ZooKeeper: Waitfree coordination for Internetscale systems
Patrick Hunt and Mahadev Konar Yahoo! Grid phunt,mahadevyahooinc.com
Abstract
In this paper, we describe ZooKeeper, a service for co ordinating processes of distributed applications. Since ZooKeeper is part of critical infrastructure, ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client. It incorporates elements from group messaging, shared registers, and distributed lock services in a repli cated, centralized service. The interface exposed by Zoo Keeper has the waitfree aspects of shared registers with an eventdriven mechanism similar to cache invalidations of distributed file systems to provide a simple, yet pow erful coordination service.
The ZooKeeper interface enables a highperformance service implementation. In addition to the waitfree property, ZooKeeper provides a per client guarantee of FIFO execution of requests and linearizability for all re quests that change the ZooKeeper state. These design de cisions enable the implementation of a high performance processing pipeline with read requests being satisfied by local servers. We show for the target workloads, 2:1 to 100:1 read to write ratio, that ZooKeeper can handle tens to hundreds of thousands of transactions per second. This performance allows ZooKeeper to be used exten sively by client applications.
1 Introduction
Largescale distributed applications require different forms of coordination. Configuration is one of the most basic forms of coordination. In its simplest form, con figuration is just a list of operational parameters for the system processes, whereas more sophisticated systems have dynamic configuration parameters. Group member ship and leader election are also common in distributed systems: often processes need to know which other pro cesses are alive and what those processes are in charge of. Locks constitute a powerful coordination primitive
Flavio P. Junqueira and Benjamin Reed Yahoo! Research fpj,breedyahooinc.com
that implement mutually exclusive access to critical re sources.
One approach to coordination is to develop services for each of the different coordination needs. For exam ple, Amazon Simple Queue Service 3 focuses specif ically on queuing. Other services have been devel oped specifically for leader election 25 and configura tion 27. Services that implement more powerful prim itives can be used to implement less powerful ones. For example, Chubby 6 is a locking service with strong synchronization guarantees. Locks can then be used to implement leader election, group membership, etc.
When designing our coordination service, we moved away from implementing specific primitives on the server side, and instead we opted for exposing an API that enables application developers to implement their own primitives. Such a choice led to the implementa tion of a coordination kernel that enables new primitives without requiring changes to the service core. This ap proach enables multiple forms of coordination adapted to the requirements of applications, instead of constraining developers to a fixed set of primitives.
When designing the API of ZooKeeper, we moved away from blocking primitives, such as locks. Blocking primitives for a coordination service can cause, among other problems, slow or faulty clients to impact nega tively the performance of faster clients. The implemen tation of the service itself becomes more complicated if processing requests depends on responses and fail ure detection of other clients. Our system, Zookeeper, hence implements an API that manipulates simple wait free data objects organized hierarchically as in file sys tems. In fact, the ZooKeeper API resembles the one of any other file system, and looking at just the API signa tures, ZooKeeper seems to be Chubby without the lock methods, open, and close. Implementing waitfree data objects, however, differentiates ZooKeeper significantly from systems based on blocking primitives such as locks.
Although the waitfree property is important for per
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formance and fault tolerance, it is not sufficient for co ordination. We have also to provide order guarantees for operations. In particular, we have found that guarantee ing both FIFO client ordering of all operations and lin earizable writes enables an efficient implementation of the service and it is sufficient to implement coordination primitives of interest to our applications. In fact, we can implement consensus for any number of processes with our API, and according to the hierarchy of Herlihy, Zoo Keeper implements a universal object 14.
The ZooKeeper service comprises an ensemble of servers that use replication to achieve high availability and performance. Its high performance enables appli cations comprising a large number of processes to use such a coordination kernel to manage all aspects of co ordination. We were able to implement ZooKeeper us ing a simple pipelined architecture that allows us to have hundreds or thousands of requests outstanding while still achieving low latency. Such a pipeline naturally enables the execution of operations from a single client in FIFO order. Guaranteeing FIFO client order enables clients to submit operations asynchronously. With asynchronous operations, a client is able to have multiple outstanding operations at a time. This feature is desirable, for exam ple, when a new client becomes a leader and it has to ma nipulate metadata and update it accordingly. Without the possibility of multiple outstanding operations, the time of initialization can be of the order of seconds instead of subsecond.
To guarantee that update operations satisfy lineariz ability, we implement a leaderbased atomic broadcast protocol 23, called Zab 24. A typical workload of a ZooKeeper application, however, is dominated by read operations and it becomes desirable to scale read throughput. In ZooKeeper, servers process read opera tions locally, and we do not use Zab to totally order them.
Caching data on the client side is an important tech nique to increase the performance of reads. For example, it is useful for a process to cache the identifier of the current leader instead of probing ZooKeeper every time it needs to know the leader. ZooKeeper uses a watch mechanism to enable clients to cache data without man aging the client cache directly. With this mechanism, a client can watch for an update to a given data object, and receive a notification upon an update. Chubby man ages the client cache directly. It blocks updates to in validate the caches of all clients caching the data being changed. Under this design, if any of these clients is slow or faulty, the update is delayed. Chubby uses leases to prevent a faulty client from blocking the system indef initely. Leases, however, only bound the impact of slow or faulty clients, whereas ZooKeeper watches avoid the problem altogether.
In this paper we discuss our design and implementa
tion of ZooKeeper. With ZooKeeper, we are able to im plement all coordination primitives that our applications require, even though only writes are linearizable. To val idate our approach we show how we implement some coordination primitives with ZooKeeper.
To summarize, in this paper our main contributions are: Coordination kernel: We propose a waitfree coordi nation service with relaxed consistency guarantees for use in distributed systems. In particular, we de scribe our design and implementation of a coordi nation kernel, which we have used in many criti cal applications to implement various coordination
techniques.
Coordination recipes: We show how ZooKeeper can
be used to build higher level coordination primi tives, even blocking and strongly consistent primi tives, that are often used in distributed applications.
Experience with Coordination: We share some of the ways that we use ZooKeeper and evaluate its per formance.
