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From Edward Capriolo <>
Subject Re: Strong Consistency with ONE read/writes
Date Sat, 02 Jul 2011 21:08:29 GMT
On Sat, Jul 2, 2011 at 3:57 PM, Yang <> wrote:

> Jonathan:
> could you please elaborate more on specifically why they are "not even
> close"?
>  --- I kind of see what you mean (please correct me if I misunderstood):
> Cassandra failure detector
> is consulted on every write; while HBase failure detector is only used when
> the tablet server joins or leaves.
>  in order to have the single write entry point approach originally brought
> up in this thread,
> I think you need a strong membership protocol to lock on the key range
>  leadership, once leadership is acquired,
> failure detectors do not need to be consulted on every write.
> yes by definition of the original requirement brought up in this thread,
> Cassandra's write behavior is going to be changed, to be more like Hbase,
> and mongo in "replica set" mode. but
> it seems that this leader mode can even co-exist with the multi-entry write
> mode that Cassandra uses now, just as
> you can use different CL for each single write request.  in that case you
> would need to keep both the current lightweight Phi-detector
> and add the ZK for leader election for single-entry mode write.
> Thanks
> Yang
> (I should correct my terminology .... it's not a "strong failure detector"
> that's needed, it's a "strong membership protocol". strongly complete and
> accurate failure detectors do not exist in
> async distributed systems (Tushar Chandra  " Unreliable Failure Detectors
> for Reliable Distributed Systems, Journal of the ACM, 43(2):225-267, 1996<>"
>  and FLP "Impossibility of  Distributed Consensus with One Faulty Process<>"
> )  )
> On Sat, Jul 2, 2011 at 10:11 AM, Jonathan Ellis <> wrote:
>> The way HBase uses ZK (for master election) is not even close to how
>> Cassandra uses the failure detector.
>> Using ZK for each operation would (a) not scale and (b) not work
>> cross-DC for any reasonable latency requirements.
>> On Sat, Jul 2, 2011 at 11:55 AM, Yang <> wrote:
>> > there is a JIRA completed in 0.7.x that "Prefers" a certain node in
>> snitch,
>> > so this does roughly what you want MOST of the time
>> >
>> > but the problem is that it does not GUARANTEE that the same node will
>> always
>> > be read.  I recently read into the HBase vs Cassandra comparison thread
>> that
>> > started after Facebook dropped Cassandra for their messaging system, and
>> > understood some of the differences. what you want is essentially what
>> HBase
>> > does. the fundamental difference there is really due to the gossip
>> protocol:
>> > it's a probablistic, or eventually consistent failure detector  while
>> > HBase/Google Bigtable use Zookeeper/Chubby to provide a strong failure
>> > detector (a distributed lock).  so in HBase, if a tablet server goes
>> down,
>> > it really goes down, it can not re-grab the tablet from the new tablet
>> > server without going through a start up protocol (notifying the master,
>> > which would notify the clients etc),  in other words it is guaranteed
>> that
>> > one tablet is served by only one tablet server at any given time.  in
>> > comparison the above JIRA only TRYIES to serve that key from one
>> particular
>> > replica. HBase can have that guarantee because the group membership is
>> > maintained by the strong failure detector.
>> > just for hacking curiosity, a strong failure detector + Cassandra
>> replicas
>> > is not impossible (actually seems not difficult), although the
>> performance
>> > is not clear. what would such a strong failure detector bring to
>> Cassandra
>> > besides this ONE-ONE strong consistency ? that is an interesting
>> question I
>> > think.
>> > considering that HBase has been deployed on big clusters, it is probably
>> OK
>> > with the performance of the strong  Zookeeper failure detector. then a
>> > further question was: why did Dynamo originally choose to use the
>> > probablistic failure detector? yes Dynamo's main theme is "eventually
>> > consistent", so the Phi-detector is **enough**, but if a strong detector
>> > buys us more with little cost, wouldn't that  be great?
>> >
>> >
>> > On Fri, Jul 1, 2011 at 6:53 PM, AJ <> wrote:
>> >>
>> >> Is this possible?
>> >>
>> >> All reads and writes for a given key will always go to the same node
>> from
>> >> a client.  It seems the only thing needed is to allow the clients to
>> compute
>> >> which node is the closes replica for the given key using the same
>> algorithm
>> >> C* uses.  When the first replica receives the write request, it will
>> write
>> >> to itself which should complete before any of the other replicas and
>> then
>> >> return.  The loads should still stay balanced if using random
>> partitioner.
>> >>  If the first replica becomes unavailable (however that is defined),
>> then
>> >> the clients can send to the next repilca in the ring and switch from
>> ONE
>> >> write/reads to QUORUM write/reads temporarily until the first replica
>> >> becomes available again.  QUORUM is required since there could be some
>> >> replicas that were not updated after the first replica went down.
>> >>
>> >> Will this work?  The goal is to have strong consistency with a
>> read/write
>> >> consistency level as low as possible while secondarily a network
>> performance
>> >> boost.
>> >
>> >
>> --
>> Jonathan Ellis
>> Project Chair, Apache Cassandra
>> co-founder of DataStax, the source for professional Cassandra support
They are not close.

HBase uses zookeeper and its master servers as a single source of truth.
When a node responsible for a region is down it can not be read or written
to until the master elects a new node to be responsible for the region. Also
all the storage is one back namely HDFS.

With Cassandra each node has a failure detector and it's own view of the
network. This is why Cassandra is peer to peer. This is why the failure
detector is consulted on every write/read because the network is converging.
With Cassandra each node has its own storage. This is why you can work at
ONE and QUORUM and clients are unaffected by single node failures.

If you want Cassandra to work like hbase you can....
1) Buy a SAN
2) make one LUN for each node
3) Use replication factor=1
4) running linux-ha on each node
5) when a Cassandra fails, linux-ha can detect failure and take over that
nodes data and Cassandra instance :)


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