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Membership and Coordination Services

Projects like ZooKeeper or etcd are often described as “distributed key-value stores” or “coordination and configuration services.” The API of such a service looks pretty much like that of a database: you can read and write the value for a given key, and iterate over keys. So if they’re basically databases, why do they go to all the effort of implementing a consensus algorithm? What makes them different from any other kind of database?

To understand this, it is helpful to briefly explore how a service like ZooKeeper is used. As an application developer, you will rarely need to use ZooKeeper directly, because it is actually not well suited as a general-purpose database. It is more likely that you will end up relying on it indirectly via some other project: for example, HBase, Hadoop YARN, OpenStack Nova, and Kafka all rely on ZooKeeper running in the background. What is it that these projects get from it?

ZooKeeper and etcd are designed to hold small amounts of data that can fit entirely in memory (although they still write to disk for durability)—so you wouldn’t want to store all of your application’s data here. That small amount of data is replicated across all the nodes using a fault-tolerant total order broadcast algorithm. As discussed previously, total order broadcast is just what you need for database replication: if each message represents a write to the database, applying the same writes in the same order keeps replicas consistent with each other.

ZooKeeper is modeled after Google’s Chubby lock service [14, 98], implementing not only total order broadcast (and hence consensus), but also an interesting set of other features that turn out to be particularly useful when building distributed systems:

Linearizable atomic operations

Using an atomic compare-and-set operation, you can implement a lock: if several nodes concurrently try to perform the same operation, only one of them will succeed. The consensus protocol guarantees that the operation will be atomic and linearizable, even if a node fails or the network is interrupted at any point. A distributed lock is usually implemented as a lease, which has an expiry time so that it is eventually released in case the client fails (see “Process Pauses” on page 295).

Total ordering of operations

As discussed in “The leader and the lock” on page 301, when some resource is protected by a lock or lease, you need a fencing token to prevent clients from conflicting with each other in the case of a process pause. The fencing token is some number that monotonically increases every time the lock is acquired. ZooKeeper provides this by totally ordering all operations and giving each operation a monotonically increasing transaction ID (zxid) and version number (eversion) [15].

Failure detection

Clients maintain a long-lived session on ZooKeeper servers, and the client and server periodically exchange heartbeats to check that the other node is still alive. Even if the connection is temporarily interrupted, or a ZooKeeper node fails, the session remains active. However, if the heartbeats cease for a duration that is longer than the session timeout, ZooKeeper declares the session to be dead. Any locks held by a session can be configured to be automatically released when the session times out (ZooKeeper calls these ephemeral nodes).

Change notifications

Not only can one client read locks and values that were created by another client, but it can also watch them for changes. Thus, a client can find out when another client joins the cluster (based on the value it writes to ZooKeeper), or if another client fails (because its session times out and its ephemeral nodes disappear). By subscribing to notifications, a client avoids having to frequently poll to find out about changes.

Of these features, only the linearizable atomic operations really require consensus. However, it is the combination of these features that makes systems like ZooKeeper so useful for distributed coordination.

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