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https://issues.apache.org/jira/browse/CASSANDRA-14001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph Lynch resolved CASSANDRA-14001.
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Resolution: Cannot Reproduce
Closing since I don't think the issue here is actually the issue.
> Gossip after node restart can take a long time to converge about "down" nodes
> in large clusters
> -----------------------------------------------------------------------------------------------
>
> Key: CASSANDRA-14001
> URL: https://issues.apache.org/jira/browse/CASSANDRA-14001
> Project: Cassandra
> Issue Type: Improvement
> Components: Lifecycle
> Reporter: Joseph Lynch
> Priority: Minor
>
> When nodes restart in a large cluster, they mark all nodes as "alive", which
> first calls {{markDead}} and then creates an {{EchoMessage}} and in the
> callback to that marks the node as alive. This works great, except when that
> initial echo fails for w.e. reason and that node is marked as dead, in which
> case it will remain dead for a long while.
> We mostly see this on 100+ node clusters, and almost always when nodes are in
> different datacenters that have unreliable network connections (e.g, cross
> region in AWS) and I think that it comes down to a combination of:
> 1. Only a node itself can mark another node as "UP"
> 2. Nodes only gossip with dead nodes with probability {{#dead / (#live +1)}}
> In particular the algorithm in #2 leads to long convergence times because the
> number of dead nodes it typically very small compared to the cluster size. My
> back of the envelope model of this algorithm indicates that for a 100 node
> cluster this would take an average of ~50 seconds with a stdev of 50 seconds,
> which means we might be waiting _minutes_ for the nodes to gossip with each
> other. I'm modeling this as the minimum of two [geometric
> distributions|https://en.wikipedia.org/wiki/Geometric_distribution] with
> parameter {{p=1/#nodes}}, yielding a geometric distribution with parameter
> {{p=1-(1-(1/#nodes)^2)}}. So for a 100 node cluster:
> {noformat}
> 100 node cluster =>
> X = Pr(node1 gossips with node2) = geom(0.01)
> Y = Pr(node 2 gossips with node1) = geom(0.01)
> Z = min(X or Y) = geom(1 - (1 - 0.01)^2) = geom(0.02)
> E[Z] = 1/0.02 = 50
> V[Z] = (1-0.02)/(0.02)^2 = 2450
> 1000 node cluster ->
> Z = geom(1 - (1 - 0.001)^2) = geom(0.002)
> E[Z] = 500
> V[Z] = 24500
> {noformat}
> Since we gossip every second that means that on expectation in a 100 node
> cluster these nodes would see each other after about a minute and in a
> thousand node cluster, after ~8 minutes. For 100 node clusters the variance
> is astounding, and means that in particular edge cases we might be waiting
> hours before these nodes gossip with each other.
> I'm thinking of writing a patch which either:
> # Makes gossip order a shuffled list that includes dead nodes a la [swim
> gossip|https://www.cs.cornell.edu/~asdas/research/dsn02-swim.pdf]. This would
> make it so that we waste some rounds on dead nodes but guarantee linear
> bounding of gossip.
> # Adds an endpoint that re-triggers gossip with all nodes. Operators could
> call this after a restart a few times if they detect a gossip inconsistency.
> # Bounding the probability we gossip with a dead node at some reasonable
> number like 1/10 or something. This might cause a lot of gossip load when a
> node is actually down for large clusters, but would also act to bound the
> variance.
> # Something else?
> I've got a WIP
> [branch|https://github.com/apache/cassandra/compare/cassandra-3.11...jolynch:force_gossip]
> on 3.11 which implements options #1 and #2, but I can reduce/change/modify
> as needed if people think there is a better way. The patch doesn't pass tests
> yet but I'm not going to change/add the tests unless we think moving to time
> bounded gossip for down nodes is a good idea.
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