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From William Katsak <wkat...@gmail.com>
Subject Re: Gossip Analysis
Date Tue, 30 Sep 2014 17:20:36 GMT
Thanks for this info, I will read it now.

The idea is, I have a modified version of Cassandra for a specific 
research application. I want to be able to make a supportable statement 
like this:

After a state change, we wait x*gossip_interval in order to ensure with 
0.9xxxx probability that all nodes have seen the change.

Thanks,
Bill

On 09/30/2014 12:02 PM, Brandon Williams wrote:
> There is this very old ticket:
> https://issues.apache.org/jira/browse/CASSANDRA-617
>
> But note that is gossip simulation, not the actual gossiper Cassandra uses
> (and also very antiquated.)  Using the actual gossiper under simulation is
> unfortunately complicated by
> https://issues.apache.org/jira/browse/CASSANDRA-6881
>
> That said, I'm pretty confident in our implementation, after beating out
> the details the scuttlebutt paper won't tell you over the last years, like
> how to remove a node.  So the number of cycles required to answer you is,
> yet, not scientifically determined in our implementation, but I feel it's
> sufficient in most deployments right now, and the knob we have to turn if
> not is -Dcassandra.ring_delay_ms.
>
> On Tue, Sep 30, 2014 at 10:50 AM, William Katsak <wkatsak@gmail.com> wrote:
>
>> Hello,
>>
>> Forgive me if I have missed anything in the obvious locations, but I am
>> trying to find out if anyone has done an analysis of the gossip protocol as
>> implemented in Cassandra? In particular, I am interested in the the
>> theoretical propagation time (to all nodes) of a change. For example, if a
>> single node makes a change, what is the number of gossip cycles that must
>> elapse before we can be sure (to very high probability obviously, not 100%
>> sure) that everyone has seen it. For those familiar, this is obviously an
>> epidemic analysis, but the Scuttlebutt-style protocol makes it a bit more
>> complex.
>>
>> If anyone already has this, it would be much appreciated.
>>
>> Thanks!
>> -Bill Katsak
>> Ph.D. Student
>> Department of Computer Science
>> Rutgers University
>>
>>
>

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