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From "Branimir Lambov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-11349) MerkleTree mismatch when multiple range tombstones exists for the same partition and interval
Date Wed, 04 May 2016 12:55:13 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-11349?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270572#comment-15270572
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Branimir Lambov commented on CASSANDRA-11349:
---------------------------------------------

As I see it neither solution will be sufficient. A lot of the visible effects of the problem
come as a side effect of CASSANDRA-7953, but there are some underlying issues that are only
really solved in 3.0 by the new tombstone handling from CASSANDRA-8099.

Whether we change {{onDiskAtomComparator}} or not, we will still get disordered or multiple
equal range tombstones from a single source as that's how they are written in the sstables.
{{MergeIterator}} will not combine equal entries from the same source, even if it did and
everything was written using the {{onDiskAtomComparator}} (which I don't believe to be the
case), it is still in the wrong order for resolving which tombstones can be deleted without
delaying their processing.

In other words the problem cannot be solved by changing the reducer; we can, however, do it
if we change {{update}} to follow closely or, better still, _call_ {{IndexBuilder.buildForCompaction}}
and make the builder accept a prepared atom serializer (or some subinterface) instead of an
output file, and update the digest in the calls to that serializer.

> MerkleTree mismatch when multiple range tombstones exists for the same partition and
interval
> ---------------------------------------------------------------------------------------------
>
>                 Key: CASSANDRA-11349
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-11349
>             Project: Cassandra
>          Issue Type: Bug
>            Reporter: Fabien Rousseau
>            Assignee: Stefan Podkowinski
>              Labels: repair
>             Fix For: 2.1.x, 2.2.x
>
>         Attachments: 11349-2.1-v2.patch, 11349-2.1-v3.patch, 11349-2.1.patch
>
>
> We observed that repair, for some of our clusters, streamed a lot of data and many partitions
were "out of sync".
> Moreover, the read repair mismatch ratio is around 3% on those clusters, which is really
high.
> After investigation, it appears that, if two range tombstones exists for a partition
for the same range/interval, they're both included in the merkle tree computation.
> But, if for some reason, on another node, the two range tombstones were already compacted
into a single range tombstone, this will result in a merkle tree difference.
> Currently, this is clearly bad because MerkleTree differences are dependent on compactions
(and if a partition is deleted and created multiple times, the only way to ensure that repair
"works correctly"/"don't overstream data" is to major compact before each repair... which
is not really feasible).
> Below is a list of steps allowing to easily reproduce this case:
> {noformat}
> ccm create test -v 2.1.13 -n 2 -s
> ccm node1 cqlsh
> CREATE KEYSPACE test_rt WITH replication = {'class': 'SimpleStrategy', 'replication_factor':
2};
> USE test_rt;
> CREATE TABLE IF NOT EXISTS table1 (
>     c1 text,
>     c2 text,
>     c3 float,
>     c4 float,
>     PRIMARY KEY ((c1), c2)
> );
> INSERT INTO table1 (c1, c2, c3, c4) VALUES ( 'a', 'b', 1, 2);
> DELETE FROM table1 WHERE c1 = 'a' AND c2 = 'b';
> ctrl ^d
> # now flush only one of the two nodes
> ccm node1 flush 
> ccm node1 cqlsh
> USE test_rt;
> INSERT INTO table1 (c1, c2, c3, c4) VALUES ( 'a', 'b', 1, 3);
> DELETE FROM table1 WHERE c1 = 'a' AND c2 = 'b';
> ctrl ^d
> ccm node1 repair
> # now grep the log and observe that there was some inconstencies detected between nodes
(while it shouldn't have detected any)
> ccm node1 showlog | grep "out of sync"
> {noformat}
> Consequences of this are a costly repair, accumulating many small SSTables (up to thousands
for a rather short period of time when using VNodes, the time for compaction to absorb those
small files), but also an increased size on disk.



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