Chapter 1. Introduction

In the past, each application was a single program running on a single computer with a single CPU. Today, things have changed. In the Big Data and Cloud Computing world, applications are made up of many independent programs running on an ever-changing set of computers.

Coordinating the actions of these independent programs is far more difficult than writing a single program to run on a single computer. It is easy for developers to get mired in coordination logic and lack the time to write their application logic properly—or perhaps the converse, to spend little time with the coordination logic and simply to write a quick-and-dirty master coordinator that is fragile and becomes an unreliable single point of failure.

ZooKeeper was designed to be a robust service that enables application developers to focus mainly on their application logic rather than coordination. It exposes a simple API, inspired by the file system API, that allows developers to implement common coordination tasks, such as electing a master server, managing group membership, and managing metadata. ZooKeeper is an application library with two principal implementations of the APIs—Java and C—and a service component implemented in Java that runs on an ensemble of dedicated servers. Having an ensemble of servers enables ZooKeeper to tolerate faults and scale throughput.

When designing an application with ZooKeeper, one ideally separates application data from control or coordination data. For example, the users of an email service are interested in their mailbox content, but are not concerned by which server is handling the requests of a particular mailbox. The mailbox content is application data, whereas the mapping of the mailbox to a specific mail server is part of the coordination data (or metadata). A ZooKeeper ensemble manages the latter.

The ZooKeeper Mission

Trying to explain what ZooKeeper does for us is like trying to explain what a screwdriver can do for us. In very basic terms, a screwdriver allows us to turn or drive screws, but putting it this way does not really express the power of the tool. It enables us to assemble pieces of furniture and electronic devices, and in some cases hang pictures on the wall. By providing some examples, we give some insight on what it can do for us, but it is certainly not exhaustive.

The argument for what a system like ZooKeeper can do for us is along the same lines: it enables coordination tasks for distributed systems. A coordination task is a task involving multiple processes. Such a task can be for the purposes of cooperation or to regulate contention. Cooperation means that processes need to do something together, and processes take action to enable other processes to make progress. For example, in typical master–worker architectures, the worker informs the master that it is available to do work. The master consequently assigns tasks to the worker. Contention is different: it refers to situations in which two processes cannot make progress concurrently, so one must wait for the other. Using the same master–worker example, we really want to have a single master, but multiple processes may try to become the master. The multiple processes consequently need to implement mutual exclusion. We can actually think of the task of acquiring mastership as the one of acquiring a lock: the process that acquires the mastership lock exercises the role of master.

If you have any experience with multithreaded programs, you will recognize that there are a lot of similar problems. In fact, having a number of processes running in the same computer or across computers is conceptually not different at all. Synchronization primitives that are useful in the context of multiple threads are also useful in the context of distributed systems. One important difference, however, stems from the fact that different computers do not share anything other than the network in a typical shared-nothing architecture. While there are a number of message-passing algorithms to implement synchronization primitives, it is typically much easier to rely upon a component that provides a shared store with some special ordering properties, like ZooKeeper does.

Coordination does not always take the form of synchronization primitives like leader election or locks. Configuration metadata is often used as a way for a process to convey what others should be doing. For example, in a master–worker system, workers need to know the tasks that have been assigned to them, and this information must be available even if the master crashes.

Let’s look at some examples where ZooKeeper has been useful to get a better sense of where it is applicable:

Apache HBase

HBase is a data store typically used alongside Hadoop. In HBase, ZooKeeper is used to elect a cluster master, to keep track of available servers, and to keep cluster metadata.

Apache Kafka

Kafka is a pub–sub messaging system. It uses ZooKeeper to detect crashes, to implement topic discovery, and to maintain production and consumption state for topics.

Apache Solr

Solr is an enterprise search platform. In its distributed form, called SolrCloud, it uses ZooKeeper to store metadata about the cluster and coordinate the updates to this metadata.

Yahoo! Fetching Service

Part of a crawler implementation, the Fetching Service fetches web pages efficiently by caching content while making sure that web server policies, such as those in robots.txt files, are preserved. This service uses ZooKeeper for tasks such as master election, crash detection, and metadata storage.

