The name "etcd" originated from two ideas, the unix "/etc" folder and "d"istibuted systems. The "/etc" folder is a place to store configuration data for a single system whereas etcd stores configuration information for large scale distributed systems. Hence, a "d"istributed "/etc" is "etcd".
etcd stores metadata in a consistent and fault-tolerant way. Distributed systems use etcd as a consistent key-value store for configuration management, service discovery, and coordinating distributed work. Common distributed patterns using etcd include leader election, distributed locks, and monitoring machine liveness.
When deciding whether to use etcd as a key-value store, it’s worth keeping in mind etcd’s main goal. Namely, etcd is designed as a general substrate for large scale distributed systems. These are systems that will never tolerate split-brain operation and are willing to sacrifice availability to achieve this end. An etcd cluster is meant to provide consistent key-value storage with best of class stability, reliability, scalability and performance. The upshot of this focus is many organizations already use etcd to implement production systems such as container schedulers, service discovery services, distributed data storage, and more.
Perhaps etcd already seems like a good fit, but as with all technological decisions, proceed with caution. Please note this documentation is written by the etcd team. Although the ideal is a disinterested comparison of technology and features, the authors’ expertise and biases obviously favor etcd. Use only as directed.
The table below is a handy quick reference for spotting the differences among etcd and its most popular alternatives at a glance. Further commentary and details for each column are in the sections following the table.
|etcd||ZooKeeper||Consul||NewSQL (Cloud Spanner, CockroachDB, TiDB)|
|Concurrency Primitives||Lock RPCs, Election RPCs, command line locks, command line elections, recipes in go||External curator recipes in Java||Native lock API||Rare, if any|
|Multi-version Concurrency Control||Yes||No||No||Sometimes|
|Transactions||Field compares, Read, Write||Version checks, Write||Field compare, Lock, Read, Write||SQL-style|
|Change Notification||Historical and current key intervals||Current keys and directories||Current keys and prefixes||Triggers (sometimes)|
|User permissions||Role based||ACLs||ACLs||Varies (per-table GRANT, per-database roles)|
|Maximum reliable database size||Several gigabytes||Hundreds of megabytes (sometimes several gigabytes)||Hundreds of MBs||Terabytes+|
|Minimum read linearization latency||Network RTT||No read linearization||RTT + fsync||Clock barriers (atomic, NTP)|
ZooKeeper solves the same problem as etcd: distributed system coordination and metadata storage. However, etcd has the luxury of hindsight taken from engineering and operational experience with ZooKeeper’s design and implementation. The lessons learned from Zookeeper certainly informed etcd’s design, helping it support large scale systems like Kubernetes. The improvements etcd made over Zookeeper include:
Furthermore, etcd supports a wide range of languages and frameworks out of the box. Whereas Zookeeper has its own custom Jute RPC protocol, which is totally unique to Zookeeper and limits its supported language bindings, etcd’s client protocol is built from gRPC, a popular RPC framework with language bindings for go, C++, Java, and more. Likewise, gRPC can be serialized into JSON over HTTP, so even general command line utilities like
curl can talk to it. Since systems can select from a variety of choices, they are built on etcd with native tooling rather than around etcd with a single fixed set of technologies.
When considering features, support, and stability, new applications planning to use Zookeeper for a consistent key value store would do well to choose etcd instead.
Consul bills itself as an end-to-end service discovery framework. To wit, it includes services such as health checking, failure detection, and DNS. Incidentally, Consul also exposes a key value store with mediocre performance and an intricate API. As it stands in Consul 0.7, the storage system does not scales well; systems requiring millions of keys will suffer from high latencies and memory pressure. The key value API is missing, most notably, multi-version keys, conditional transactions, and reliable streaming watches.
etcd and Consul solve different problems. If looking for a distributed consistent key value store, etcd is a better choice over Consul. If looking for end-to-end cluster service discovery, etcd will not have enough features; choose Kubernetes, Consul, or SmartStack.
Both etcd and NewSQL databases (e.g., Cockroach, TiDB, Google Spanner) provide strong data consistency guarantees with high availability. However, the significantly different system design parameters lead to significantly different client APIs and performance characteristics.
NewSQL databases are meant to horizontally scale across data centers. These systems typically partition data across multiple consistent replication groups (shards), potentially distant, storing data sets on the order of terabytes and above. This sort of scaling makes them poor candidates for distributed coordination as they have long latencies from waiting on clocks and expect updates with mostly localized dependency graphs. The data is organized into tables, including SQL-style query facilities with richer semantics than etcd, but at the cost of additional complexity for processing, planning, and optimizing queries.
In short, choose etcd for storing metadata or coordinating distributed applications. If storing more than a few GB of data or if full SQL queries are needed, choose a NewSQL database.
etcd replicates all data within a single consistent replication group. For storing up to a few GB of data with consistent ordering, this is the most efficient approach. Each modification of cluster state, which may change multiple keys, is assigned a global unique ID, called a revision in etcd, from a monotonically increasing counter for reasoning over ordering. Since there’s only a single replication group, the modification request only needs to go through the raft protocol to commit. By limiting consensus to one replication group, etcd gets distributed consistency with a simple protocol while achieving low latency and high throughput.
The replication behind etcd cannot horizontally scale because it lacks data sharding. In contrast, NewSQL databases usually shard data across multiple consistent replication groups, storing data sets on the order of terabytes and above. However, to assign each modification a global unique and increasing ID, each request must go through an additional coordination protocol among replication groups. This extra coordination step may potentially conflict on the global ID, forcing ordered requests to retry. The result is a more complicated approach with typically worse performance than etcd for strict ordering.
If an application reasons primarily about metadata or metadata ordering, such as to coordinate processes, choose etcd. If the application needs a large data store spanning multiple data centers and does not heavily depend on strong global ordering properties, choose a NewSQL database.
etcd has distributed coordination primitives such as event watches, leases, elections, and distributed shared locks out of the box. These primitives are both maintained and supported by the etcd developers; leaving these primitives to external libraries shirks the responsibility of developing foundational distributed software, essentially leaving the system incomplete. NewSQL databases usually expect these distributed coordination primitives to be authored by third parties. Likewise, ZooKeeper famously has a separate and independent library of coordination recipes. Consul, which provides a native locking API, goes so far as to apologize that it’s “not a bulletproof method”.
In theory, it’s possible to build these primitives atop any storage systems providing strong consistency. However, the algorithms tend to be subtle; it is easy to develop a locking algorithm that appears to work, only to suddenly break due to thundering herd and timing skew. Furthermore, other primitives supported by etcd, such as transactional memory depend on etcd’s MVCC data model; simple strong consistency is not enough.
For distributed coordination, choosing etcd can help prevent operational headaches and save engineering effort.