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The ZooKeeper service
Clients submit requests to ZooKeeper through a client API using a ZooKeeper client library. In addition to ex posing the ZooKeeper service interface through the client API, the client library also manages the network connec tions between the client and ZooKeeper servers.
In this section, we first provide a highlevel view of the ZooKeeper service. We then discuss the API that clients use to interact with ZooKeeper.
Terminology. In this paper, we use client to denote a user of the ZooKeeper service, server to denote a process providing the ZooKeeper service, and znode to denote an inmemory data node in the ZooKeeper data, which is organized in a hierarchical namespace referred to as the data tree. We also use the terms update and write to refer to any operation that modifies the state of the data tree. Clients establish a session when they connect to ZooKeeper and obtain a session handle through which they issue requests.
2.1 Service overview
ZooKeeper provides to its clients the abstraction of a set of data nodes znodes, organized according to a hierar chical name space. The znodes in this hierarchy are data objects that clients manipulate through the ZooKeeper API. Hierarchical name spaces are commonly used in file systems. It is a desirable way of organizing data objects, since users are used to this abstraction and it enables bet ter organization of application metadata. To refer to a
given znode, we use the standard UNIX notation for file system paths. For example, we use ABC to denote the path to znode C, where C has B as its parent and B has A as its parent. All znodes store data, and all znodes, except for ephemeral znodes, can have children.
chical keys. The hierarchal namespace is useful for al locating subtrees for the namespace of different applica tions and for setting access rights to those subtrees. We also exploit the concept of directories on the client side to build higher level primitives as we will see in section 2.4.
Unlike files in file systems, znodes are not designed for general data storage. Instead, znodes map to abstrac tions of the client application, typically corresponding to metadata used for coordination purposes. To illus trate, in Figure 1 we have two subtrees, one for Applica tion 1 app1 and another for Application 2 app2. The subtree for Application 1 implements a simple group membership protocol: each client process pi creates a znode p i under app1, which persists as long as the process is running.
Although znodes have not been designed for general data storage, ZooKeeper does allow clients to store some information that can be used for metadata or configu ration in a distributed computation. For example, in a leaderbased application, it is useful for an application server that is just starting to learn which other server is currently the leader. To accomplish this goal, we can have the current leader write this information in a known location in the znode space. Znodes also have associated metadata with time stamps and version counters, which allow clients to track changes to znodes and execute con ditional updates based on the version of the znode.
Sessions. A client connects to ZooKeeper and initiates a session. Sessions have an associated timeout. Zoo Keeper considers a client faulty if it does not receive any thing from its session for more than that timeout. A ses sion ends when clients explicitly close a session handle or ZooKeeper detects that a clients is faulty. Within a ses sion, a client observes a succession of state changes that reflect the execution of its operations. Sessions enable a client to move transparently from one server to another within a ZooKeeper ensemble, and hence persist across ZooKeeper servers.
2.2 Client API
We present below a relevant subset of the ZooKeeper API, and discuss the semantics of each request. createpath, data, flags: Creates a znode
with path name path, stores data in it, and returns the name of the new znode. flags en ables a client to select the type of znode: regular, ephemeral, and set the sequential flag;
deletepath, version: Deletes the znode path if that znode is at the expected version; existspath, watch: Returns true if the znode
with path name path exists, and returns false oth erwise. The watch flag enables a client to set a
app1
app1p3
app2
app1p1
app1p2
Figure 1: Illustration of ZooKeeper hierarchical name space.
There are two types of znodes that a client can create: Regular: Clients manipulate regular znodes by creating
and deleting them explicitly;
Ephemeral: Clients create such znodes, and they ei
ther delete them explicitly, or let the system remove them automatically when the session that creates them terminates deliberately or due to a failure.
Additionally, when creating a new znode, a client can set a sequential flag. Nodes created with the sequen tial flag set have the value of a monotonically increas ing counter appended to its name. If n is the new znode and p is the parent znode, then the sequence value of n is never smaller than the value in the name of any other sequential znode ever created under p.
ZooKeeper implements watches to allow clients to receive timely notifications of changes without requir ing polling. When a client issues a read operation with a watch flag set, the operation completes as nor mal except that the server promises to notify the client when the information returned has changed. Watches are onetime triggers associated with a session; they are unregistered once triggered or the session closes. Watches indicate that a change has happened, but do not provide the change. For example, if a client is sues a getDatafoo, true before foo is changed twice, the client will get one watch event telling the client that data for foo has changed. Ses sion events, such as connection loss events, are also sent to watch callbacks so that clients know that watch events may be delayed.
Data model. The data model of ZooKeeper is essen tially a file system with a simplified API and only full data reads and writes, or a keyvalue table with hierar
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watch on the znode;
getDatapath, watch: Returns the data and
metadata, such as version information, associated with the znode. The watch flag works in the same way as it does for exists, except that Zoo Keeper does not set the watch if the znode does not exist;
setDatapath, data, version: Writes data to znode path if the version number is the current version of the znode;
getChildrenpath, watch: Returns the set of names of the children of a znode;
syncpath: Waits for all updates pending at the start of the operation to propagate to the server that the client is connected to. The path is currently ignored.
All methods have both a synchronous and an asyn chronous version available through the API. An applica tion uses the synchronous API when it needs to execute a single ZooKeeper operation and it has no concurrent tasks to execute, so it makes the necessary ZooKeeper call and blocks. The asynchronous API, however, en ables an application to have both multiple outstanding ZooKeeper operations and other tasks executed in par allel. The ZooKeeper client guarantees that the corre sponding callbacks for each operation are invoked in or der.
Note that ZooKeeper does not use handles to access znodes. Each request instead includes the full path of the znode being operated on. Not only does this choice simplifies the API no open or close methods, but it also eliminates extra state that the server would need to maintain.
Each of the update methods take an expected ver sion number, which enables the implementation of con ditional updates. If the actual version number of the zn ode does not match the expected version number the up date fails with an unexpected version error. If the version number is 1, it does not perform version checking.
2.3 ZooKeeper guarantees
ZooKeeper has two basic ordering guarantees: Linearizable writes: all requests that update the state of ZooKeeper are serializable and respect prece
dence;
FIFO client order: all requests from a given client are
executed in the order that they were sent by the
client.
