[jira] [Commented] (CASSANDRA-8099) Refactor and modernize the storage engine

["Sylvain Lebresne (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/CASSANDRA-8099?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609796#comment-14609796 ] 

Sylvain Lebresne commented on CASSANDRA-8099:
---------------------------------------------

bq. making complementing open/close/boundary markers equal rather than ordered and making sure there's only one present in each stream

I was already partly making that, but for some reason I hadn't made all the bounds that should be equal, actually equal. But then changing the last bits was actually reasonably simple so I took the liberty to take a quick shot at it (hopefully you haven't started working on it yourself yet). The result is [here|https://github.com/pcmanus/cassandra/commits/8099-RT-fix] and it actually somewhat simplify the merging. I've included your test on the branch and re-enabled the assertion that was failing and {{testDuplicateRangeCase}} passes but I noticed 2 things with that test:
# it actually generate somewhat invalid inputs in that it can generate duplicate rows. That is, the first "order violation" assumption of {{verifyValid}} should really use a strict inequality, but this currently break due to rows (doesn't invalidate the testing of range tombstones, but would be nice to fix it since the tests is more of a general merge test).
# {{testInvalidRangeInput}} fails but that seems expected to me. I suppose the test is meant to check {{verifyValid}} does what it should?

> Refactor and modernize the storage engine
> -----------------------------------------
>
>                 Key: CASSANDRA-8099
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-8099
>             Project: Cassandra
>          Issue Type: Improvement
>            Reporter: Sylvain Lebresne
>            Assignee: Sylvain Lebresne
>             Fix For: 3.0 beta 1
>
>         Attachments: 8099-nit
>
>
> The current storage engine (which for this ticket I'll loosely define as "the code implementing the read/write path") is suffering from old age. One of the main problem is that the only structure it deals with is the cell, which completely ignores the more high level CQL structure that groups cell into (CQL) rows.
> This leads to many inefficiencies, like the fact that during a reads we have to group cells multiple times (to count on replica, then to count on the coordinator, then to produce the CQL resultset) because we forget about the grouping right away each time (so lots of useless cell names comparisons in particular). But outside inefficiencies, having to manually recreate the CQL structure every time we need it for something is hindering new features and makes the code more complex that it should be.
> Said storage engine also has tons of technical debt. To pick an example, the fact that during range queries we update {{SliceQueryFilter.count}} is pretty hacky and error prone. Or the overly complex ways {{AbstractQueryPager}} has to go into to simply "remove the last query result".
> So I want to bite the bullet and modernize this storage engine. I propose to do 2 main things:
> # Make the storage engine more aware of the CQL structure. In practice, instead of having partitions be a simple iterable map of cells, it should be an iterable list of row (each being itself composed of per-column cells, though obviously not exactly the same kind of cell we have today).
> # Make the engine more iterative. What I mean here is that in the read path, we end up reading all cells in memory (we put them in a ColumnFamily object), but there is really no reason to. If instead we were working with iterators all the way through, we could get to a point where we're basically transferring data from disk to the network, and we should be able to reduce GC substantially.
> Please note that such refactor should provide some performance improvements right off the bat but it's not it's primary goal either. It's primary goal is to simplify the storage engine and adds abstraction that are better suited to further optimizations.



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