I'm no expert. So addressing the question to me probably give you real answers :)
The single entry mode makes sure that all writes coming through the leader are received by replicas before ack to client. Probably wont be stale data
On Jul 3, 2011 11:20 AM, "AJ" <email@example.com> wrote:
> How would you deal with the problem when the 1st node responds success
> but then crashes before completely forwarding any replicas? Then, after
> switching to the next primary, a read would return stale data.
> Here's a quick-n-dirty way: Send the value to the primary replica and
> send placeholder values to the other replicas. The placeholder value is
> something like, "PENDING_UPDATE". The placeholder values are sent with
> timestamps 1 less than the timestamp for the actual value that went to
> the primary. Later, when the changes propagate, the actual values will
> overwrite the placeholders. In event of a crash before the placeholder
> gets overwritten, the next read value will tell the client so. The
> client will report to the user that the key/column is unavailable. The
> downside is you've overwritten your data and maybe would like to know
> what the old data was! But, maybe there's another way using other
> columns or with MVCC. The client would want a success from the primary
> and the secondary replicas to be certain of future read consistency in
> case the primary goes down immediately as I said above. The ability to
> set an "update_pending" flag on any column value would probably make
> this work. But, I'll think more on this later.
> On 7/2/2011 10:55 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 <firstname.lastname@example.org
>> <mailto:email@example.com>> 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.