Note that our definition of linearizability is different
from the one originally proposed by Herlihy 15, and we call it Alinearizability asynchronous linearizabil ity. In the original definition of linearizability by Her lihy, a client is only able to have one outstanding opera tion at a time a client is one thread. In ours, we allow a
client to have multiple outstanding operations, and con sequently we can choose to guarantee no specific order for outstanding operations of the same client or to guar antee FIFO order. We choose the latter for our property. It is important to observe that all results that hold for linearizable objects also hold for Alinearizable objects because a system that satisfies Alinearizability also sat isfies linearizability. Because only update requests are A linearizable, ZooKeeper processes read requests locally at each replica. This allows the service to scale linearly as servers are added to the system.
To see how these two guarantees interact, consider the following scenario. A system comprising a number of processes elects a leader to command worker processes. When a new leader takes charge of the system, it must change a large number of configuration parameters and notify the other processes once it finishes. We then have two important requirements:
As the new leader starts making changes, we do not want other processes to start using the configuration that is being changed;
If the new leader dies before the configuration has been fully updated, we do not want the processes to use this partial configuration.
Observe that distributed locks, such as the locks pro vided by Chubby, would help with the first requirement but are insufficient for the second. With ZooKeeper, the new leader can designate a path as the ready znode; other processes will only use the configuration when that znode exists. The new leader makes the configuration change by deleting ready, updating the various configu ration znodes, and creating ready. All of these changes can be pipelined and issued asynchronously to quickly update the configuration state. Although the latency of a change operation is of the order of 2 milliseconds, a new leader that must update 5000 different znodes will take 10 seconds if the requests are issued one after the other; by issuing the requests asynchronously the requests will take less than a second. Because of the ordering guaran tees, if a process sees the ready znode, it must also see all the configuration changes made by the new leader. If the new leader dies before the ready znode is created, the other processes know that the configuration has not been finalized and do not use it.
The above scheme still has a problem: what happens if a process sees that ready exists before the new leader starts to make a change and then starts reading the con figuration while the change is in progress. This problem is solved by the ordering guarantee for the notifications: if a client is watching for a change, the client will see the notification event before it sees the new state of the system after the change is made. Consequently, if the process that reads the ready znode requests to be notified of changes to that znode, it will see a notification inform
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ing the client of the change before it can read any of the new configuration.
Another problem can arise when clients have their own communication channels in addition to ZooKeeper. For example, consider two clients A and B that have a shared configuration in ZooKeeper and communicate through a shared communication channel. If A changes the shared configuration in ZooKeeper and tells B of the change through the shared communication channel, B would ex pect to see the change when it rereads the configuration. If Bs ZooKeeper replica is slightly behind As, it may not see the new configuration. Using the above guar antees B can make sure that it sees the most uptodate information by issuing a write before rereading the con figuration. To handle this scenario more efficiently Zoo Keeper provides the sync request: when followed by a read, constitutes a slow read. sync causes a server to apply all pending write requests before processing the read without the overhead of a full write. This primitive is similar in idea to the flush primitive of ISIS 5.
ZooKeeper also has the following two liveness and durability guarantees: if a majority of ZooKeeper servers are active and communicating the service will be avail able; and if the ZooKeeper service responds successfully to a change request, that change persists across any num ber of failures as long as a quorum of servers is eventu ally able to recover.
2.4 Examples of primitives
In this section, we show how to use the ZooKeeper API to implement more powerful primitives. The ZooKeeper service knows nothing about these more powerful primi tives since they are entirely implemented at the client us ing the ZooKeeper client API. Some common primitives such as group membership and configuration manage ment are also waitfree. For others, such as rendezvous, clients need to wait for an event. Even though ZooKeeper is waitfree, we can implement efficient blocking primi tives with ZooKeeper. ZooKeepers ordering guarantees allow efficient reasoning about system state, and watches allow for efficient waiting.
Configuration Management ZooKeeper can be used to implement dynamic configuration in a distributed ap plication. In its simplest form configuration is stored in a znode, zc. Processes start up with the full pathname of zc. Starting processes obtain their configuration by reading zc with the watch flag set to true. If the config uration in zc is ever updated, the processes are notified and read the new configuration, again setting the watch flag to true.
Note that in this scheme, as in most others that use watches, watches are used to make sure that a process has
the most recent information. For example, if a process watching zc is notified of a change to zc and before it can issue a read for zc there are three more changes to zc, the process does not receive three more notification events. This does not affect the behavior of the process, since those three events would have simply notified the process of something it already knows: the information it has for zc is stale.
Rendezvous Sometimes in distributed systems, it is not always clear a priori what the final system config uration will look like. For example, a client may want to start a master process and several worker processes, but the starting processes is done by a scheduler, so the client does not know ahead of time information such as ad dresses and ports that it can give the worker processes to connect to the master. We handle this scenario with Zoo Keeper using a rendezvous znode, zr, which is an node created by the client. The client passes the full pathname of zr as a startup parameter of the master and worker processes. When the master starts it fills in zr with in formation about addresses and ports it is using. When workers start, they read zr with watch set to true. If zr has not been filled in yet, the worker waits to be notified when zr is updated. If zr is an ephemeral node, master and worker processes can watch for zr to be deleted and clean themselves up when the client ends.
Group Membership We take advantage of ephemeral nodes to implement group membership. Specifically, we use the fact that ephemeral nodes allow us to see the state of the session that created the node. We start by designat ing a znode, zg to represent the group. When a process member of the group starts, it creates an ephemeral child znode under zg. If each process has a unique name or identifier, then that name is used as the name of the child znode; otherwise, the process creates the znode with the SEQUENTIAL flag to obtain a unique name assignment. Processes may put process information in the data of the child znode, addresses and ports used by the process, for example.
After the child znode is created under zg the process starts normally. It does not need to do anything else. If the process fails or ends, the znode that represents it un der zg is automatically removed.
Processes can obtain group information by simply list ing the children of zg. If a process wants to monitor changes in group membership, the process can set the watch flag to true and refresh the group information al ways setting the watch flag to true when change notifi cations are received.
