[jira] [Commented] (CASSANDRA-4011) range-based log(n) elimination of sstables in read path

["Jonathan Ellis (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/CASSANDRA-4011?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13563179#comment-13563179 ] 

Jonathan Ellis commented on CASSANDRA-4011:
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DataTracker.intervalTree is used on the read path regardless of compactionstrategy.
                
> range-based log(n) elimination of sstables in read path
> -------------------------------------------------------
>
>                 Key: CASSANDRA-4011
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-4011
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Peter Schuller
>
> If the read path was able to eliminate sstables based on token ranges, we would avoid {{O(n)}} bloom filter checks ({{n}} being number of sstables).
> Contributing motivation:
> * For maximally efficient bulk-import, you tend to want a lot of small sstables to avoid having to build up huge ones during the bulk creation process.
> * To avoid having to keep duplicate data when switching a data set (in a periodic bulk replace import process), keeping sstables partitioned on token range (similarly to leveled compaction) allows in-place replacement of sstables one sstable at a time.
> Those two in combination would mean that you can run a bulk-import based total-dataset-replacement cluster with zero compaction and with zero disk space overhead stemming from having to have overhead for compaction.
> In addition:
> * For e.g. leveled compaction where we have range based partitioning anyway, {{log(n)}} is preferable to {{o(n)}}; especially if it would allow us to have more than 10 "partitions" per level. I'm not sure yet whether there are other reasons to have "only" 10, but if we can make them smaller by eliminating the {{o(n)}} behavior in the read path, individual compactions can be even smaller with leveled and you would scale even more easily with large data sets while avoiding build-up in L0.

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