Facebook Messages

This is a Facebook application that integrates communication channels: email, SMS, Facebook Chat, and the existing Facebook Inbox. It uses ZooKeeper as a controller for implementing sharding and failover, and also for service discovery.

There are a lot more examples out there; this is a just a sample. Given this sample, let’s now bring the discussion to a more abstract level. When programming with ZooKeeper, developers design their applications as a set of clients that connect to ZooKeeper servers and invoke operations on them through the ZooKeeper client API. Among the strengths of the ZooKeeper API, it provides:

  • Strong consistency, ordering, and durability guarantees

  • The ability to implement typical synchronization primitives

  • A simpler way to deal with many aspects of concurrency that often lead to incorrect behavior in real distributed systems

ZooKeeper, however, is not magic; it will not solve all problems out of the box. It is important to understand what ZooKeeper provides and to be aware of its tricky aspects. One of the goals of this book is to discuss ways to deal with these issues. We cover the basic material needed to get the reader to understand what ZooKeeper actually does for developers. We additionally discuss several issues we have come across while implementing applications with ZooKeeper and helping developers new to ZooKeeper.

How the World Survived without ZooKeeper

Has ZooKeeper enabled a whole new class of applications to be developed? That doesn’t seem to be the case. ZooKeeper instead simplifies the development process, making it more agile and enabling more robust implementations.

Previous systems have implemented components like distributed lock managers or have used distributed databases for coordination. ZooKeeper, in fact, borrows a number of concepts from these prior systems. It does not expose a lock interface or a general-purpose interface for storing data, however. The design of ZooKeeper is specialized and very focused on coordination tasks. At the same time, it does not try to impose a particular set of synchronization primitives upon the developer, being very flexible with respect to what can be implemented.

It is certainly possible to build distributed systems without using ZooKeeper. ZooKeeper, however, offers developers the possibility of focusing more on application logic rather than on arcane distributed systems concepts. Programming distributed systems without ZooKeeper is possible, but more difficult.

What ZooKeeper Doesn’t Do

The ensemble of ZooKeeper servers manages critical application data related to coordination. ZooKeeper is not for bulk storage. For bulk storage of application data, there are a number of options available, such as databases and distributed file systems. When designing an application with ZooKeeper, one ideally separates application data from control or coordination data. They often have different requirements; for example, with respect to consistency and durability.

ZooKeeper implements a core set of operations that enable the implementation of tasks that are common to many distributed applications. How many applications do you know that have a master or need to track which processes are responsive? ZooKeeper, however, does not implement the tasks for you. It does not elect a master or track live processes for the application out of the box. Instead, it provides the tools for implementing such tasks. The developer decides what coordination tasks to implement.

The Apache Project

ZooKeeper is an open source project hosted by the Apache Software Foundation. It has a Project Management Committee (PMC) that is responsible for management and oversight of the project. Only committers can check in patches, but any developer can contribute a patch. Developers can become committers after contributing to the project. Contributions to the project are not limited to patches—they can come in other forms and interactions with other members of the community. We have lots of discussions on the mailing lists about new features, questions from new users, etc. We highly encourage developers interested in participating in the community to subscribe to the mailing lists and participate in the discussions. If you want to have a long-term relationship with ZooKeeper through some project, you may also find it worthwhile to become a committer.

Building Distributed Systems with ZooKeeper

There are multiple definitions of a distributed system, but for the purposes of this book, we define it as a system comprised of multiple software components running independently and concurrently across multiple physical machines. There are a number of reasons to design a system in a distributed manner. A distributed system is capable of exploiting the capacity of multiple processors by running components, perhaps replicated, in parallel. A system might be distributed geographically for strategic reasons, such as the presence of servers in multiple locations participating in a single application.

Having a separate coordination component has a couple of important advantages. First, it allows the component to be designed and implemented independently. Such an independent component can be shared across many applications. Second, it enables a system architect to reason more easily about the coordination aspect, which is not trivial (as this book tries to expose). Finally, it enables a system to run and manage the coordination component separately. Running such a component separately simplifies the task of solving issues in production.

Software components run in operating system processes, in many cases executing multiple threads. Thus, ZooKeeper servers and clients are processes. Often, a single physical server (whether a standalone machine or an operating system in a virtual environment) runs a single application process, although the process might execute multiple threads to exploit the multicore capacity of modern processors.