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Simple Locks Although ZooKeeper is not a lock ser vice, it can be used to implement locks. Applications using ZooKeeper usually use synchronization primitives tailored to their needs, such as those shown above. Here we show how to implement locks with ZooKeeper to show that it can implement a wide variety of general syn chronization primitives.
The simplest lock implementation uses lock files. The lock is represented by a znode. To acquire a lock, a client tries to create the designated znode with the EPHEMERAL flag. If the create succeeds, the client holds the lock. Otherwise, the client can read the zn ode with the watch flag set to be notified if the current leader dies. A client releases the lock when it dies or ex plicitly deletes the znode. Other clients that are waiting for a lock try again to acquire a lock once they observe the znode being deleted.
While this simple locking protocol works, it does have some problems. First, it suffers from the herd effect. If there are many clients waiting to acquire a lock, they will all vie for the lock when it is released even though only one client can acquire the lock. Second, it only imple ments exclusive locking. The following two primitives show how both of these problems can be overcome.
Simple Locks without Herd Effect We define a lock znode l to implement such locks. Intuitively we line up all the clients requesting the lock and each client obtains the lock in order of request arrival. Thus, clients wishing to obtain the lock do the following:
Lock
1 ncreatellock, EPHEMERALSEQUENTIAL 2 CgetChildrenl, false
3 if n is lowest znode in C, exit
4 pznode in C ordered just before n
5 if existsp, true wait for watch event 6 goto 2
Unlock
1 deleten
The use of the SEQUENTIAL flag in line 1 of Lock orders the clients attempt to acquire the lock with re spect to all other attempts. If the clients znode has the lowest sequence number at line 3, the client holds the lock. Otherwise, the client waits for deletion of the zn ode that either has the lock or will receive the lock be fore this clients znode. By only watching the znode that precedes the clients znode, we avoid the herd effect by only waking up one process when a lock is released or a lock request is abandoned. Once the znode being watched by the client goes away, the client must check if it now holds the lock. The previous lock request may have been abandoned and there is a znode with a lower sequence number still waiting for or holding the lock.
Releasing a lock is as simple as deleting the zn ode n that represents the lock request. By using the
EPHEMERAL flag on creation, processes that crash will automatically cleanup any lock requests or release any locks that they may have.
In summary, this locking scheme has the following ad vantages:
1. The removal of a znode only causes one client to wake up, since each znode is watched by exactly one other client, so we do not have the herd effect;
2. There is no polling or timeouts;
3. Because of the way we have implemented locking,
we can see by browsing the ZooKeeper data the amount of lock contention, break locks, and debug locking problems.
ReadWrite Locks To implement readwrite locks we change the lock procedure slightly and have separate read lock and write lock procedures. The unlock pro cedure is the same as the global lock case.
Write Lock
1 ncreatelwrite, EPHEMERALSEQUENTIAL 2 CgetChildrenl, false
3 if n is lowest znode in C, exit
4 pznode in C ordered just before n
5 if existsp, true wait for event 6 goto 2
Read Lock
1 ncreatelread, EPHEMERALSEQUENTIAL
2 CgetChildrenl, false
3 if no write znodes lower than n in C, exit
4 pwrite znode in C ordered just before n
5 if existsp, true wait for event
6 goto 3
This lock procedure varies slightly from the previous
locks. Write locks differ only in naming. Since read locks may be shared, lines 3 and 4 vary slightly because only earlier write lock znodes prevent the client from ob taining a read lock. It may appear that we have a herd effect when there are several clients waiting for a read lock and get notified when the write znode with the lower sequence number is deleted; in fact, this is a de sired behavior, all those read clients should be released since they may now have the lock.
Double Barrier Double barriers enable clients to syn chronize the beginning and the end of a computation. When enough processes, defined by the barrier thresh old, have joined the barrier, processes start their compu tation and leave the barrier once they have finished. We represent a barrier in ZooKeeper with a znode, referred to as b. Every process p registers with bby creating a znode as a child of bon entry, and unregistersre moves the childwhen it is ready to leave. Processes can enter the barrier when the number of child znodes of b exceeds the barrier threshold. Processes can leave the barrier when all of the processes have removed their children. We use watches to efficiently wait for enter and
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exit conditions to be satisfied. To enter, processes watch for the existence of a ready child of b that will be cre ated by the process that causes the number of children to exceed the barrier threshold. To leave, processes watch for a particular child to disappear and only check the exit condition once that znode has been removed.
3 ZooKeeper Applications
We now describe some applications that use ZooKeeper, and explain briefly how they use it. We show the primi tives of each example in bold.
The Fetching Service Crawling is an important part of a search engine, and Yahoo! crawls billions of Web doc uments. The Fetching Service FS is part of the Yahoo! crawler and it is currently in production. Essentially, it has master processes that command pagefetching pro cesses. The master provides the fetchers with configura tion, and the fetchers write back informing of their status and health. The main advantages of using ZooKeeper for FS are recovering from failures of masters, guaran teeing availability despite failures, and decoupling the clients from the servers, allowing them to direct their re quest to healthy servers by just reading their status from ZooKeeper. Thus, FS uses ZooKeeper mainly to man age configuration metadata, although it also uses Zoo Keeper to elect masters leader election.
Katta Katta 17 is a distributed indexer that uses Zoo Keeper for coordination, and it is an example of a non Yahoo! application. Katta divides the work of indexing using shards. A master server assigns shards to slaves and tracks progress. Slaves can fail, so the master must redistribute load as slaves come and go. The master can also fail, so other servers must be ready to take over in case of failure. Katta uses ZooKeeper to track the status of slave servers and the master group membership, and to handle master failover leader election. Katta also uses ZooKeeper to track and propagate the assign ments of shards to slaves configuration management.
Yahoo! Message Broker Yahoo! Message Broker YMB is a distributed publishsubscribe system. The system manages thousands of topics that clients can pub lish messages to and receive messages from. The topics are distributed among a set of servers to provide scala bility. Each topic is replicated using a primarybackup scheme that ensures messages are replicated to two ma chines to ensure reliable message delivery. The servers that makeup YMB use a sharednothing distributed ar chitecture which makes coordination essential for correct operation. YMB uses ZooKeeper to manage the distribu tion of topics configuration metadata, deal with fail ures of machines in the system failure detection and group membership, and control system operation.
