Backgound:
In section 3, named Implementing a State Machine, of Lamport's paper Paxos Made Simple, Multi-Paxos is described. Multi-Paxos is used in Google Paxos Made Live. (Multi-Paxos is used in Apache ZooKeeper). In Multi-Paxos, gaps can appear:
In general, suppose a leader can get α commands ahead--that is, it can propose commands i + 1 through i + α commands after commands 1 through i are chosen. A gap of up to α - 1 commands could then arise.
Now consider the following scenario:
The whole system uses master-slave architecture. Only the master serves client commands. Master and slaves reach consensus on the sequence of commands via Multi-Paxos. The master is the leader in Multi-Paxos instances. Assume now the master and two of its slaves have the states (commands have been chosen) shown in the following figure:
.
Note that, there are more than one gaps in the master state. Due to asynchrony, the two slaves lag behind. At this time, the master fails.
Problem:
What should the slaves do after they have detected the failure of the master (for example, by heartbeat mechanism)?
In particular, how to handle with the gaps and the missing commands with respect to that of the old master?
Update about Zab:
As #sbridges has pointed out, ZooKeeper uses Zab instead of Paxos. To quote,
Zab is primarily designed for primary-backup (i.e., master-slave) systems, like ZooKeeper, rather than for state machine replication.
It seems that Zab is closely related to my problems listed above. According to the short overview paper of Zab, Zab protocol consists of two modes: recovery and broadcast. In recovery mode, two specific guarantees are made: never forgetting committed messages and letting go of messages that are skipped. My confusion about Zab is:
In recovery mode does Zab also suffer from the gaps problem? If so, what does Zab do?
The gap should be the Paxos instances that has not reached agreement. In the paper Paxos Made Simple, the gap is filled by proposing a special “no-op” command that leaves the state unchanged.
If you cares about the order of chosen values for Paxos instances, you'd better use Zab instead, because Paxos does not preserve causal order. https://cwiki.apache.org/confluence/display/ZOOKEEPER/PaxosRun
The missing command should be the Paxos instances that has reached agreement, but not learned by learner. The value is immutable because it has been accepted a quorum of acceptor. When you run a paxos instance of this instance id, the value will be chosen and recovered to the same one on phase 1b.
When slaves/followers detected a failure on Leader, or the Leader lost a quorum support of slaves/follower, they should elect a new leader.
In zookeeper, there should be no gaps as the follower communicates with leader by TCP which keeps FIFO.
In recovery mode, after the leader is elected, the follower synchronize with leader first, and apply the modification on state until NEWLEADER is received.
In broadcast mode, the follower queues the PROPOSAL in pendingTxns, and wait the COMMIT in the same order. If the zxid of COMMIT mismatch with the zxid of head of pendingTxns, the follower will exit.
https://cwiki.apache.org/confluence/display/ZOOKEEPER/Zab1.0
Multi-Paxos is used in Apache ZooKeeper
Zookeeper uses zab, not paxos. See this link for the difference.
In particular, each zookeeper node in an ensemble commits updates in the same order as every other nodes,
Unlike client requests, state updates must be applied in the exact
original generation order of the primary, starting from the original
initial state of the primary. If a primary fails, a new primary that
executes recovery cannot arbitrarily reorder uncommitted state
updates, or apply them starting from a different initial state.
Specifically the ZAB paper says that a newly elected leader undertakes discovery to learn the next epoch number to set and who has the most up-to-date commit history. The follower sands an ACK-E message which states the max contiguous zxid it has seen. It then says that it undertakes a synchronisation phase where it transmits the state which followers which they have missed. It notes that in interesting optimisation is to only elect a leader which has a most up to date commit history.
With Paxos you don't have to allow gaps. If you do allow gaps then the paper Paxos Made Simple explains how to resolve them from page 9. A new leader knows the last committed value it saw and possibly some committed values above. It probes the slots from the lowest gap it knows about by running phase 1 to propose to those slots. If there are values in those slots it runs phase 2 to fix those values but if it is free to set a value it sets no-op value. Eventually it gets to the slot number where there have been no values proposed and it runs as normal.