Processes in a distributed system have two broad options for communication: they can exchange messages directly through a network, or read and write to some shared storage. ZooKeeper uses the shared storage model to let applications implement coordination and synchronization primitives. But shared storage itself requires network communication between the processes and the storage. It is important to stress the role of network communication because it is an important source of complications in the design of a distributed system.

In real systems, it is important to watch out for the following issues:

Message delays

Messages can get arbitrarily delayed; for instance, due to network congestion. Such arbitrary delays may introduce undesirable situations. For example, process P may send a message before another process Q sends its message, according to a reference clock, but Q’s message might be delivered first.

Processor speed

Operating system scheduling and overload might induce arbitrary delays in message processing. When one process sends a message to another, the overall latency of this message is roughly the sum of the processing time on the sender, the transmission time, and the processing time on the receiver. If the sending or receiving process requires time to be scheduled for processing, then the message latency is higher.

Clock drift

It is not uncommon to find systems that use some notion of time, such as when determining the time at which events occur in the system. Processor clocks are not reliable and can arbitrarily drift away from each other. Consequently, relying upon processor clocks might lead to incorrect decisions.

One important consequence of these issues is that it is very hard in practice to tell if a process has crashed or if any of these factors is introducing some arbitrary delay. Not receiving a message from a process could mean that it has crashed, that the network is delaying its latest message arbitrarily, that there is something delaying the process, or that the process clock is drifting away. A system in which such a distinction can’t be made is said to be asynchronous.

Data centers are generally built using large batches of mostly uniform hardware. But even in data centers, we have observed the impact of all these issues on applications due to the use of multiple generations of hardware in a single application, and subtle but significant performance differences even within the same batch of hardware. All these things complicate the life of a distributed systems designer.

ZooKeeper has been designed precisely to make it simpler to deal with these issues. ZooKeeper does not make the problems disappear or render them completely transparent to applications, but it does make the problems more tractable. ZooKeeper implements solutions to important distributed computing problems and packages up these implementations in a way that is intuitive to developers… at least, this has been our hope all along.

Example: Master–Worker Application

We have talked about distributed systems in the abstract, but it is now time to make it a bit more concrete. Let’s consider a common architecture that has been used extensively in the design of distributed systems: a master–worker architecture (Figure 1-1). One important example of a system following this architecture is HBase, a clone of Google’s Bigtable. At a very high level, the master server (HMaster) is responsible for keeping track of the available region servers (HRegionServer) and assigning regions to servers. Because we don’t cover it here, we encourage you to check the HBase documentation for further details on how it uses ZooKeeper. Our discussion instead focuses on a generic master–worker architecture.

Master–worker example.
Figure 1-1. Master–worker example

In general, in such an architecture a master process is responsible for keeping track of the workers and tasks available, and for assigning tasks to workers. For ZooKeeper, this architecture style is representative because it illustrates a number of popular tasks, like electing a master, keeping track of available workers, and maintaining application metadata.

To implement a master–worker system, we must solve three key problems:

Master crashes

If the master is faulty and becomes unavailable, the system cannot allocate new tasks or reallocate tasks from workers that have also failed.

Worker crashes

If a worker crashes, the tasks assigned to it will not be completed.

Communication failures

If the master and a worker cannot exchange messages, the worker might not learn of new tasks assigned to it.

To deal with these problems, the system must be able to reliably elect a new master if the previous one is faulty, determine which workers are available, and decide when the state of a worker is stale with respect to the rest of the system. We’ll look at each task briefly in the following sections.

Master Failures

To mask master crashes, we need to have a backup master. When the primary master crashes, the backup master takes over the role of primary master. Failing over, however, is not as simple as starting to process requests that come in to the master. The new primary master must be able to recover the state of the system at the time the old primary master crashed. For recoverability of the master state, we can’t rely on pulling it from the faulty master because it has crashed; we need to have it somewhere else. This somewhere else is ZooKeeper.

