ZooKeeper is a highly available, high-performance coordination service. In this section, we look at the nature of the service it provides: its model, operations, and implementation.
ZooKeeper maintains a hierarchical tree of nodes called znodes. A znode stores data and has an associated ACL. ZooKeeper is designed for coordination (which typically uses small data files), not high-volume data storage, so there is a limit of 1 MB on the amount of data that may be stored in any znode.
Data access is atomic. A client reading the data stored at a znode will never receive only some of the data; either the data will be delivered in its entirety, or the read will fail.
Similarly, a write will replace all the data associated with a znode. ZooKeeper guarantees that the write will either succeed or fail; there is no such thing as a partial write, where only some of the data written by the client is stored. ZooKeeper does not support an append operation. These characteristics contrast with HDFS, which is designed for high-volume data storage, with streaming data access, and provides an appendoperation.
Znodes are referenced by paths, which in ZooKeeper are represented as slash-delimited Unicode character strings, like filesystem paths in Unix. Paths must be absolute, so they must begin with a slash character. Furthermore, they are canonical, which means that each path has a single representation, and so paths do not undergo resolution. For example, in Unix, a file with the path /a/b can equivalently be referred to by thepath /a/./b, since “.” refers to the current directory at the point it is encountered in the path. In ZooKeeper, “.” does not have this special meaning and is actually illegal as a path component (as is “..” for the parent of the current directory).
Path components are composed of Unicode characters, with a few restrictions (these are spelled out in the ZooKeeper reference documentation). The string “zookeeper” is a reserved word and may not be used as a path component. In particular, ZooKeeper uses the /zookeeper subtree to store management information, such as information on quotas.
Note that paths are not URIs, and they are represented in the Java API by a java.lang.String, rather than the Hadoop Path class (or by the java.net.URI class, for that matter).
Znodes have some properties that are very useful for building distributed applications, which we discuss in the following sections.
Znodes can be one of two types: ephemeral or persistent. A znode’s type is set at creation time and may not be changed later. An ephemeral znode is deleted by ZooKeeper when the creating client’s session ends. By contrast, a persistent znode is not tied to the client’s session and is deleted only when explicitly deleted by a client (not necessarily the one that created it). An ephemeral znode may not have children, not even ephemeral ones.
Even though ephemeral nodes are tied to a client session, they are visible to all clients (subject to their ACL policy, of course). Ephemeral znodes are ideal for building applications that need to know when certaindistributed resources are available. The example earlier in this chapter uses ephemeral znodes to implement a group membership service, so any process can discover the members of the group at any particular time.
A sequential znode is given a sequence number by ZooKeeper as a part of its name. If a znode is created with the sequential flag set, then the value of a monotonically increasing counter (maintained by the parent znode) is appended to its name.
If a client asks to create a sequential znode with the name /a/b-, for example, then the znode created may actually have the name /a/b-3.§ If, later on, another sequential znode with the name /a/b- is created, then it will be given a unique name with a larger value of the counter for example, /a/b-5. In the Java API, the actual path given to sequential znodes is communicated back to the client as the return value of the create() call.
Sequence numbers can be used to impose a global ordering on events in a distributed system, and may be used by the client to infer the ordering. In “A Lock Service” , you will learn how to use sequential znodes to build a shared lock.
Watches allow clients to get notifications when a znode changes in some way. Watches are set by operations on the ZooKeeper service, and are triggered by other operations on the service. For example, a client might call the exists operation on a znode, placing a watch on it at the same time. If the znode doesn’t exist, then the exists operation will return false. If, some time later, the znode is created by a second client, then thewatch is triggered, notifying the first client of the znode’s creation. You will see precisely which operations trigger others in the next section.
Watchers are triggered only once. To receive multiple notifications, a client needs to reregister the watch. If the client in the previous example wishes to receive further § It is conventional (but not required) to have a trailing dash on path names for sequential nodes, to make their sequence numbers easy to read and parse (by the application). Except for callbacks for connection events, which do not need reregistration.notifications for the znode’s existence (to be notified when it is deleted, for example), it needs to call the exists operation again to set a new watch.
