jm + quorum   3

Aurora for MySQL is coming
'Anurag@AWS posts a quite interesting comment on Aurora failover: We asynchronously write to 6 copies and ack the write when we see four completions. So, traditional 4/6 quorums with synchrony as you surmised. Now, each log record can end up with a independent quorum from any other log record, which helps with jitter, but introduces some sophistication in recovery protocols. We peer to peer to fill in holes. We also will repair bad segments in the background, and downgrade to a 3/4 quorum if unable to place in an AZ for any extended period. You need a pretty bad failure to get a write outage.' (via High Scalability)
via:highscalability  mysql  aurora  failover  fault-tolerance  aws  replication  quorum 
december 2014 by jm
CAP Confusion: Problems with ‘partition tolerance’
Another good clarification about CAP which resurfaced during last week's discussion:
So what causes partitions? Two things, really. The first is obvious – a network failure, for example due to a faulty switch, can cause the network to partition. The other is less obvious, but fits with the definition [...]: machine failures, either hard or soft. In an asynchronous network, i.e. one where processing a message could take unbounded time, it is impossible to distinguish between machine failures and lost messages. Therefore a single machine failure partitions it from the rest of the network. A correlated failure of several machines partitions them all from the network. Not being able to receive a message is the same as the network not delivering it. In the face of sufficiently many machine failures, it is still impossible to maintain availability and consistency, not because two writes may go to separate partitions, but because the failure of an entire ‘quorum’ of servers may render some recent writes unreadable.

(sorry, catching up on old interesting things posted last week...)
failure  scalability  network  partitions  cap  quorum  distributed-databases  fault-tolerance 
may 2013 by jm
Alex Feinberg's response to Damien Katz' anti-Dynamoish/pro-Couchbase blog post
Insightful response, worth bookmarking. (the original post is at http://damienkatz.net/2013/05/dynamo_sure_works_hard.html ).
while you are saving on read traffic (online reads only go to the master), you are now decreasing availability (contrary to your stated goal), and increasing system complexity.
You also do hurt performance by requiring all writes and reads to be serialized through a single node: unless you plan to have a leader election whenever the node fails to meet a read SLA (which is going to result a disaster -- I am speaking from personal experience), you will have to accept that you're bottlenecked by a single node. With a Dynamo-style quorum (for either reads or writes), a single straggler will not reduce whole-cluster latency.
The core point of Dynamo is low latency, availability and handling of all kinds of partitions: whether clean partitions (long term single node failures), transient failures (garbage collection pauses, slow disks, network blips, etc...), or even more complex dependent failures.
The reality, of course, is that availability is neither the sole, nor the principal concern of every system. It's perfect fine to trade off availability for other goals -- you just need to be aware of that trade off.
cap  distributed-databases  databases  quorum  availability  scalability  damien-katz  alex-feinberg  partitions  network  dynamo  riak  voldemort  couchbase 
may 2013 by jm

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