Recovering the state is not the only important issue. Suppose that the primary master is up, but the backup master suspects that the primary master has crashed. This false suspicion could happen because, for example, the primary master is heavily loaded and its messages are being delayed arbitrarily (see the discussion in “Building Distributed Systems with ZooKeeper”). The backup master will execute all necessary procedures to take over the role of primary master and may eventually start executing the role of primary master, becoming a second primary master. Even worse, if some workers can’t communicate with the primary master, say because of a network partition, they may end up following the second primary master. This scenario leads to a problem commonly called split-brain: two or more parts of the system make progress independently, leading to inconsistent behavior. As part of coming up with a way to cope with master failures, it is critical that we avoid split-brain scenarios.

Worker Failures

Clients submit tasks to the master, which assigns the tasks to available workers. The workers receive assigned tasks and report the status of the execution once these tasks have been executed. The master next informs the clients of the results of the execution.

If a worker crashes, all tasks that were assigned to it and not completed must be reassigned. The first requirement here is to give the master the ability to detect worker crashes. The master must be able to detect when a worker crashes and must be able to determine what other workers are available to execute its tasks. If a worker crashes, it may end up partially executing tasks or even fully executing tasks but not reporting the results. If the computation has side effects, some recovery procedure might be necessary to clean up the state.

Communication Failures

If a worker becomes disconnected from the master, say due to a network partition, reassigning a task could lead to two workers executing the same task. If executing a task more than once is acceptable, we can reassign without verifying whether the first worker has executed the task. If it is not acceptable, then the application must be able to accommodate the possibility that multiple workers may end up trying to execute the task.

Another important issue with communication failures is the impact they have on synchronization primitives like locks. Because nodes can crash and systems are prone to network partitions, locks can be problematic: if a node crashes or gets partitioned away, the lock can prevent others from making progress. ZooKeeper consequently needs to implement mechanisms to deal with such scenarios. First, it enables clients to say that some data in the ZooKeeper state is ephemeral. Second, the ZooKeeper ensemble requires that clients periodically notify that they are alive. If a client fails to notify the ensemble in a timely manner, then all ephemeral state belonging to this client is deleted. Using these two mechanisms, we are able to prevent clients individually from bringing the application to a halt in the presence of crashes and communication failures.

Recall that we argued that in systems in which we cannot control the delay of messages it is not possible to tell if a client has crashed or if it is just slow. Consequently, when we suspect that a client has crashed, we actually need to react by assuming that it could just be slow, and that it may execute some other actions in the future.

Summary of Tasks

From the preceding descriptions, we can extract the following requirements for our master–worker architecture:

Master election

It is critical for progress to have a master available to assign tasks to workers.

Crash detection

The master must be able to detect when workers crash or disconnect.

Group membership management

The master must be able to figure out which workers are available to execute tasks.

Metadata management

The master and the workers must be able to store assignments and execution statuses in a reliable manner.

Ideally, each of these tasks is exposed to the application in the form of a primitive, hiding completely the implementation details from the application developer. ZooKeeper provides key mechanisms to implement such primitives so that developers can implement the ones that best suit their needs and focus on the application logic. Throughout this book, we often refer to implementations of tasks like master election or crash detection as primitives because these are concrete tasks that distributed applications build upon.

Why Is Distributed Coordination Hard?

Some of the complications of writing distributed applications are immediately apparent. For example, when our application starts up, somehow all of the different processes need to find the application configuration. Over time, this configuration may change. We could shut everything down, redistribute configuration files, and restart, but that may incur extended periods of application downtime during reconfiguration.

Related to the configuration problem is the problem of group membership. As the load changes, we want to be able to add or remove new machines and processes.

The problems just described are functional problems that you can design solutions for as you implement your distributed application; you can test your solutions before deployment and be pretty sure that you have solved the problems correctly. The truly difficult problems you will encounter as you develop distributed applications have to do with faults—specifically, crashes and communication faults. These failures can crop up at any point, and it may be impossible to enumerate all the different corner cases that need to be handled.

Byzantine Faults

Byzantine faults are faults that may cause a component to behave in some arbitrary (and often unanticipated) way. Such a faulty component might, for example, corrupt application state or even behave maliciously. Systems that are built under the assumption that these faults can occur require a higher degree of replication and the use of security primitives. Although we acknowledge that there have been significant advances in the development of techniques to tolerate Byzantine faults in the academic literature, we haven’t felt the need to adopt such techniques in ZooKeeper, and consequently we have avoided the additional complexity in the code base.