There is an example in “A Configuration Service” demonstrating how to use watches to update configuration across a cluster.
There are nine basic operations in ZooKeeper, listed in Table
Update operations in ZooKeeper are conditional. A delete or setData operation has to specify the version number of the znode that is being updated (which is found from a previous exists call). If the version number does not match, the update will fail. Updates are a nonblocking operation, so a client that loses an update (because another process updated the znode in the meantime) can decide whether to try again or take some other action, and it can do so without blocking the progress of any other process.
Although ZooKeeper can be viewed as a filesystem, there are some filesystem primitives that it does away with in the name of simplicity. Because files are small and are written and read in their entirety, there is no need to provide open, close, or seek operations.
The sync operation is not like fsync() in POSIX filesystems. As mentioned earlier, writes in ZooKeeper are atomic, and a successful write operation is guaranteed to have been written to persistent storage on amajority of ZooKeeper servers. However, it is permissible for reads to lag the latest state of ZooKeeper service, and the sync operation exists to allow a client to bring itself up-to-date. This topic is covered in moredetail in the section on “Consistency” .
There are two core language bindings for ZooKeeper clients, one for Java and one for C; there are also contrib bindings for Perl, Python, and REST clients. For each binding, there is a choice between performing operations synchronously or asynchronously.We’ve already seen the synchronous Java API. Here’s the signature for the exists operation, which returns a Stat object encapsulating the znode’s metadata, or null if theznode doesn’t exist:
public Stat exists(String path, Watcher watcher) throws KeeperException,
The asynchronous equivalent, which is also found in the ZooKeeper class, looks like this:
public void exists(String path, Watcher watcher, StatCallback cb, Object ctx)
In the Java API, all the asynchronous methods have void return types, since the result of the operation is conveyed via a callback. The caller passes a callback implementation, whose method is invoked when a response is received from ZooKeeper. In this case, the callback is the StatCallback interface, which has the following method:
public void processResult(int rc, String path, Object ctx, Stat stat);
The rc argument is the return code, corresponding to the codes defined by KeeperEx ception. A nonzero code represents an exception, in which case the stat parameter will be null. The path and ctx arguments correspond to the equivalent arguments passed by the client to the exists() method, and can be used to identify the request for which this callback is a response. The ctx parameter can be an arbitrary object that may be used by the client when the path does not give enough context to disambiguate the request. If not needed, it may be set to null.
There are actually two C shared libraries. The single-threaded library, zookeeper_st, supports only the asynchronous API and is intended for platforms where the pthread library is not available or stable. Most developers will use the multithreaded library, zookeeper_mt, as it supports both the synchronous and asynchronous APIs. For details on how to build and use the C API, please refer to the README file in the src/c directory of the ZooKeeper distribution.
Should I Use the Synchronous or Asynchronous API?
Both APIs offer the same functionality, so the one you use is largely a matter of style. The asynchronous API is appropriate if you have an event-driven programming model, for example.
The asynchronous API allows you to pipeline requests, which in some scenarios can offer better throughput. Imagine that you want to read a large batch of znodes and process them independently. Using the synchronous API, each read would block until it returned, whereas with the asynchronous API, you can fire off all the asynchronous reads very quickly and process the responses in a separate thread as they come back.
The read operations exists, getChildren, and getData may have watches set on them, and the watches are triggered by write operations: create, delete, and setData. ACL operations do not participate in watches.
When a watch is triggered, a watch event is generated, and the watch event’s type depends both on the watch and the operation that triggered it:
The combinations are summarized in Table
A watch event includes the path of the znode that was involved in the event, so for NodeCreated and NodeDeleted events, you can tell which node was created or deleted simply by inspecting the path. To discover which children have changed after a Node ChildrenChanged event, you need to call getChildren again to retrieve the new list of children. Similarly, to discover the new data for a NodeDataChanged event, you need tocall getData. In both of these cases, the state of the znodes may have changed between receiving the watch event and performing the read operation, so you should bear this in mind when writing applications.
A znode is created with a list of ACLs, which determines who can perform certain operations on it.