In answer to your questions:
What should the slaves do after they have detected the failure of the master (for example, by heartbeat mechanism)?
They should attempt to lead after a randomised delay to try to reduce the risk of two candidates proposing at the same time which would waste messages and disk flushes as only one can lead. Randomised leader timeout is well covered in the Raft paper; the same approach can be used for Paxos.
In particular, how to handle with the gaps and the missing commands with respect to that of the old master?
The new leader should probe and fix the gaps to either the highest value proposed to that slot else a no-op until it has filled in the gaps then it can lead as normal.
The answer of #Hailin explains the gap problem as follows:
In zookeeper, there should be no gaps as the follower communicates with leader by TCP which keeps FIFO"
To supplement:
In the paper A simple totally ordered broadcast protocol, it mentions that ZooKeeper requires the prefix property:
If $m$ is the last message delivered for a leader $L$, any message proposed before $m$ by $L$ must also be delivered".
This property mainly relies on the TCP mechanism used in Zab. In Zab Wiki, it mentions that the implementation of Zab must follow the following assumption (besides others):
Servers must process packets in the order that they are received. Since TCP maintains ordering when sending packets, this means that packets will be processed in the order defined by the sender.
Related
I am new to the Distributed System and Consensus Algorithm. I understand how it works but I am confused by some corner cases: when the acceptors received an ACCEPT for an instance but never heard back about what the final consensus or decision is, what will the acceptors react. For example, the proposer is robooting or failed during commit or right after it sends all the ACCEPT. What will happen in this case?
Thanks.
There are two parts to this question: How do the acceptors react to new proposals? and How do acceptors react if they never learn the result?
In plain-old paxos, the acceptors never actually need to know the result. In fact it is perfectly reasonable that different acceptors have different values in their memory, never knowing if the value they have is the committed value.
The real point of paxos is to deal with the first question. And seeing that the acceptor never actually knows if it has the committed value, it has to assume that it could have the committed but be open to replacing its value if it doesn't have the committed value. How does it know? When receiving a message the proposer always compares the round number and if that is old then the acceptor signals to the proposer that it has to "catch up" first (a Nack). Otherwise, it trusts that the proposer knows what it is doing.
Now for a word about real systems. Some real paxos systems can get away with the acceptors not caring what the committed value is: Paxos is just there to choose what the value will be. But many real systems use Paxos & Friends to make redundant copies of the data for safekeeping.
Some paxos systems will continue paxos-ing until all the acceptors have the data. (Notice that without interference from other proposers, an extra paxos round copies the committed value everywhere.) Others systems are wary about interference from other proposers and will use a different Committed message that teach the acceptors (and other Learners) what the committed value is.
But what happens if the proposer crashes? A subsequent proposer can come along and propose a no-op value. If the subsequent proposer Prepares (Phase 1A) and can communicate with ANY of the acceptors that the prior proposer successfully sent Accepts to (Phase 2A) then it will know what the prior proposer was trying to do (via the response in Phase 1B: PrepareAck). Otherwise a harmless no-op value gets committed.
when the acceptors received an ACCEPT for an instance but never heard back about what the final consensus or decision is, [how] will the acceptors react.
The node sending the value usually learns its value is fixed by counting positive responses to its ACCEPT messages until it sees a majority. If messages are dropped they can be resent until enough messages get through to determine a majority outcome. The acceptors don't have to do anything but accurately follow the algorithm when repeated messages are sent.
For example, the proposer is robooting or failed during commit or right after it sends all the ACCEPT. What will happen in this case?
Indeed this is an interesting case. A value might be accepted by a majority and so fixed but no-one knows as all scheduled messages have failed to arrive.
The responses to PREPARE messages have the information about the values already accepted. So any node can issue PREPARE messages and learn if a value has been fixed. That is actually the genius of Paxos. Once a value is accepted by a majority if is fixed because any node running the algorithm must keep choosing the same value under all message loss and crash scenarios.
