Yang,
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.
aj
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 <a...@dude.podzone.net
<mailto:a...@dude.podzone.net>> 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.