ACLs depend on authentication, the process by which the client identifies itself to ZooKeeper. There are a few authentication schemes that ZooKeeper provides:
The client is identified by a username and password.
The client is identified by his hostname.
The client is identified by his IP address.
Clients may authenticate themselves after establishing a ZooKeeper session. Authentication is optional, although a znode’s ACL may require an authenticated client, in which case the client must authenticate itself to access the znode. Here is an example of using the digest scheme to authenticate with a username and password:
An ACL is the combination of an authentication scheme, an identity for that scheme, and a set of permissions. For example, if we wanted to give clients in the domain example.com read access to a znode, we would set an ACL on the znode with the host scheme, an ID of example.com, and READ permission. In Java, we would create the ACL object as follows:
The full set of permissions are listed in Table 14-3. Note that the exists operation is not governed by an ACL permission, so any client may call exists to find the Stat for a znode or to discover that a znode does not in fact exist.
Table . ACL permissions
There are a number of predefined ACLs defined in the ZooDefs.Ids class, including OPEN_ACL_UNSAFE, which gives all permissions (except ADMIN permission) to everyone. In addition, ZooKeeper has a pluggable authentication mechanism, which makes it possible to integrate third-party authentication systems if needed.
The ZooKeeper service can run in two modes. In standalone mode, there is a single ZooKeeper server, which is useful for testing due to its simplicity (it can even be embedded in unit tests), but provides no guarantees of high-availability or resilience.
In production, ZooKeeper runs in replicated mode, on a cluster of machines called an ensemble. ZooKeeper achieves high-availability through replication, and can provide a service as long as a majority of the machines in the ensemble are up. For example, in a five-node ensemble, any two machines can fail and the service will still work because a majority of three remain. Note that a six-node ensemble can also tolerate only two machines failing, since with three failures the remaining three do not constitute a majority of the six. For this reason, it is usual to have an odd number of machines in an ensemble.
Conceptually, ZooKeeper is very simple: all it has to do is ensure that every modification to the tree of znodes is replicated to a majority of the ensemble. If a minority of the machines fail, then a minimum of one machine will survive with the latest state. The other remaining replicas will eventually catch up with this state.
The implementation of this simple idea, however, is nontrivial. ZooKeeper uses a protocol called Zab that runs in two phases, which may be repeated indefinitely:
Phase 1: Leader election
The machines in an ensemble go through a process of electing a distinguished member, called the leader. The other machines are termed followers. This phase is finished once a majority (or quorum) of followers have synchronized their state with the leader.
Phase 2: Atomic broadcast
All write requests are forwarded to the leader, which broadcasts the update to the followers. When a majority have persisted the change, the leader commits the update, and the client gets a response saying the update succeeded. The protocol for achieving consensus is designed to be atomic, so a change either succeeds or fails.It resembles a two-phase commit.
Does ZooKeeper Use Paxos?
No. ZooKeeper’s Zab protocol is not the same as the well-known Paxos algorithm (Leslie Lamport, “Paxos Made Simple,” ACM SIGACT News [Distributed Computing Column] 32, 4 [Whole Number 121, December 2001] 51–58.). Zab is similar, but it differs in several aspects of its operation, such as relying on TCP for its message ordering guarantees.
Zab is described in “A simple totally ordered broadcast protocol” by Benjamin Reed and Flavio Junqueira (LADIS ’08: Proceedings of the 2nd Workshop on Large-Scale Distributed Systems and Middleware, pages 1–6, New York, NY, USA, 2008. ACM). Google’s Chubby Lock Service (Mike Burrows, “The Chubby Lock Service for Loosely- Coupled Distributed Systems,” November 2006, which shares similar goals with ZooKeeper, is based on Paxos.
If the leader fails, the remaining machines hold another leader election and continue as before with the new leader. If the old leader later recovers, it then starts as a follower. Leader election is very fast, around 200 ms according to one published result, so performance does not noticeably degrade during an election.
All machines in the ensemble write updates to disk before updating their in-memory copy of the znode tree. Read requests may be serviced from any machine, and since they involve only a lookup from memory, they are very fast.
