MicroRaft is a feature-complete and stable open-source implementation of the Raft consensus algorithm in Java. It is released with the Apache 2 license.

MicroRaft works on top of a minimalistic and modular design. It is a single lightweight JAR with a few hundred KBs of size and only logging dependency. It contains an isolated implementation of the Raft consensus algorithm, and a set of accompanying interfaces to run the algorithm in a multi-threaded and distributed environment. These interfaces surround the Raft consensus algorithm, and abstract away the concerns of persistence, thread-safety, serialization, networking and actual state machine logic. Developers are required to implement these interfaces to build CP distributed systems on top of MicroRaft.

You can read the public announcement here.


MicroRaft is a complete implementation of the Raft consensus algorithm. It implements the leader election, log replication, log compaction (snapshotting), and cluster membership changes components. Additionally, it realizes a rich set of optimizations and enhancements, as listed below, to allow developers to run Raft clusters in a reliable and performant manner, and tune its behaviour based on their needs.

Use cases

MicroRaft can be used for building highly available and strongly consistent data, metadata and coordination services.

An example of data service is a distributed key-value store. You can build a distributed key-value store where each partition / shard is maintained by a separate Raft cluster (Raft group in MicroRaft terms).

MicroRaft can be also used for building a control plane or coordination cluster. It can store the metadata of your large-scale data services. High-level APIs, such as leader election mechanisms, group membership management systems, distributed locks, distributed transaction managers, or distributed resource schedulers can be also built on top of MicroRaft.

Please note that MicroRaft is not a high-level solution like a distributed key-value store, or a distributed lock service. It is a library that offers a set of abstractions and functionalities to help you build such high-level systems without intertwining your system with Raft code.

Get started

Just run the following command on your terminal for a sneak peek at MicroRaft. It starts a 3-node local Raft group, elects a leader, and commits a number of operations.

$ gh repo clone MicroRaft/MicroRaft && cd MicroRaft && ./mvnw clean test -Dtest=io.microraft.tutorial.OperationCommitTest -DfailIfNoTests=false -Ptutorial

If you want to learn more about how to use MicroRaft for building a CP distributed system, you can check out the Main Abstractions section first, and then read the tutorial to build an atomic register on top of MicroRaft.

Use MicroRaft in your project

Add MicroRaft to your dependency list:


Get involved

MicroRaft is a new open-source library. Your contribution and feedback is welcome! The development happens on Github. You can follow @MicroRaft on Twitter for announcements.

What is consensus?

Consensus is one of the fundamental problems in distributed systems. It involves multiple servers agree on a value. Once a value is decided, the decision is final. Consensus algorithms are very useful in a plethora of distributed systems that require high availability and strong consistency. Paxos, first introduced by Leslie Lamport, is probably the most widely known consensus algorithm. However, it has been also known as difficult to reason about and lacking details for building practical implementations. Raft was introduced in 2013 as a new consensus algorithm with the main goal of understandability. Ever since its introduction, Raft has received widespread adoption in the industry.

Raft approaches the consensus problem in the context of replicated state machines, where a group of servers applies the same set of operations and computes identical copies of the same state. Raft's primary enabler of understandability is the problem decomposition technique. It divides the consensus problem into 3 pieces: leader election, log replication and safety, and solves each piece relatively independently. Raft starts by electing a leader. There is a single functional leader managing the servers, and upon its failure a new leader is elected. Each server keeps a local log. Clients send their requests to the leader. The leader appends incoming requests into its log and replicates them to the other servers. Each server appends the requests sent by the leader into its log. Once a request is appended to the local logs of sufficient number (i.e., more than half) of servers, the leader considers the request committed, hence executes it on its local state machine, also notifies other servers to do the same. Raft orders requests by the indices they are appended to the replicated log. In addition, Raft's leader election and log replication rules ensure that once a request is committed and executed at a given log index on one server, no other server can execute another request for the same log index, including the presence of non-Byzantine failures. This is basically Raft's safety property. Thanks to this property, each server executes the same sequence of requests. Once these requests are deterministic, servers compute identical copies of the same state and produce the same output values.

For more details about Raft, please see the In Search of an Understandable Consensus Algorithm paper by Diego Ongaro and John Ousterhout.


MicroRaft originates from Hazelcast IMDG's Raft implementation and includes several significant improvements on the public APIs and internals.

MicroRaft's logo is created by modifying Raft's original logo. Raft's logo was created by Andrea Ruygt and licensed under the Creative Commons Attribution-4.0 International.