Typically Paxos uses a stable leader who streams ACCEPT messages for successive rounds with successive values. If the leader crashes any node can timeout and attempt to lead by sending PREPARE messages. Multiple nodes issuing PREPARE messages trying to lead can interrupt each other giving live-lock. Yet they can never disagree about what value is fixed once it is fixed. They can only compete to get their own value fixed until enough messages get through to have a winner.
Once again acceptor nodes don’t have to do anything other than follow the algorithm when a new leader takes over from a crashed leader. The invariants of the algorithm mean that no leader will contradict any previous leader as to the fixed value. New leaders collaborate with old leaders and acceptors can simply trust that this is the case. Eventually enough messages will get through for all nodes to learn the result.
This question is in reference to https://zookeeper.apache.org/doc/trunk/zookeeperObservers.html
Observers are non-voting members of an ensemble which only hear the
results of votes, not the agreement protocol that leads up to them.
Other than this simple distinction, Observers function exactly the
same as Followers - clients may connect to them and send read and
write requests to them. Observers forward these requests to the Leader
like Followers do, but they then simply wait to hear the result of the
vote. Because of this, we can increase the number of Observers as much
as we like without harming the performance of votes.
Observers have other advantages. Because they do not vote, they are
not a critical part of the ZooKeeper ensemble. Therefore they can
fail, or be disconnected from the cluster, without harming the
availability of the ZooKeeper service. The benefit to the user is that
Observers may connect over less reliable network links than Followers.
In fact, Observers may be used to talk to a ZooKeeper server from
another data center. Clients of the Observer will see fast reads, as
all reads are served locally, and writes result in minimal network
traffic as the number of messages required in the absence of the vote
protocol is smaller.
1) non-voting members of an ensemble - What do the voting members vote on?
2) How does an update request work for observers - When a ZK leader gets an update request, it requires a quorum of nodes to respond. Observer nodes seems like is not considered a quorum node. Does that mean an observer node lags behind the leader node for updates? If that is true, how does it ensure that observer nodes do not respond with stale data during reads?
3) Clients of the Observer will see fast reads, as all reads are served locally, and writes result in minimal network traffic as the number of messages required in the absence of the vote protocol is smaller - Reads from all the other nodes will also be local only because they are in-sync with the leader, no? And I did not get the part about writes.
These questions should be good to understanding zookeeper and distributed systems in general. Appreciate a good detailed answer for these. Thanks in advance !
1) non-voting members of an ensemble - What do the voting members vote on?
Typical members of the ensemble (not observers) vote on success/failure of proposed changes coordinated by the leader. There is some further discussion of the details in the paper ZooKeeper: Wait-free coordination for Internet-scale systems.
2) How does an update request work for observers - When a ZK leader gets an update request, it requires a quorum of nodes to respond. Observer nodes seems like is not considered a quorum node. Does that mean an observer node lags behind the leader node for updates? If that is true, how does it ensure that observer nodes do not respond with stale data during reads?
You are correct that observer nodes are not considered necessary participants in the quorum. In general, update lag will be subject to network latency between the observer and the leader. (Whether or not this is noticeable is subject to specific external factors, such as whether or not the observer and leader are in the same data center with a low-latency network link.)
Note that even without use of observers, there is no guarantee that every server in the ensemble is always completely up to date. The Apache ZooKeeper documentation on Consistency Guarantees contains this disclaimer:
Sometimes developers mistakenly assume one other guarantee that ZooKeeper does not in fact make. This is:
Simultaneously Consistent Cross-Client Views ZooKeeper does not
guarantee that at every instance in time, two different clients will
have identical views of ZooKeeper data. Due to factors like network
delays, one client may perform an update before another client gets
notified of the change. Consider the scenario of two clients, A and B.
If client A sets the value of a znode /a from 0 to 1, then tells
client B to read /a, client B may read the old value of 0, depending
on which server it is connected to. If it is important that Client A
and Client B read the same value, Client B should should call the
sync() method from the ZooKeeper API method before it performs its
read.
