While reading the ZooKeeper's recipe for lock, I got confused. It seems that this recipe for distributed locks can not guarantee "any snapshot in time no two clients think they hold the same lock". But since ZooKeeper is so widely adopted, if there were such mistakes in the reference documentation, someone should have pointed it out long ago, so what did I misunderstand?
Quoting the recipe for distributed locks:
Locks
Fully distributed locks that are globally synchronous, meaning at any snapshot in time no two clients think they hold the same lock. These can be implemented using ZooKeeeper. As with priority queues, first define a lock node.
Call create( ) with a pathname of "locknode/guid-lock-" and the sequence and ephemeral flags set.
Call getChildren( ) on the lock node without setting the watch flag (this is important to avoid the herd effect).
If the pathname created in step 1 has the lowest sequence number suffix, the client has the lock and the client exits the protocol.
The client calls exists( ) with the watch flag set on the path in the lock directory with the next lowest sequence number.
if exists( ) returns false, go to step 2. Otherwise, wait for a notification for the pathname from the previous step before going to step 2.
Consider the following case:
Client1 successfully acquired the lock (in step 3), with ZooKeeper node "locknode/guid-lock-0";
Client2 created node "locknode/guid-lock-1", failed to acquire the lock, and is now watching "locknode/guid-lock-0";
Later, for some reason (say, network congestion), Client1 fails to send a heartbeat message to the ZooKeeper cluster on time, but Client1 is still working away, mistakenly assuming that it still holds the lock.
But, ZooKeeper may think Client1's session is timed out, and then
delete "locknode/guid-lock-0",
send a notification to Client2 (or maybe send the notification first?),
but can not send a "session timeout" notification to Client1 in time (say, due to network congestion).
Client2 gets the notification, goes to step 2, gets the only node ""locknode/guid-lock-1", which it created itself; thus, Client2 assumes it hold the lock.
But at the same time, Client1 assumes it holds the lock.
Is this a valid scenario?
The scenario you describe could arise. Client 1 thinks it has the lock, but in fact its session has timed out, and Client 2 acquires the lock.
The ZooKeeper client library will inform Client 1 that its connection has been disconnected (but the client doesn't know the session has expired until the client connects to the server), so the client can write some code and assume that his lock has been lost if he has been disconnected too long. But the thread which uses the lock needs to check periodically that the lock is still valid, which is inherently racy.
...But, Zookeeper may think client1's session is timeouted, and then...
From the Zookeeper documentation:
The removal of a node will only cause one client to wake up since
each node is watched by exactly one client. In this way, you avoid
the herd effect.
There is no polling or timeouts.
So I don't think the problem you describe arises. It looks to me as thought there could be a risk of hanging locks if something happens to the clients that create them, but the scenario you describe should not arise.
from packt book - Zookeeper Essentials
If there was a partial failure in the creation of znode due to connection loss, it's
possible that the client won't be able to correctly determine whether it successfully
created the child znode. To resolve such a situation, the client can store its session ID
in the znode data field or even as a part of the znode name itself. As a client retains
the same session ID after a reconnect, it can easily determine whether the child znode
was created by it by looking at the session ID.
Related
We are observing some behaviours/errors in some of our workflows, related to the consistency and visiblity of a Postgres write transaction, followed by a read. One of our developers offered an explanation, but I could not find any search results documenting the proposed reasoning.
Given a single Postgres 10.3 host, the following operations take place:
ClientA performs a successful write transaction
After the COMMIT, an external notification is emitted
ClientB reacts to external notification and performs a read, only to find that the UPDATE transaction changes are not visible
The explanation that was proposed is that two postgres client connections on different threads don't have a guaranteed view snapshot and may not immediately observe the write transaction update after the commit. But from what I have read, I would expect that after the COMMIT has succeeded, a read operation then starting in response should see the effects of that write.
My specific question is: Given two database client connections on different threads, is it possible for a race condition with one client viewing the effects of a write transaction AFTER the other client has committed? (no overlapping transactions).
Every bit of documentation I have found thus far only refers to concerns about overlapping/concurrent transaction and the MVCC/transaction isolation topics. Nothing about a synchronised serial operation between two different client connections.
Edit: Some extra details about the configuration.
ClientA and ClientB would be different threads accessing postgres through a connection pool. Clients may both be in the same connection pool on the same application server, or it may be ClientA/ApplicationA and ClientB/ApplicationB.
When ClientB reacts, it will access the existing Application server connection pool to make a new read.
No, that cannot happen, unless the reading transaction started earlier and is running at the REPEATABLE READ or SERIALIZABLE isolation level.
There is also the possibility that the reading transaction does not connect to the same server as the writing transaction, but to a streaming replication standby server with hot_standby enabled. Then this can easily happen, even with synchronous replication (unless you set synchronous_commit = remote_apply).
We use Zookeeper to coordinate task execution among our clustered servers. One of our customers have a very instable network and our servers keep disconnecting and reconnecting to Zookeeper.
The problem is that while being disconnected, our servers will miss the events that occurred and won't handle them even after re-connecting to Zookeeper again.
Is there a recommened\standard method to handle such situations using Zookeeper and Apache Curator ?
How to identify the current epoch time at Zookeeper ?
My proposal so far is:
We keep track of the last time we were connected to Zookeeper. That's right before we get disconnected.
On re-connecting again, we ask the listener to clearAndRefresh which fires CHILD_ADDED events for all child nodes for monitored path.
On handling these CHILD_ADDED events, we only handled those for paths that were created or modified after the last time we were connected to Zookeeper.
I don't think using timestamp will be a good idea. Instead, you can use Curator's inbuilt:
TreeCache if you want to watch an entire tree
PathChildrenCache if you want to watch only a sub directory.
It doesn't matter which one you use, both support listening to ChildAdded and DataChanged events which will do exactly what you need. When you reconnect after been disconnected, Curator will internally evaluate newly added children and compare data of existing children to determine changes. No pressure on you. You only need to use the listeners provided.
In terms of accuracy TreeCache is not guaranteeing 100% accuracy. So, you it is better if you can re-design you approach to use PathChildrenCache instead.
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.
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.