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From "Peter Schuller (Updated) (JIRA)" <>
Subject [jira] [Updated] (CASSANDRA-3831) scaling to large clusters in GossipStage impossible due to calculatePendingRanges
Date Sat, 04 Feb 2012 01:25:53 GMT


Peter Schuller updated CASSANDRA-3831:

    Attachment: CASSANDRA-3831-memoization-not-for-inclusion.txt

I am attaching {{CASSANDRA\-3831\-memoization\-not\-for\-inclusion.txt}} as an "FYI" and in
case it helps others. It's against 0.8, and implements memoization of calculate pending ranges.

The correct/clean fix is probably to change behavior so that it doesn't get called unnecessarily
to begin with. This patch was made specifically to address the production issue we are having
in a minimally dangerous fashion, and is not to be taken as a suggested fix.
> scaling to large clusters in GossipStage impossible due to calculatePendingRanges 
> ----------------------------------------------------------------------------------
>                 Key: CASSANDRA-3831
>                 URL:
>             Project: Cassandra
>          Issue Type: Bug
>          Components: Core
>            Reporter: Peter Schuller
>            Assignee: Peter Schuller
>            Priority: Critical
>         Attachments: CASSANDRA-3831-memoization-not-for-inclusion.txt
> (most observations below are from 0.8, but I just now tested on
> trunk and I can trigger this problem *just* by bootstrapping a ~180
> nod cluster concurrently, presumably due to the number of nodes that
> are simultaneously in bootstrap state)
> It turns out that:
> * (1) calculatePendingRanges is not just expensive, it's computationally complex - cubic
or worse
> * (2) it gets called *NOT* just once per node being bootstrapped/leaving etc, but is
called repeatedly *while* nodes are in these states
> As a result, clusters start exploding when you start reading 100-300
> nodes. The GossipStage will get backed up because a single
> calculdatePenginRanges takes seconds, and depending on what the
> average heartbeat interval is in relation to this, this can lead to
> *massive* cluster-wide flapping.
> This all started because we hit this in production; several nodes
> would start flapping several other nodes as down, with many nodes
> seeing the entire cluster, or a large portion of it, as down. Logging
> in to some of these nodes you would see that they would be constantly
> flapping up/down for minutes at a time until one became lucky and it
> stabilized.
> In the end we had to perform an emergency full-cluster restart with
> gossip patched to force-forget certain nodes in bootstrapping state.
> I can't go into all details here from the post-mortem (just the
> write-up would take a day), but in short:
> * We graphed the number of hosts in the cluster that had more than 5
>   Down (in a cluster that should have 0 down) on a minutely timeline.
> * We also graphed the number of hosts in the cluster that had GossipStage backed up.
> * The two graphs correlated *extremely* well
> * jstack sampling showed it being CPU bound doing mostly sorting under calculatePendingRanges
> * We were never able to exactly reproduce it with normal RING_DELAY and gossip intervals,
even on a 184 node cluster (the production cluster is around 180).
> * Dropping RING_DELAY and in particular dropping gossip interval to 10 ms instead of
1000 ms, we were able to observe all of the behavior we saw in production.
> So our steps to reproduce are:
> * Launch 184 node cluster w/ gossip interval at 10ms and RING_DELAY at 1 second.
> * Do something like: {{while [ 1 ] ; do date ; echo decom ; nodetool decommission ; date
; echo done leaving decommed for a while ; sleep 3 ; date ; echo done restarting; sudo rm
-rf /data/disk1/commitlog/* ; sudo rm -rf /data/diskarray/tables/* ; sudo monit restart cassandra
;date ; echo restarted waiting for a while ; sleep 40; done}} (or just do a manual decom/bootstrap
once, it triggers every time)
> * Watch all nodes flap massively and not recover at all, or maybe after a *long* time.
> I observed the flapping using a python script that every 5 second
> (randomly spread out) asked for unreachable nodes from *all* nodes in
> the cluster, and printed any nodes and their counts when they had
> unreachables > 5. The cluster can be observed instantly going into
> massive flapping when leaving/bootstrap is initiated. Script needs
> Cassandra running with Jolokia enabled for http/json access to
> JMX. Can provide scrit if needed after cleanup.
> The phi conviction, based on logging I added, was legitimate. Using
> the 10 ms interval the average heartbeat interval ends up being like 25
> ms or something like that. As a result, a single ~ 2 second delay in
> gossip stage is huge in comparison to those 25 ms, and so we go past
> the phi conviction threshold. This is much more sensitive than in
> production, but it's the *same* effect, even if it triggers less
> easily for real.
> The best work around currently internally is to memoize
> calculatePendingRanges so that we don't re-calculate if token meta
> data, list of moving, list of bootstrapping and list of leaving are
> all the same as on prior calculation. It's not entirely clear at this
> point whether there is a clean fix to avoid executing
> calculatePendingRanges more than once per unique node in this state.
> It should be noted though that even if that is fixed, it is not
> acceptable to spend several seconds doing these calculations on a ~
> 200 node cluster and it needs to be made fundamentally more efficient.
> Here is a dump of thoughts by me in an internal JIRA ticket (not
> exhaustive, I just went as far as to show that there is an issue;
> there might be worse things I missed, but worse than cubic is bad
> enough that I stopped):
> (Comment uses 0.8 source.)
> {quote}
> Okay, so let's break down the computational complexity here.
> Suppose ring size is {{n}} and number of bootstrapping/leaving tokens is {{m}}.  One
of two places that take time (by measurement) is this part of calculatePendingRanges():
> {code}
>        // At this stage pendingRanges has been updated according to leave operations.
We can
>         // now continue the calculation by checking bootstrapping nodes.
>         // For each of the bootstrapping nodes, simply add and remove them one by one
>         // allLeftMetadata and check in between what their ranges would be.
>         for (Map.Entry<Token, InetAddress> entry : bootstrapTokens.entrySet())
>         {
>             InetAddress endpoint = entry.getValue();
>             allLeftMetadata.updateNormalToken(entry.getKey(), endpoint);
>             for (Range range : strategy.getAddressRanges(allLeftMetadata).get(endpoint))
>                 pendingRanges.put(range, endpoint);
>             allLeftMetadata.removeEndpoint(endpoint);
>         }
> {code}
> I'll ignore stuff that's log(n) or better.
> The outer loops is {{O(m)}}. The inner loop is {{O(n)}}, making aggregate so far {{O(nm)}}.
> We have a call in there to updateNormalTokens() which implies a sorting, which his {{O(n
log(n))}}. So now we're at {{O(n log(n) m)}}.
> Next up we call {{getAddressRanges()}} which immediately does another {{O(n log(n)}}
sort. we're still at {{O(n log(n) m}}. It then iterates (linear) and:
> * calls {{getPrimaryRangeFor()}} for each.
> * calls {{calculateNaturalEndpoints}} for each.
> The former ends up sorting again, so now we're at {{O(n log(n) n log(n) m}} (worse than
> {{NTS.calculateNaturalEndpoints}} starts by collecting token meta data for nodes in the
DC, by using {{updateNormalToken}}, which *implies sorting*. Woha woha. Now we're at {{O(n
log(n) n log (n) n log(n) m)}}.
> I might have missed things that are even worse, but this is bad enough to warrant this
ticket. To put into perspective, 168 ^ 3 is 4.7 million.
> {quote}

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