However, clients of ZooKeeper will never appear to "go back in time" by reading stale data from a point in time prior to the data they already read. This is accomplished by attaching a monotonically increasing transaction ID (called "zxid") to each ZooKeeper transaction. When the ZooKeeper client interacts with a server, it compares the client's last seen zxid to the current zxid of the server. If the server is behind the client, then it will not allow the client's next read to be processed by that server.
3) Clients of the Observer will see fast reads, as all reads are served locally, and writes result in minimal network traffic as the number of messages required in the absence of the vote protocol is smaller - Reads from all the other nodes will also be local only because they are in-sync with the leader, no? And I did not get the part about writes.
It's important to note that this statement from the documentation is written in the context of an important use-case for observers: multiple data center deployments with higher network latency between different data centers. In this statement, "served locally" means served from a ZooKeeper server within the same data center as the client, so that it doesn't suffer from the longer latency of connecting to another data center. For full context, here is a copy of the full quote:
In fact, Observers may be used to talk to a ZooKeeper server from another data center. Clients of the Observer will see fast reads, as all reads are served locally, and writes result in minimal network traffic as the number of messages required in the absence of the vote protocol is smaller.
I'm developing distributed system that consists of master and worker servers. There should be 2 kind of messages:
Heartbeat
Master gets state of worker and respond immediately with appropriate command. For instance:
Message from Worker to Master: "Hey there! I have data a,b,c"
Response from Master to Worker: "All ok, But throw away c - we dont need this anymore"
The participants exchange this messages with interval T.
Direct master command
Lets say client asks master to kill job #123. Here is conversation:
Message from Master to Worker: "Alarm! We need to kill job #123"
Message from Worker to Master: "No problem! Done."
Obvious that we can't predict when this message appear.
Simplest solution is that master is initiator of all communications for both messages (in case of heartbeat we will include another one from master to start exchange). But lets assume that it is expensive to do all heartbeat housekeeping on master side for N workers. And we don't want to waste our resources to keep several tcp connections to worker servers so we have just one.
Is there any solution for this constraints?
First off, you have to do some bookkeeping somewhere. Otherwise, who's going to realize that a worker has died? The natural place to put that data is on the master, if you're building a master/worker system. Otherwise, the workers could be asked to keep track of each other in a long circle, or a randomized graph. If a worker notices that their accountabilibuddy is not responding anymore, it can alert the master.
Same thing applies to the list of jobs currently running; who keeps track of that? It also scales O(n), so presumably the master doesn't have space for that either. Sharding that data out among the workers (e.g. by keeping track of what things their accountabilibuddy is supposed to be doing) only works so far; if a and b crashes, and a is the only one looking after b, you just lost the list of jobs running on b (and possibly the alert that was supposed to notify you that b crashed).
I'd recommend a distributed consensus algorithm for this kind of task. For production, use something someone else has already written; they probably know what they're doing. If it's for learning purposes, which I presume, have a look at the raft consensus algorithm. It's not too hard to understand, but still highlights a lot of the complexity in distributed systems. The simulator is gold for proper understanding.
A master/worker system will never properly work with less than O(n) resources for n workers in the face of crashing workers. By definition, the master needs to control the workers, which is an O(n) job, even if some workers manage other workers. Also, what happens if the master crashes?
Like Filip Haglund said read the raft paper you should also implement it yourself. However in a nutshell what you need to extract from it would be this. In regaurds to membership management.
You need to keep membership lists and the masters Identity on all nodes.
Raft does it's heartbeat sending on master's end it is not very expensive network wise you don't need to keep them open. Every 200 ms to a second you need to send the heartbeat if they don't reply back the Master tells the slaves remove member x from list.
However what what to do if the master dies well basically you need to preset candidate nodes. If you haven't received a heart beat within the timeout the candidate requests votes from the rest of the cluster. If you get the slightest majority you become the new leader.
If you want to join a existing cluster basically same as above if not leader respond not leader with leaders address.
In the Consistency Guarantees section of ZooKeeper Programmer's Guide, it states that ZooKeeper will give "Single System Image" guarantees:
A client will see the same view of the service regardless of the server that it connects to.
