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From "Sylvain Lebresne (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-2699) continuous incremental anti-entropy
Date Tue, 26 Feb 2013 08:46:18 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-2699?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13586942#comment-13586942
] 

Sylvain Lebresne commented on CASSANDRA-2699:
---------------------------------------------

bq. I'm pretty sure this means that we should be able to XOR the buckets together from pre-computed
merkle tree SSTable components

No, I don't think that works. Because nodes are certainly not guaranteed to be at the same
state of compaction, even if they have the same data. Meaning that for a given column whose
value A has been overwritten to B at some point, one of the nodes may have 1 sstable with
just B while another node may have 2 sstables, one with A and one with B, because it hasn't
compacted things yet. But hash(B) (on the first node) will not be equal to hash(A) xor hash(B)
(on the second node), even though both node really have the same data (since B overwrites
A upon column merge on the 2nd node).

                
> continuous incremental anti-entropy
> -----------------------------------
>
>                 Key: CASSANDRA-2699
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-2699
>             Project: Cassandra
>          Issue Type: Improvement
>            Reporter: Peter Schuller
>            Assignee: Peter Schuller
>
> Currently, repair works by periodically running "bulk" jobs that (1)
> performs a validating compaction building up an in-memory merkle tree,
> and (2) streaming ring segments as needed according to differences
> indicated by the merkle tree.
> There are some disadvantages to this approach:
> * There is a trade-off between memory usage and the precision of the
>   merkle tree. Less precision means more data streamed relative to
>   what is strictly required.
> * Repair is a periodic "bulk" process that runs for a significant
>   period and, although possibly rate limited as compaction (if 0.8 or
>   backported throttling patch applied), is a divergence in terms of
>   performance characteristics from "normal" operation of the cluster.
> * The impact of imprecision can be huge on a workload dominated by I/O
>   and with cache locality being critical, since you will suddenly
>   transfers lots of data to the target node.
> I propose a more incremental process whereby anti-entropy is
> incremental and continuous over time. In order to avoid being
> seek-bound one still wants to do work in some form of bursty fashion,
> but the amount of data processed at a time could be sufficiently small
> that the impact on the cluster feels a lot more continuous, and that
> the page cache allows us to avoid re-reading differing data twice.
> Consider a process whereby a node is constantly performing a per-CF
> repair operation for each CF. The current state of the repair process
> is defined by:
> * A starting timestamp of the current iteration through the token
>   range the node is responsible for.
> * A "finger" indicating the current position along the token ring to
>   which iteration has completed.
> This information, other than being in-memory, could periodically (every
> few minutes or something) be stored persistently on disk.
> The finger advances by the node selecting the next small "bit" of the
> ring and doing whatever merkling/hashing/checksumming is necessary on
> that small part, and then asking neighbors to do the same, and
> arranging for neighbors to send the node data for mismatching
> ranges. The data would be sent either by way of mutations like with
> read repair, or by streaming sstables. But it would be small amounts
> of data that will act roughly the same as regular writes for the
> perspective of compaction.
> Some nice properties of this approach:
> * It's "always on"; no periodic sudden effects on cluster performance.
> * Restarting nodes never cancels or breaks anti-entropy.
> * Huge compactions of entire CF:s never clog up the compaction queue
>   (not necessarily a non-issue even with concurrent compactions in
>   0.8).
> * Because we're always operating on small chunks, there is never the
>   same kind of trade-off for memory use. A merkel tree or similar
>   could be calculated at a very detailed level potentially. Although
>   the precision from the perspective of reading from disk would likely
>   not matter much if we are in page cache anyway, very high precision
>   could be *very* useful when doing anti-entropy across data centers
>   on slow links.
> There are devils in details, like how to select an appropriate ring
> segment given that you don't have knowledge of the data density on
> other nodes. But I feel that the overall idea/process seems very
> promising.

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