Understanding the basis of ZooKeeper’s implementation helps in understanding the consistency guarantees that the service makes. The terms “leader” and “follower” for the machines in an ensemble are apt, for they make the point that a follower may lag the leader by a number of updates. This is a consequence of the fact that only a majority and not all of the ensemble needs to have persisted a change before it is committed. Agood mental model for ZooKeeper is of clients connected to ZooKeeper servers that are following the leader. A client may actually be connected to the leader, but it has no control over this, and cannot even know if this is the case.* See Figure
Every update made to the znode tree is given a globally unique identifier, called a zxid (which stands for “ZooKeeper transaction ID”). Updates are ordered, so if zxid z1 is less than z2, then z1 happened before z2, according to ZooKeeper, which is the single authority on ordering in the distributed system.Reported by Yahoo! at .
It is possible to configure ZooKeeper so that the leader does not accept client connections. In this case, its only job is to coordinate updates. Do this by setting the leaderServes property to no. This is recommendedfor ensembles of more than three servers.
The following guarantees for data consistency flow from ZooKeeper’s design:
Updates from any particular client are applied in the order that they are sent. This means that if a client updates the znode z to the value a, and in a later operation, it updates z to the value b, then no client will ever see z with value a after it has seen it with value b (if no other updates are made to z).
Updates either succeed or fail. This means that if an update fails, no client will ever see it.
Single system image
A client will see the same view of the system regardless of the server it connects to. This means that if a client connects to a new server during the same session, it will not see an older state of the system than the one it saw with the previous server. When a server fails and a client tries to connect to another in the ensemble, a server that is behind the one that failed will not accept connections from the client until it has caught up with the failed server.
Once an update has succeeded, it will persist and will not be undone. This means updates will survive server failures.
The lag in any client’s view of the system is bounded, so it will not be out of date by more than some multiple of tens of seconds. This means that rather than allowa client to see data that is very stale, a server will shut down, forcing the client to switch to a more up-to-date server.
For performance reasons, reads are satisfied from a ZooKeeper’s server’s memory and do not participate in the global ordering of writes. This property can lead to the appearance of inconsistent ZooKeeper states from clients that communicate through a mechanism outside ZooKeeper.
For example, client A updates znode z from a to a’, A tells B to read z, B reads the value of z as a, not a’. This is perfectly compatible with the guarantees that ZooKeeper makes (this condition that it does not promise is called “Simultaneously Consistent Cross- Client Views”). To prevent this condition from happening, B should call sync on z, before reading z’s value. The sync operation forces the ZooKeeper server to which B isconnected to “catch up” with the leader, so that when B reads z’s value it will be the one that A set (or a later value).
Slightly confusingly, the sync operation is only available as an asynchronous call. The reason for this is that you don’t need to wait for it to return, since ZooKeeper guarantees that any subsequent operation willhappen after the sync completes on the server, even if the operation is issued before the sync completes
A ZooKeeper client is configured with the list of servers in the ensemble. On startup, it tries to connect to one of the servers in the list. If the connection fails, it tries another server in the list, and so on, until it either successfully connects to one of them or fails if all ZooKeeper servers are unavailable.
Once a connection has been made with a ZooKeeper server, the server creates a new session for the client. A session has a timeout period that is decided on by the application that creates it. If the server hasn’t received a request within the timeout period, it may expire the session. Once a session has expired, it may not be reopened, and any ephemeral nodes associated with the session will be lost. Although session expiry is a comparatively rare event, since sessions are long-lived, it is important for applications to handle it (we will see how in “The Resilient ZooKeeper Application” ).
Sessions are kept alive by the client sending ping requests (also known as heartbeats) whenever the session is idle for longer than a certain period. (Pings are automatically sent by the ZooKeeper client library, so your code doesn’t need to worry about maintaining the session.) The period is chosen to be low enough to detect server failure (manifested by a read timeout) and reconnect to another server within the session timeout period.
Failover to another ZooKeeper server is handled automatically by the ZooKeeper client, and, crucially, sessions (and associated ephemeral znodes) are still valid after another server takes over from the failed one.
