45 lines
2.3 KiB
Markdown
45 lines
2.3 KiB
Markdown
---
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layout: "intro"
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page_title: "Serf vs. ZooKeeper, doozerd, etcd"
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sidebar_current: "vs-other-zk"
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---
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# Serf vs. ZooKeeper, doozerd, etcd
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ZooKeeper, doozerd and etcd are all similar in their client/server
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architecture. All three have server nodes that require a quorum of
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nodes to operate (usually a simple majority). They are strongly consistent,
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and expose various primitives that can be used through client libraries within
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applications to build complex distributed systems.
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Serf has a radically different architecture based on gossip and provides a
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smaller feature set. Serf only provides membership, failure detection,
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and user events. Serf is designed to operate under network partitions
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and embraces eventual consistency. Designed as a tool, it is friendly
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for both system administrators and application developers.
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ZooKeeper et al. by contrast are much more complex, and cannot be used directly
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as a tool. Application developers must use libraries to build the features
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they need, although some libraries exist for common patterns. Most failure
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detection schemes built on these systems also have intrinsic scalability issues.
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Most naive failure detection schemes depend on heartbeating, which use
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periodic updates and timeouts. These schemes require work linear to
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the number of nodes and place the demand on a fixed number of servers.
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Additionally, the failure detection window is at least as long as the timeout,
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meaning that in many cases failures may not be detected for a long time.
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Additionally, ZooKeeper ephemeral nodes require that many active connections
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be maintained to a few nodes.
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The strong consistency provided by these systems is essential for building leader
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election and other types of coordination for distributed systems, but it limits
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their ability to operate under network partitions. At a minimum, if a majority of
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nodes are not available, writes are disallowed. Since a failure is indistinguishable
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from a slow response, the performance of these systems may rapidly degrade
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under certain network conditions. All of these issues can be highly
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problematic when partition tolerance is needed, for example in a service
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discovery layer.
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Additionally, Serf is not mutually exclusive with any of these strongly
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consistent systems. Instead, they can be used in combination to create systems
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that are more scalable and fault tolerant, without sacrificing features.
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