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From Yang <>
Subject Re: Strong Consistency with ONE read/writes
Date Tue, 12 Jul 2011 16:48:19 GMT
for example,
coord writes record 1,2 ,3 ,4,5 in sequence
if u have replica A, B, C
currently A can have 1 , 3
B can have 1,3,4,
C can have 2345

by "prefix", I mean I want them to have only 1---n  where n is some
number  between 1 and 5,
for example A having 1,2,3
B having 1,2,3,4
C having 1,2,3,4,5

the way we enforce this prefix pattern is that
1) the leader is ensured to have everything that's sent out, otherwise
it's removed from leader position
2) non-leader replicas is guaranteed to receive a prefix, because of
FIFO of the connection between replica and coordinator, if this
connection breaks, replica must catchup from the authoritative source
of leader

there is one point I hand-waved a bit: there are many coordinators,
the "prefix" from each of them is different, still need to think about
this, worst case is that we need to force the traffic come from the
leader, which is less interesting because it's almost hbase then...

On Tue, Jul 12, 2011 at 7:37 AM, AJ <> wrote:
> Yang, I'm not sure I understand what you mean by "prefix of the HLog".
>  Also, can you explain what failure scenario you are talking about?  The
> major failure that I see is when the leader node confirms to the client a
> successful local write, but then fails before the write can be replicated to
> any other replica node.  But, then again, you also say that the leader does
> not forward replicas in your idea; so it's not real clear.
> I'm still trying to figure out how to make this work with normal Cass
> operation.
> aj
> On 7/11/2011 3:48 PM, Yang wrote:
>> I'm not proposing any changes to be done, but this looks like a very
>> interesting topic for thought/hack/learning, so the following are only
>> for thought exercises ....
>> HBase enforces a single write/read entry point, so you can achieve
>> strong consistency by writing/reading only one node.  but just writing
>> to one node exposes you to loss of data if that node fails. so the
>> region server HLog is replicated to 3 HDFS data nodes.  the
>> interesting thing here is that each replica sees a complete *prefix*
>> of the HLog: it won't miss a record, if a record sync() to a data node
>> fails, all the existing bytes in the block are replicated to a new
>> data node.
>> if we employ a similar "leader" node among the N replicas of
>> cassandra (coordinator always waits for the reply from leader, but
>> leader does not do further replication like in HBase or counters), the
>> leader sees all writes onto the key range, but the other replicas
>> could miss some writes, as a result, each of the non-leader replicas'
>> write history has some "holes", so when the leader dies, and when we
>> elect a new one, no one is going to have a complete history. so you'd
>> have to do a repair amongst all the replicas to reconstruct the full
>> history, which is slow.
>> it seems possible that we could utilize the FIFO property of the
>> InComingTCPConnection to simplify history reconstruction, just like
>> Zookeeper. if the IncomingTcpConnection of a replica fails, that means
>> that it may have missed some edits, then when it reconnects, we force
>> it to talk to the active leader first, to catch up to date. when the
>> leader dies, the next leader is elected to be the replica with the
>> most recent history.  by maintaining the property that each node has a
>> complete prefix of history, we only need to catch up on the tail of
>> history, and avoid doing a complete repair on the entire
>> memtable+SStable.  but one issue is that the history at the leader has
>> to be kept really long ----- if a non-leader replica goes off for 2
>> days, the leader has to keep all the history for 2 days to feed them
>> to the replica when it comes back online. but possibly this could be
>> limited to some max length so that over that length, the woken replica
>> simply does a complete bootstrap.
>> thanks
>> yang
>> On Sun, Jul 3, 2011 at 8:25 PM, AJ<>  wrote:
>>> We seem to be having a fundamental misunderstanding.  Thanks for your
>>> comments. aj
>>> On 7/3/2011 8:28 PM, William Oberman wrote:
>>> I'm using cassandra as a tool, like a black box with a certain contract
>>> to
>>> the world.  Without modifying the "core", C* will send the updates to all
>>> replicas, so your plan would cause the extra write (for the placeholder).
>>>  I
>>> wasn't assuming a modification to how C* fundamentally works.
>>> Sounds like you are hacking (or at least looking) at the source, so all
>>> the
>>> power to you if/when you try these kind of changes.
>>> will
>>> On Sun, Jul 3, 2011 at 8:45 PM, AJ<>  wrote:
>>>> On 7/3/2011 6:32 PM, William Oberman wrote:
>>>> Was just going off of: " Send the value to the primary replica and send
>>>> placeholder values to the other replicas".  Sounded like you wanted to
>>>> write
>>>> the value to one, and write the placeholder to N-1 to me.
>>>> Yes, that is what I was suggesting.  The point of the placeholders is to
>>>> handle the crash case that I talked about... "like" a WAL does.
>>>> But, C* will propagate the value to N-1 eventually anyways, 'cause
>>>> that's
>>>> just what it does anyways :-)
>>>> will
>>>> On Sun, Jul 3, 2011 at 7:47 PM, AJ<>  wrote:
>>>>> On 7/3/2011 3:49 PM, Will Oberman wrote:
>>>>> Why not send the value itself instead of a placeholder?  Now it takes
>>>>> 2x
>>>>> writes on a random node to do a single update (write placeholder, write
>>>>> update) and N*x writes from the client (write value, write placeholder
>>>>> to
>>>>> N-1). Where N is replication factor.  Seems like extra network and IO
>>>>> instead of less...
>>>>> To send the value to each node is 1.) unnecessary, 2.) will only cause
>>>>> a
>>>>> large burst of network traffic.  Think about if it's a large data
>>>>> value,
>>>>> such as a document.  Just let C* do it's thing.  The extra messages
>>>>> tiny
>>>>> and doesn't significantly increase latency since they are all sent
>>>>> asynchronously.
>>>>> Of course, I still think this sounds like reimplementing Cassandra
>>>>> internals in a Cassandra client (just guessing, I'm not a cassandra
>>>>> dev)
>>>>> I don't see how.  Maybe you should take a peek at the source.
>>>>> On Jul 3, 2011, at 5:20 PM, AJ<>  wrote:
>>>>> 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
>>>>> 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
>>>>> 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
>>>>> 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
>>>>>> 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
>>>>>> partitioner.  If the first replica becomes unavailable (however
>>>>>> 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
>>>>>> 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
>>>>>> network
>>>>>> performance boost.
>>>> --
>>>> Will Oberman
>>>> Civic Science, Inc.
>>>> 3030 Penn Avenue., First Floor
>>>> Pittsburgh, PA 15201
>>>> (M) 412-480-7835
>>>> (E)
>>> --
>>> Will Oberman
>>> Civic Science, Inc.
>>> 3030 Penn Avenue., First Floor
>>> Pittsburgh, PA 15201
>>> (M) 412-480-7835
>>> (E)

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