2000
1500
1000
500
read
write
broker domain
migrationprohibited
Number of operations
0
0h 6h 12h 18h 24h 30h 36h 42h 48h 54h 60h 66h
Time in seconds
Figure 2: Workload for one ZK server with the Fetching Service. Each point represents a onesecond sample.
Figure 2 shows the read and write traffic for a Zoo Keeper server used by FS through a period of three days. To generate this graph, we count the number of opera tions for every second during the period, and each point corresponds to the number of operations in that second. We observe that the read traffic is much higher compared to the write traffic. During periods in which the rate is higher than 1, 000 operations per second, the read:write ratio varies between 10:1 and 100:1. The read operations in this workload are getData, getChildren, and exists, in increasing order of prevalence.
Figure 3: The layout of Yahoo! Message Broker YMB structures in ZooKeeper
Figure 3 shows part of the znode data layout for YMB. Each broker domain has a znode called nodes that has an ephemeral znode for each of the active servers that compose the YMB service. Each YMB server creates an ephemeral znode under nodes with load and sta tus information providing both group membership and status information through ZooKeeper. Nodes such as shutdown and migration prohibited are mon itored by all of the servers that make up the service and allow centralized control of YMB. The topics direc tory has a child znode for each topic managed by YMB. These topic znodes have child znodes that indicate the
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shutdown
nodes
topics
topic
brokerdisabled
. topic
hostname hostname
load
of topics
..
hostname
topic
primary
hostname
backup
primary and backup server for each topic along with the subscribers of that topic. The primary and backup server znodes not only allow servers to discover the servers in charge of a topic, but they also manage leader election and server crashes.
message proposals consisting of state changes from the leader and agree upon state changes.
4.1 Request Processor
Since the messaging layer is atomic, we guarantee that the local replicas never diverge, although at any point in time some servers may have applied more transactions than others. Unlike the requests sent from clients, the transactions are idempotent. When the leader receives a write request, it calculates what the state of the sys tem will be when the write is applied and transforms it into a transaction that captures this new state. The fu ture state must be calculated because there may be out standing transactions that have not yet been applied to the database. For example, if a client does a conditional setData and the version number in the request matches the future version number of the znode being updated, the service generates a setDataTXN that contains the new data, the new version number, and updated time stamps. If an error occurs, such as mismatched version numbers or the znode to be updated does not exist, an errorTXN is generated instead.
4.2 Atomic Broadcast
All requests that update ZooKeeper state are forwarded to the leader. The leader executes the request and broadcasts the change to the ZooKeeper state through Zab 24, an atomic broadcast protocol. The server that receives the client request responds to the client when it delivers the corresponding state change. Zab uses by de fault simple majority quorums to decide on a proposal, so Zab and thus ZooKeeper can only work if a majority of servers are correct i.e., with 2f1 server we can tolerate f failures.
To achieve high throughput, ZooKeeper tries to keep the request processing pipeline full. It may have thou sands of requests in different parts of the processing pipeline. Because state changes depend on the appli cation of previous state changes, Zab provides stronger order guarantees than regular atomic broadcast. More specifically, Zab guarantees that changes broadcast by a leader are delivered in the order they were sent and all changes from previous leaders are delivered to an estab lished leader before it broadcasts its own changes.
There are a few implementation details that simplify our implementation and give us excellent performance. We use TCP for our transport so message order is main tained by the network, which allows us to simplify our implementation. We use the leader chosen by Zab as the ZooKeeper leader, so that the same process that cre ates transactions also proposes them. We use the log to keep track of proposals as the writeahead log for the in
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Figure 4: The components of the ZooKeeper service.
ZooKeeper Implementation
Write Request
Response
ZooKeeper Service
Request Processor
txn
Replicated Database
txn
Atomic Broadcast
ZooKeeper provides high availability by replicating the ZooKeeper data on each server that composes the ser vice. We assume that servers fail by crashing, and such faulty servers may later recover. Figure 4 shows the high level components of the ZooKeeper service. Upon re ceiving a request, a server prepares it for execution re quest processor. If such a request requires coordina tion among the servers write requests, then they use an agreement protocol an implementation of atomic broad cast, and finally servers commit changes to the Zoo Keeper database fully replicated across all servers of the ensemble. In the case of read requests, a server simply reads the state of the local database and generates a re sponse to the request.
The replicated database is an inmemory database con taining the entire data tree. Each znode in the tree stores a maximum of 1MB of data by default, but this maximum value is a configuration parameter that can be changed in specific cases. For recoverability, we efficiently log up dates to disk, and we force writes to be on the disk media before they are applied to the inmemory database. In fact, as Chubby 8, we keep a replay log a writeahead log, in our case of committed operations and generate periodic snapshots of the inmemory database.
Every ZooKeeper server services clients. Clients con nect to exactly one server to submit its requests. As we noted earlier, read requests are serviced from the local replica of each server database. Requests that change the state of the service, write requests, are processed by an agreement protocol.
As part of the agreement protocol write requests are forwarded to a single server, called the leader1. The rest of the ZooKeeper servers, called followers, receive
1Details of leaders and followers, as part of the agreement protocol, are out of the scope of this paper.
Read Request
8
memory database, so that we do not have to write mes sages twice to disk.
During normal operation Zab does deliver all mes sages in order and exactly once, but since Zab does not persistently record the id of every message delivered, Zab may redeliver a message during recovery. Because we use idempotent transactions, multiple delivery is ac ceptable as long as they are delivered in order. In fact, ZooKeeper requires Zab to redeliver at least all messages that were delivered after the start of the last snapshot.
4.3 Replicated Database
Each replica has a copy in memory of the ZooKeeper state. When a ZooKeeper server recovers from a crash, it needs to recover this internal state. Replaying all deliv ered messages to recover state would take prohibitively long after running the server for a while, so ZooKeeper uses periodic snapshots and only requires redelivery of messages since the start of the snapshot. We call Zoo Keeper snapshots fuzzy snapshots since we do not lock the ZooKeeper state to take the snapshot; instead, we do a depth first scan of the tree atomically reading each zn odes data and metadata and writing them to disk. Since the resulting fuzzy snapshot may have applied some sub set of the state changes delivered during the generation of the snapshot, the result may not correspond to the state of ZooKeeper at any point in time. However, since state changes are idempotent, we can apply them twice as long as we apply the state changes in order.