Failures also highlight a big difference between applications that run on a single machine and distributed applications: in distributed apps, partial failures can take place. When a single machine crashes, all the processes running on that machine fail. If there are multiple processes running on the machine and a process fails, the other processes can find out about the failure from the operating system. The operating system can also provide strong messaging guarantees between processes. All of this changes in a distributed environment: if a machine or process fails, other machines will keep running and may need to take over for the faulty processes. To handle faulty processes, the processes that are still running must be able to detect the failure; messages may be lost, and there may even be clock drift.

Ideally, we design our systems under the assumption that communication is asynchronous: the machines we use may experience clock drift and may experience communication failures. We make this assumption because these things do happen. Clocks drift all the time, we have all experienced occasional network problems, and unfortunately, failures also happen. What kinds of limits does this put on what we can do?

Well, let’s take the simplest case. Let’s assume that we have a distributed configuration that has been changing. This configuration is as simple as it can be: one bit. The processes in our application can start up once all running processes have agreed on the value of the configuration bit.

It turns out that a famous result in distributed computing, known as FLP after the authors Fischer, Lynch, and Patterson, proved that in a distributed system with asynchronous communication and process crashes, processes may not always agree on the one bit of configuration.1 A similar result known as CAP, which stands for Consistency, Availability, and Partition-tolerance, says that when designing a distributed system we may want all three of those properties, but that no system can handle all three.2 ZooKeeper has been designed with mostly consistency and availability in mind, although it also provides read-only capability in the presence of network partitions.

OK, so we cannot have an ideal fault-tolerant, distributed, real-world system that transparently takes care of all problems that might ever occur. We can strive for a slightly less ambitious goal, though. First, we have to relax some of our assumptions and/or our goals. For example, we may assume that the clock is synchronized within some bounds; we may choose to be always consistent and sacrifice the ability to tolerate some network partitions; there may be times when a process may be running, but must act as if it is faulty because it cannot be sure of the state of the system. While these are compromises, they are compromises that have allowed us to build some rather impressive distributed systems.

ZooKeeper Is a Success, with Caveats

Having pointed out that the perfect solution is impossible, we can repeat that ZooKeeper is not going to solve all the problems that the distributed application developer has to face. It does give the developer a nice framework to deal with these problems, though. There has been a lot of work over the years in distributed computing that ZooKeeper builds upon. Paxos3 and virtual synchrony4 have been particularly influential in the design of ZooKeeper. It deals with the changes and situations as they arise as seamlessly as possible, and gives developers a framework to deal with situations that arise that just cannot be handled automatically.

ZooKeeper was originally developed at Yahoo!, home to an abundance of large distributed applications. We noticed that the distributed coordination aspects of some applications were not treated appropriately, so systems were deployed with single points of failure or were brittle. On the other hand, other developers would spend so much time on the distributed coordination that they wouldn’t have enough resources to focus on the application functionality. We also noticed that these applications all had some basic coordination requirements in common, so we set out to devise a general solution that contained some key elements that we could implement once and use in many different applications. ZooKeeper has proven to be far more general and popular than we had ever thought possible.

Over the years, we have found that people can easily deploy a ZooKeeper cluster and develop applications for it—so easily, in fact, that some developers use it without completely understanding some of the cases that require the developer to make decisions that ZooKeeper cannot make by itself. One of the purposes of writing this book is to make sure that developers understand what they need to do to use ZooKeeper effectively and why they need to do it that way.

1 Michael J. Fischer, Nancy A. Lynch, and Michael S. Paterson, “Impossibility of Distributed Consensus with One Faulty Process,” Proceedings of the 2nd ACM SIGACT-SIGMOD Symposium on Principles of Database Systems (1983), doi:10.1145/588058.588060.

2 Seth Gilbert and Nancy Lynch, “Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services,” ACM SIGACT News 33:2 (2002), doi:10.1145/564585.564601.

3 Leslie Lamport, “The Part-Time Parliament,” ACM Transactions on Computer Systems 16:2 (1998): 133–169.

4 K. Birman and T. Joseph, “Exploiting Virtual Synchrony in Distributed Systems,” Proceedings of the 11th ACM Symposium on Operating Systems Principles (1987): 123–138.

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