During failover, the application will receive notifications of disconnections and connections to the service. Watch notifications will not be delivered while the client is disconnected, but they will be delivered when the client successfully reconnects. Also, if the application tries to perform an operation while the client is reconnecting to another server, the operation will fail. This underlines the importance of handling connectionloss exceptions in real-world ZooKeeper applications (described in “The Resilient ZooKeeper Application”).
There are several time parameters in ZooKeeper. The tick time is the fundamental period of time in ZooKeeper and is used by servers in the ensemble to define the schedule on which their interactions run. Other settings are defined in terms of tick time, or are at least constrained by it. The session timeout, for example, may not be less than 2 ticks or more than 20. If you attempt to set a session timeout outside this range, it will be modified to fall within the range.
A common tick time setting is 2 seconds (2,000 milliseconds). This translates to an allowable session timeout of between 4 and 40 seconds. There are a few considerations in selecting a session timeout.
A low session timeout leads to faster detection of machine failure. In the group membership example, the session timeout is the time it takes for a failed machine to be removed from the group. Beware of setting the session timeout too low, however, since a busy network can cause packets to be delayed and may cause inadvertent session expiry. In such an event, a machine would appear to “flap”: leaving and then rejoiningthe group repeatedly in a short space of time. Applications that create more complex ephemeral state should favor longer session timeouts, as the cost of reconstruction is higher. In some cases, it is possible to designthe application so it can restart within the session timeout period and avoid session expiry. (This might be desirable to perform maintenance or upgrades.) Every session is given a unique identity and password by the server, and if these are passed to Zoo- Keeper while a connection is being made, it is possible to recover a session (as long as it hasn’t expired). An application can therefore arrange a graceful shutdown, wherebyit stores the session identity and password to stable storage before restarting the process, retrieving the stored session identity and password and recovering the session.
You should view this feature as an optimization, which can help avoid expire sessions. It does not remove the need to handle session expiry, which can still occur if a machine fails unexpectedly, or even if an application is shut down gracefully but does not restart before its session expires for whatever reason.
As a general rule, the larger the ZooKeeper ensemble, the larger the session timeout should be. Connection timeouts, read timeouts, and ping periods are all defined internally as a function of the number of servers in the ensemble, so as the ensemble grows, these periods decrease. Consider increasing the timeout if you experience frequent connection loss. You can monitor ZooKeeper metrics such as request latency statistics using JMX.
The ZooKeeper object transitions through different states in its lifecycle (see Figure ). You can query its state at any time by using the getState() method:
public States getState()
States is an enum representing the different states that a ZooKeeper object may be in. (Despite the enum’s name, an instance of ZooKeeper may only be in one state at a time.) A newly constructed ZooKeeper instance is in the CONNECTING state, while it tries to establish a connection with the ZooKeeper service. Once a connection is established, it goes into the CONNECTED state
A client using the ZooKeeper object can receive notifications of the state transitions by registering a Watcher object. On entering the CONNECTED state, the watcher receives a WatchedEvent whose KeeperState value is SyncConnected.
A ZooKeeper Watcher object serves double duty: it can be used to be notified of changes in the ZooKeeper state (as described in this section), and it can be used to be notified of changes in znodes (described in“Watch triggers” ). The (default) watcher passed into the ZooKeeper object constructor is used for state changes, but znode changes may either use a dedicated instance of Watcher (by passing one in to the appropriate read operation), or they may share the default one if using the form of the read operation that takes a boolean flag to specify whether to use a watcher.
The ZooKeeper instance may disconnect and reconnect to the ZooKeeper service, moving between the CONNECTED and CONNECTING states. If it disconnects, the watcher receives a Disconnected event. Note that these state transitions are initiated by the ZooKeeper instance itself, and it will automatically try to reconnect if the connection is lost.
The ZooKeeper instance may transition to a third state, CLOSED, if either the close() method is called or the session times out as indicated by a KeeperState of type Expired. Once in the CLOSED state, the ZooKeeper object is no longer considered to be alive (this can be tested using the isAlive() method on States) and cannot be reused.
To reconnect to the ZooKeeper service, the client must construct a new ZooKeeper instance.
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