For example, assume that in a ZooKeeper data tree two nodes foo and goo have values f1 and g1 respec tively and both are at version 1 when the fuzzy snap shot begins, and the following stream of state changes arrivehavingtheformtransactionType, path, value, newversion:
SetDataTXN, foo, f2, 2 SetDataTXN, goo, g2, 2 SetDataTXN, foo, f3, 3
After processing these state changes, foo and goo have values f3 and g2 with versions 3 and 2 respec tively. However, the fuzzy snapshot may have recorded that foo and goo have values f3 and g1 with ver sions 3 and 1 respectively, which was not a valid state of the ZooKeeper data tree. If the server crashes and recovers with this snapshot and Zab redelivers the state changes, the resulting state corresponds to the state of the service before the crash.
4.4 ClientServer Interactions
When a server processes a write request, it also sends out and clears notifications relative to any watch that corre
sponds to that update. Servers process writes in order and do not process other writes or reads concurrently. This ensures strict succession of notifications. Note that servers handle notifications locally. Only the server that a client is connected to tracks and triggers notifications for that client.
Read requests are handled locally at each server. Each read request is processed and tagged with a zxid that cor responds to the last transaction seen by the server. This zxid defines the partial order of the read requests with re spect to the write requests. By processing reads locally, we obtain excellent read performance because it is just an inmemory operation on the local server, and there is no disk activity or agreement protocol to run. This design choice is key to achieving our goal of excellent perfor mance with readdominant workloads.
One drawback of using fast reads is not guaranteeing precedence order for read operations. That is, a read op eration may return a stale value, even though a more recent update to the same znode has been committed. Not all of our applications require precedence order, but for applications that do require it, we have implemented sync. This primitive executes asynchronously and is ordered by the leader after all pending writes to its lo cal replica. To guarantee that a given read operation re turns the latest updated value, a client calls sync fol lowed by the read operation. The FIFO order guarantee of client operations together with the global guarantee of sync enables the result of the read operation to reflect any changes that happened before the sync was issued. In our implementation, we do not need to atomically broadcast sync as we use a leaderbased algorithm, and we simply place the sync operation at the end of the queue of requests between the leader and the server ex ecuting the call to sync. In order for this to work, the follower must be sure that the leader is still the leader. If there are pending transactions that commit, then the server does not suspect the leader. If the pending queue is empty, the leader needs to issue a null transaction to commit and orders the sync after that transaction. This has the nice property that when the leader is under load, no extra broadcast traffic is generated. In our implemen tation, timeouts are set such that leaders realize they are not leaders before followers abandon them, so we do not issue the null transaction.
ZooKeeper servers process requests from clients in FIFO order. Responses include the zxid that the response is relative to. Even heartbeat messages during intervals of no activity include the last zxid seen by the server that the client is connected to. If the client connects to a new server, that new server ensures that its view of the Zoo Keeper data is at least as recent as the view of the client by checking the last zxid of the client against its last zxid. If the client has a more recent view than the server, the
9
server does not reestablish the session with the client un til the server has caught up. The client is guaranteed to be able to find another server that has a recent view of the system since the client only sees changes that have been replicated to a majority of the ZooKeeper servers. This behavior is important to guarantee durability.
To detect client session failures, ZooKeeper uses time outs. The leader determines that there has been a failure if no other server receives anything from a client ses sion within the session timeout. If the client sends re quests frequently enough, then there is no need to send any other message. Otherwise, the client sends heartbeat messages during periods of low activity. If the client cannot communicate with a server to send a request or heartbeat, it connects to a different ZooKeeper server to reestablish its session. To prevent the session from tim ing out, the ZooKeeper client library sends a heartbeat after the session has been idle for s3 ms and switch to a new server if it has not heard from a server for 2s3 ms, where s is the session timeout in milliseconds.
5 Evaluation
We performed all of our evaluation on a cluster of 50 servers. Each server has one Xeon dualcore 2.1GHz processor, 4GB of RAM, gigabit ethernet, and two SATA hard drives. We split the following discussion into two parts: throughput and latency of requests.
5.1 Throughput
To evaluate our system, we benchmark throughput when the system is saturated and the changes in throughput for various injected failures. We varied the number of servers that make up the ZooKeeper service, but always kept the number of clients the same. To simulate a large number of clients, we used 35 machines to simulate 250 simultaneous clients.
We have a Java implementation of the ZooKeeper server, and both Java and C clients2. For these experi ments, we used the Java server configured to log to one dedicated disk and take snapshots on another. Our bench mark client uses the asynchronous Java client API, and each client has at least 100 requests outstanding. Each request consists of a read or write of 1K of data. We do not show benchmarks for other operations since the performance of all the operations that modify state are approximately the same, and the performance of non state modifying operations, excluding sync, are approx imately the same. The performance of sync approxi mates that of a lightweight write, since the request must
2The implementation is publicly available at http:hadoop. apache.orgzookeeper.
go to the leader, but does not get broadcast. Clients send counts of the number of completed operations ev ery 300ms and we sample every 6s. To prevent memory overflows, servers throttle the number of concurrent re quests in the system. ZooKeeper uses request throttling to keep servers from being overwhelmed. For these ex periments, we configured the ZooKeeper servers to have a maximum of 2, 000 total requests in process.
90000 80000 70000 60000 50000 40000 30000 20000 10000
Throughput of saturated system
3 servers
5 servers
7 servers
00 20 40 60 80 100 Percentage of read requests
Figure 5: The throughput performance of a saturated sys tem as the ratio of reads to writes vary.
Table 1: The throughput performance of the extremes of a saturated system.
