain Lebresne
                Reporter:
                slebresne Sylvain Lebresne
                Authors:
                Sylvain Lebresne
                Reviewers:
                Carl Yeksigian
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