Rapid is a distributed membership service. It allows a set of processes to easily form clusters and receive notifications when the membership changes.
We observe that datacenter failure scenarios are not always crash failures, but commonly involve misconfigured firewalls, one-way connectivity loss, flip-flops in reachability, and some-but-not-all packets being dropped. However, existing membership solutions struggle with these common failure scenarios, despite being able to cleanly detect crash faults. In particular, existing tools take long to, or never converge to, a stable state where the faulty processes are removed.
To address the above challenge, we present Rapid, a scalable, distributed membership system that is stable in the face of a diverse range of failure scenarios, and provides participating processes a strongly consistent view of the system's membership.
Rapid achieves its goals through the following three building blocks:
Expander-based monitoring edge overlay. To scale monitoring load, Rapid organizes a set of processes (a configuration) into a stable failure detection topology comprising observers that monitor and disseminate reports about their communication edges to their subjects. The monitoring relationships between processes forms a directed expander graph with strong connectivity properties, which ensures with a high probability that healthy processes detect failures. We interpret multiple reports about a subject's edges as a high-fidelity signal that the subject is faulty.
Multi-process cut detection. For stability, processes in Rapid (i) suspect a faulty process p only upon receiving alerts from multiple observers of p, and (ii) delay acting on alerts about different processes until the churn stabilizes, thereby converging to detect a global, possibly multi-node cut of processes to add or remove from the membership. This filter is remarkably simple to implement, yet it suffices by itself to achieve almost-everywhere agreement -- unanimity among a large fraction of processes about the detected cut.
Practical consensus. For consistency, we show that converting almost-everywhere agreement into full agreement is practical even in large-scale settings. Rapid's consensus protocol drives configuration changes by a low-overhead, leaderless protocol in the common case: every process simply validates consensus by counting the number of identical cut detections. If there is a quorum containing three-quarters of the membership set with the same cut, then without a leader or further communication, this is a safe consensus decision.
A powerful feature of Rapid is that it allows users to use custom failure
detectors. By design, users inform Rapid how an observer o can announce its
monitoring edge to a subject s as faulty by implementing a simple interface
IEdgeFailureDetectorFactory). Rapid builds the expander-based monitoring
overlay using the user-supplied template for a monitoring edge.
When embedding a membership service in a larger system, there is no reason
for the membership service to use its own messaging implementation. For
this reason, Rapid also allows users to plugin their own messaging
implementations by implementing two interfaces,
IMessagingServer. To see example usage, have a look at
We suggest you start with our USENIX ATC 2018 paper.
The paper and an accompanying tech report are both available in the
Clone this repository and install rapid into your local maven repository:
$: mvn install
If your project uses maven, add the following dependency into your project's pom.xml:
<dependency> <groupId>com.vrg</groupId> <artifactId>rapid</artifactId> <version>1.0-SNAPSHOT</version> </dependency>
For a simple example project that uses Rapid's APIs, see
For the following steps, ensure that you've built or installed Rapid:
$: mvn package # or mvn install
To launch a simple Rapid-based agent, run the following commands in your shell from the top-level directory:
$: java -jar examples/target/standalone-agent.jar \ --listenAddress 127.0.0.1:1234 \ --seedAddress 127.0.0.1:1234
From two other terminals, try adding a few more nodes on different listening addresses, but using the same seed address of "127.0.0.1:1234". For example:
$: java -jar examples/target/standalone-agent.jar \ --listenAddress 127.0.0.1:1235 \ --seedAddress 127.0.0.1:1234 $: java -jar examples/target/standalone-agent.jar \ --listenAddress 127.0.0.1:1236 \ --seedAddress 127.0.0.1:1234
Or use the following script to start multiple agents in the background that bootstrap via node 127.0.0.1:1234.
#! /bin/bash for each in `seq 1235 1245`; do java -jar examples/target/standalone-agent.jar \ --listenAddress 127.0.0.1:$each \ --seedAddress 127.0.0.1:1234 &> /tmp/rapid.$each & done
To run the
AgentWithNettyMessaging example, replace
in the above commands with