In Figure 5, we show throughput as we vary the ratio of read to write requests, and each curve corresponds to a different number of servers providing the ZooKeeper service. Table 1 shows the numbers at the extremes of the read loads. Read throughput is higher than write throughput because reads do not use atomic broadcast. The graph also shows that the number of servers also has a negative impact on the performance of the broadcast protocol. From these graphs, we observe that the number of servers in the system does not only impact the num ber of failures that the service can handle, but also the workload the service can handle. Note that the curve for three servers crosses the others around 60. This situ ation is not exclusive of the threeserver configuration, and happens for all configurations due to the parallelism local reads enable. It is not observable for other config urations in the figure, however, because we have capped the maximum yaxis throughput for readability.
There are two reasons for write requests taking longer than read requests. First, write requests must go through atomic broadcast, which requires some extra processing
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and adds latency to requests. The other reason for longer processing of write requests is that servers must ensure that transactions are logged to nonvolatile store before sending acknowledgments back to the leader. In prin ciple, this requirement is excessive, but for our produc tion systems we trade performance for reliability since ZooKeeper constitutes application ground truth. We use more servers to tolerate more faults. We increase write throughput by partitioning the ZooKeeper data into mul tiple ZooKeeper ensembles. This performance trade off between replication and partitioning has been previously observed by Gray et al. 12.
Atomic Broadcast Throughput
90000 80000 70000 60000 50000 40000 30000 20000 10000
Throughput of saturated system all requests to leader
0
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Figure 7: Average throughput of the atomic broadcast component in isolation. Error bars denote the minimum and maximum values.
versions all require CPU. The contention for CPU low ers ZooKeeper throughput to substantially less than the atomic broadcast component in isolation. Because Zoo Keeper is a critical production component, up to now our development focus for ZooKeeper has been correctness and robustness. There are plenty of opportunities for im proving performance significantly by eliminating things like extra copies, multiple serializations of the same ob ject, more efficient internal data structures, etc.
00 20 40 60 80 100 Percentage of read requests
Figure 6: Throughput of a saturated system, varying the ratio of reads to writes when all clients connect to the leader.
ZooKeeper is able to achieve such high throughput by distributing load across the servers that makeup the ser vice. We can distribute the load because of our relaxed consistency guarantees. Chubby clients instead direct all requests to the leader. Figure 6 shows what happens if we do not take advantage of this relaxation and forced the clients to only connect to the leader. As expected the throughput is much lower for readdominant workloads, but even for writedominant workloads the throughput is lower. The extra CPU and network load caused by ser vicing clients impacts the ability of the leader to coor dinate the broadcast of the proposals, which in turn ad versely impacts the overall write performance.
The atomic broadcast protocol does most of the work of the system and thus limits the performance of Zoo Keeper more than any other component. Figure 7 shows the throughput of the atomic broadcast component. To benchmark its performance we simulate clients by gen erating the transactions directly at the leader, so there is no client connections or client requests and replies. At maximum throughput the atomic broadcast component becomes CPU bound. In theory the performance of Fig ure 7 would match the performance of ZooKeeper with 100 writes. However, the ZooKeeper client commu nication, ACL checks, and request to transaction con
70000 60000 50000 40000 30000 20000 10000
Time series with failures
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00 50 100 150 200 250 300
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Figure 8: Throughput upon failures.
To show the behavior of the system over time as fail ures are injected we ran a ZooKeeper service made up of 5 machines. We ran the same saturation benchmark as before, but this time we kept the write percentage at a constant 30, which is a conservative ratio of our ex pected workloads. Periodically we killed some of the server processes. Figure 8 shows the system throughput as it changes over time. The events marked in the figure are the following:
1. Failure and recovery of a follower;
2. Failure and recovery of a different follower;
3. Failure of the leader;
4. Failure of two followers a, b in the first two marks,
and recovery at the third mark c;
5. Failure of the leader.
Throughput
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6. Recovery of the leader.
There are a few important observations from this graph. First, if followers fail and recover quickly, then ZooKeeper is able to sustain a high throughput despite the failure. The failure of a single follower does not pre vent servers from forming a quorum, and only reduces throughput roughly by the share of read requests that the server was processing before failing. Second, our leader election algorithm is able to recover fast enough to pre vent throughput from dropping substantially. In our ob servations, ZooKeeper takes less than 200ms to elect a new leader. Thus, although servers stop serving requests for a fraction of second, we do not observe a throughput of zero due to our sampling period, which is on the order of seconds. Third, even if followers take more time to re cover, ZooKeeper is able to raise throughput again once they start processing requests. One reason that we do not recover to the full throughput level after events 1, 2, and 4 is that the clients only switch followers when their connection to the follower is broken. Thus, after event 4 the clients do not redistribute themselves until the leader fails at events 3 and 5. In practice such imbalances work themselves out over time as clients come and go.
5.2 Latency of requests
To assess the latency of requests, we created a bench mark modeled after the Chubby benchmark 6. We cre ate a worker process that simply sends a create, waits for it to finish, sends an asynchronous delete of the new node, and then starts the next create. We vary the number of workers accordingly, and for each run, we have each worker create 50,000 nodes. We calculate the throughput by dividing the number of create requests completed by the total time it took for all the workers to complete.
Table 2: Create requests processed per second.
Table 2 show the results of our benchmark. The cre ate requests include 1K of data, rather than 5 bytes in the Chubby benchmark, to better coincide with our ex pected use. Even with these larger requests, the through put of ZooKeeper is more than 3 times higher than the published throughput of Chubby. The throughput of the single ZooKeeper worker benchmark indicates that the average request latency is 1.2ms for three servers and 1.4ms for 9 servers.
Table 3: Barrier experiment with time in seconds. Each point is the average of the time for each client to finish over five runs.
5.3 Performance of barriers
In this experiment, we execute a number of barriers se quentially to assess the performance of primitives imple mented with ZooKeeper. For a given number of barriers b, each client first enters all b barriers, and then it leaves all b barriers in succession. As we use the doublebarrier algorithm of Section 2.4, a client first waits for all other clients to execute the enter procedure before mov ing to next call similarly for leave.
We report the results of our experiments in Table 3. In this experiment, we have 50, 100, and 200 clients entering a number b of barriers in succession, b200, 400, 800, 1600. Although an application can have thousands of ZooKeeper clients, quite often a much smaller subset participates in each coordination oper ation as clients are often grouped according to the specifics of the application.
