The Raft consensus algorithm

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Updated at 2023/03/07

Introduction

Distributed systems are computer systems that are composed of multiple independent components, such as servers or nodes, that communicate and coordinate with one another to accomplish a task. Distributed systems are commonly used in large-scale applications, such as cloud computing, data centers, and web services, where the system needs to be fault-tolerant and resilient to failures.

One of the main challenges of distributed systems is ensuring consistency and agreement between the different components, particularly when failures occur. To address this challenge, researchers have developed consensus algorithms, which are algorithms that allow a distributed system to reach agreement on a value or a decision.

Consensus algorithms are a fundamental building block of distributed systems and have been extensively studied and implemented over the years. One of the most popular consensus algorithms is the Raft consensus algorithm, which was introduced by Diego Ongaro and John Ousterhout in 2014.

Overview of Raft

The Raft consensus algorithm is designed to be easy to understand, implement, and debug, while providing strong safety guarantees and fault-tolerance properties. Raft works by electing a leader node that is responsible for managing the state of the system and replicating changes to the other nodes in the cluster.

Raft’s key innovation is its use of a replicated log, which is a data structure that maintains a record of all the changes made to the system. Each node in the cluster has a copy of the log, and the logs are kept consistent through a process called log replication.

Raft’s consensus algorithm is divided into two main phases: leader election and log replication.

Leader Election

In Raft, the leader node is responsible for managing the state of the system and replicating changes to the other nodes in the cluster. The leader is elected through a randomized timeout mechanism, where each node in the cluster waits for a random period of time before attempting to become a leader.

If a node doesn’t receive a message from the current leader within a certain period of time, it assumes that the leader has failed and starts a new election. During an election, each node sends out a request for votes, and the node with the most votes becomes the new leader.

Once a new leader is elected, it sends out heartbeat messages to the other nodes in the cluster to let them know that it is still alive and to maintain its leadership position.

Log Replication

Once a leader has been elected, it is responsible for managing the state of the system and replicating changes to the other nodes in the cluster. This is done through the use of the replicated log, which maintains a record of all the changes made to the system.

When a client sends a request to the leader to modify the state of the system, the leader appends the change to its log and sends the log entry to the other nodes in the cluster for replication. Once a majority of nodes have acknowledged the log entry, the leader commits the change to its state machine and notifies the client that the change has been made.

If a node fails or becomes disconnected from the cluster, Raft ensures that the log entries that were not replicated to that node are eventually replicated to a majority of the nodes in the cluster. This is done by maintaining a set of match and next index values for each follower node, which indicate the highest log entry that the follower has replicated and the next log entry that the leader should send to the follower.

Safety Properties

In addition to its ease of use and fault-tolerance properties, Raft also provides strong safety guarantees to ensure the consistency of the system. Specifically, Raft guarantees the following safety properties:

Leader completeness: If a log entry is committed, then that entry will be present in the logs of all future leaders.

Log matching: If two logs contain an entry with the same index and term, then the entries are identical in all future logs.

Leader election safety: At most one leader can be elected in a given term.

State machine safety: If a server has applied a particular log entry to its state machine, then no other server will ever apply a different log entry for the same index.

These safety properties ensure that the system remains consistent even in the presence of failures and network partitions.

Conclusion

In conclusion, the Raft consensus algorithm is a popular and widely-used algorithm for ensuring consistency and fault-tolerance in distributed systems. It provides a simple, easy-to-understand approach to consensus that is robust and scalable.

Raft’s use of a replicated log and randomized leader election mechanism make it well-suited for a variety of applications, from databases and key-value stores to web services and cloud computing.

While Raft is not the only consensus algorithm available, it has become a popular alternative to the more complex and difficult-to-implement Paxos algorithm. If you are working on a distributed system and need to ensure consistency and fault-tolerance, Raft may be a good choice for you to consider.