According to the ZAB protocol, only if more than half of the followers acknowledge a proposal, the leader could commit the transaction. So it's likely that not all the followers are in the same status.
If the followers are not in the same status, how could ZooKeeper guarantees "Single System Status"?
References:
ZooKeeper’s atomic broadcast protocol: Theory and practice
Single System Image
Leader only waits for responses from a quorum of the followers to acknowledge to commit a transaction. That doesn't mean that some of the followers need not acknowledge the transaction or can "say no".
Eventually as the rest of the followers process the commit message from leader or as part of the synchronization, will have the same state as the master (with some delay). (not to be confused with Eventual consistency)
How delayed can the follower's state be depends on the configuration items syncLimit & tickTime (https://zookeeper.apache.org/doc/current/zookeeperAdmin.html)
A follower can at most be behind by syncLimit * tickTime time units before it gets dropped.
The document is a little misleading, I have made a pr.
see https://github.com/apache/zookeeper/pull/931.
In fact, zookeeper client keeps a zxid, so it will not connect to older follower if it has read some data from a newer server.
All reads and writes go to a majority of the nodes before being considered successful, so there's no way for a read following a write to not know about that previous write. At least one node knows about it. (Otherwise n/2+1 + n/2+1 > n, which is false.) It doesn't matter if many (at most all but one) has an outdated view of the world since at least one of them knows it all.
If enough nodes crash or the network becomes partitioned so that no group of nodes that are able to talk to each other are in a majority, Zab stops handling requests. If your acknowledged update gets accepted by a set of nodes that disappear and never come back online, your cluster will lose some data (but only when you ask it to move on, and leave its dead nodes behind).
Handling more than two requests is done by handling them two at a time, until there's only one state left.
As stated in Guarantees:
Sequential Consistency - Updates from a client will be applied in the order that they were sent.
Let's assume a client makes 2 updates (update1 and update2) in a very short time window (I understand zookeeper is good at read-domination applications). So my questions are:
Is that possible update2 is received before update1, therefore for zookeeper update1 has later stamp than that of update2? I assume yes due to network connection nature. If this the case that means client will lose its update2 and will have update1. Is there anyway zookeeper can ACK back the client with different stamp or whatever other data that let the client to determine if update2 is really received after update1. Basically zookeeper tells what it sees from server side to client, which gives client some info to act if that's not what the client wants.
What if there is a leader failure after receiving and confirming update1 and before receiving update2? I assume such writes are persisted somewhere in disk/DB etc. When the new leader comes back will it catch up first, meaning conduct update1, before confirming update2 back to client?
Just curious, since zookeeper claims it supports wait-free writing, does that mean there is a message queue built inside zookeeper to hold incoming writes? Otherwise if the leader has to make sure the update is populated to all other followers, the client is actually being blocked by during this replication process. I am guessing that's part of reason zookeeper does not support heavy write application.
For the first two questions, I think you can find details in Zookeeper's paper.
It's quite normal that different operations from the same client arrive in disorder to Zookeeper node. But Zookeeper use TCP to ensure that sequential network package will be receive orderly.
Leader must write operations in Write-Ahead-Log before it can confirm operations. The problems will diverge in two dimensions. The first situation we should consider is whether the leader could recover before followers realize leader failure. If yes, nothing bad will happen, all operations in failure time will lost, and client will resend the operations. If not, then we should consider whether the Leader has proposed a proposal before it fails. If it fails before proposing a proposal, then client will know the failure. If it has proposed a proposal, there must be at least one node in the cluster which has got the newest transactions. Then it will be the new Leader in next rolling. When the original Leader recovers from failure, it will realize he's no longer the leader(All transactions of Zookeeper contains a 64-bits transaction id, of which the higher 32 bits represent epoch, and the lower 32 bits represents proposal id). It will communicate with new Leader and then get updated(Sometimes it need truncate it's local transaction log first).
I don't know the details since I haven't read ZooKeeper's source code. But Leader only needs over half acknowledge from followers before it response to clients. Zookeeper provide both blocking and non-blocking API and you can choose what you like.