Two interesting observations from this experiment are that the time to process all barriers increase roughly lin early with the number of barriers, showing that concur rent access to the same part of the data tree did not pro duce any unexpected delay, and that latency increases proportionally to the number of clients. This is a con sequence of not saturating the ZooKeeper service. In fact, we observe that even with clients proceeding in lockstep, the throughput of barrier operations enter and leave is between 1,950 and 3,100 operations per second in all cases. In ZooKeeper operations, this corresponds to throughput values between 10,700 and 17,000 opera tions per second. As in our implementation we have a ratio of reads to writes of 4:1 80 of read operations, the throughput our benchmark code uses is much lower compared to the raw throughput ZooKeeper can achieve over 40,000 according to Figure 5. This is due to clients waiting on other clients.
6 Related work
ZooKeeper has the goal of providing a service that mit igates the problem of coordinating processes in dis tributed applications. To achieve this goal, its design uses ideas from previous coordination services, fault tolerant systems, distributed algorithms, and file systems.
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We are not the first to propose a system for the coor dination of distributed applications. Some early systems propose a distributed lock service for transactional ap plications 13, and for sharing information in clusters of computers 19. More recently, Chubby proposes a system to manage advisory locks for distributed appli cations 6. Chubby shares several of the goals of Zoo Keeper. It also has a filesystemlike interface, and it uses an agreement protocol to guarantee the consistency of the replicas. However, ZooKeeper is not a lock service. It can be used by clients to implement locks, but there are no lock operations in its API. Unlike Chubby, ZooKeeper allows clients to connect to any ZooKeeper server, not just the leader. ZooKeeper clients can use their local replicas to serve data and manage watches since its con sistency model is much more relaxed than Chubby. This enables ZooKeeper to provide higher performance than Chubby, allowing applications to make more extensive use of ZooKeeper.
There have been faulttolerant systems proposed in the literature with the goal of mitigating the problem of building faulttolerant distributed applications. One early system is ISIS 5. The ISIS system transforms abstract type specifications into faulttolerant distributed objects, thus making faulttolerance mechanisms transparent to users. Horus 30 and Ensemble 31 are systems that evolved from ISIS. ZooKeeper embraces the notion of virtual synchrony of ISIS. Finally, Totem guarantees total order of message delivery in an architecture that exploits hardware broadcasts of local area networks 22. Zoo Keeper works with a wide variety of network topologies which motivated us to rely on TCP connections between server processes and not assume any special topology or hardware features. We also do not expose any of the en semble communication used internally in ZooKeeper.
One important technique for building faulttolerant services is statemachine replication 26, and Paxos 20 is an algorithm that enables efficient implementations of replicated statemachines for asynchronous systems. We use an algorithm that shares some of the character istics of Paxos, but that combines transaction logging needed for consensus with writeahead logging needed for data tree recovery to enable an efficient implementa tion. There have been proposals of protocols for practical implementations of Byzantinetolerant replicated state machines 7, 10, 18, 1, 28. ZooKeeper does not assume that servers can be Byzantine, but we do employ mech anisms such as checksums and sanity checks to catch nonmalicious Byzantine faults. Clement et al. dis cuss an approach to make ZooKeeper fully Byzantine faulttolerant without modifying the current server code base 9. To date, we have not observed faults in produc tion that would have been prevented using a fully Byzan tine faulttolerant protocol. 29.
Boxwood 21 is a system that uses distributed lock servers. Boxwood provides higherlevel abstractions to applications, and it relies upon a distributed lock service based on Paxos. Like Boxwood, ZooKeeper is a com ponent used to build distributed systems. ZooKeeper, however, has highperformance requirements and is used more extensively in client applications. ZooKeeper ex poses lowerlevel primitives that applications use to im plement higherlevel primitives.
ZooKeeper resembles a small file system, but it only provides a small subset of the file system operations and adds functionality not present in most file systems such as ordering guarantees and conditional writes. Zoo Keeper watches, however, are similar in spirit to the cache callbacks of AFS 16.
Sinfonia 2 introduces minitransactions, a new paradigm for building scalable distributed systems. Sin fonia has been designed to store application data, whereas ZooKeeper stores application metadata. Zoo Keeper keeps its state fully replicated and in memory for high performance and consistent latency. Our use of file system like operations and ordering enables functionality similar to minitransactions. The znode is a convenient abstraction upon which we add watches, a functionality missing in Sinfonia. Dynamo 11 allows clients to get and put relatively small less than 1M amounts of data in a distributed keyvalue store. Unlike ZooKeeper, the key space in Dynamo is not hierarchal. Dynamo also does not provide strong durability and consistency guarantees for writes, but instead resolves conflicts on reads.
DepSpace 4 uses a tuple space to provide a Byzan tine faulttolerant service. Like ZooKeeper DepSpace uses a simple server interface to implement strong syn chronization primitives at the client. While DepSpaces performance is much lower than ZooKeeper, it provides stronger fault tolerance and confidentiality guarantees.
7 Conclusions
ZooKeeper takes a waitfree approach to the problem of coordinating processes in distributed systems, by expos ing waitfree objects to clients. We have found Zoo Keeper to be useful for several applications inside and outside Yahoo!. ZooKeeper achieves throughput val ues of hundreds of thousands of operations per second for readdominant workloads by using fast reads with watches, both of which served by local replicas. Al though our consistency guarantees for reads and watches appear to be weak, we have shown with our use cases that this combination allows us to implement efficient and sophisticated coordination protocols at the client even though reads are not precedenceordered and the imple mentation of data objects is waitfree. The waitfree property has proved to be essential for high performance.
13
Although we have described only a few applications, there are many others using ZooKeeper. We believe such a success is due to its simple interface and the powerful abstractions that one can implement through this inter face. Further, because of the highthroughput of Zoo Keeper, applications can make extensive use of it, not only coursegrained locking.
Acknowledgements
We would like to thank Andrew Kornev and Runping Qi for their contributions to ZooKeeper; Zeke Huang and Mark Marchukov for valuable feedback; Brian Cooper and Laurence Ramontianu for their early contributions to ZooKeeper; Brian Bershad and Geoff Voelker made important comments on the presentation.
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