# Simpler Consensus with Raft

A few weeks ago, I wrote about Paxos, which is (at least in my experience), the most widely used algorithm for consensus in distributed systems. I’m a huge fan of Paxos – I think that it’s a remarkably elegant system.

But Paxos does have its problem.

1. Paxos has a lot of roles: client, proposer, learner, acceptor, leader, follower. When you want to implement Paxos, you need to figure out all of those roles, and how you’re going to implement them. In general, you end up merging roles – but there are lots of ways of doing that merge. Each particular way of setting up the roles has its own properties, and thus its own tradeoffs that you need to understand.
2. Paxos, as we normally talk about it, is really a single-consensus protocol – that is, the basic protocol is designed to get a group of agents to come to consensus just once. If you want to be able to repeatedly seek new consensus values, you’re actually going to be using an extension to the basic paxos protocol. There are a ton of Paxos extensions that work to add repeated consensus. Paxos itself is simple and elegant, with well-defined formal properties that we care about – the moment we start modifying it, we can no longer count on those properties unless we can also prove them in our extension!
3. Paxos was originally described in a truly awful paper. Leslie Lamport was trying to write a paper that would be less dull than the typical bone-dry technical snoozer – but the way that he wrote it actually makes it much harder to understand.

In short: Paxos has more complexity than it needs, and despite that, it needs to be tweaked to be really useful, and getting those tweaks right is hard. There are, sadly, a lot of incorrect Paxos implementations – and their incorrectness has all-too-often come as a surprise to the people who rely on them.

To avoid those problems, there are other consensus algorithms out there. In this post, we’re going to look at one of the Paxos competitors – a consensus algorithm/protocol called raft.

Raft does away with the role complexity of Paxos. In Raft, you have a collection of cooperating agents. There are no distinct proposers, acceptors, or learners: there are just servers. Communication between the servers in raft is done entirely with synchronous remote procedure calls.

In Raft, the target of consensus is a log containing a sequence of events. The log is the history of the distributed system. The goal of raft is that the log be maintained in a consistent state throughout the raft network. Just like in Paxos, if we have $2n+1$ servers, up to $n$ can fail without the network losing its consistency.

Raft is designed in terms of remote procedure calls between the elements of the network. In Raft, we never talk about single messages – every communication between servers is a pair of messages: a request from caller to callee; and a response from callee to caller. When a message gets lost, we’ll just talk about it as a failed remote procedure call.

Within a Raft network, at any given time, each server has a state. It can be a follower, a leader, or a candidate. Within the network, there is at most one leader. When there is a leader, all of the other servers are in the follower state. The followers are almost entirely passive. Followers don’t talk to clients at all – they just wait for RPCs from the leader. The leader is the only participant that’s allowed to talk to clients: any client request must go through the current leader. The leader is also the only server that’s allowed to add new entries to the consensus log.

Raft divides time into a sequence of terms. In each term, the servers in the raft network need to select a leader using a process called an election. Raft is a strong leader protocol – no interactions with a client can take place except through a leader. If there’s no leader, then we can’t process client requests without a leader.

So, to understand Raft, there’s three processes that we need to
understand:

2. Transitions between terms
3. Appending an entry to a log.

In those processes, the servers have a collection of variables
that they use for the Raft protocol:

currentTerm
the current term for the server.
votedFor
the serverID that this server voted for in the current term, or “none”.
log
the list of entries in the log.
commitIndex
The index of the highest log entry known to be committed by the server.
lastApplied
The index of the highest log entry that’s been added to the log – but not necessarily committed. (It doesn’t become committed until a majority of servers accept it.)

In each term, the Raft cluster needs to have a leader. The way that a leader is selected called election.

Elections are triggered by a term transition. When a server in the cluster decided that it needs to start a new term, it increments its term number, puts itself into the candidate state, and sends a `RequestVote(term, candidateId)` RPC to each of the other servers in the cluster. This request asks the other servers in the cluster to select it as the leader. If it receives enough “yes” votes, it will become the leader.

When a server receives a `RequestVote` RPC, it checks the term. If it’s smaller than the server’s current term, then it replies “No” – meaning that it cannot support the requestor as leader.

If the term in the request is greater that the receiver’s term, then the receiver cannot have voted in the new term. So it updates to the term from the request, and then it replies “Yes”.

If the term in the request equals the receiver’s term, then the receiver has already updated its term. If it’s already voted for someone else as leader, then it can’t support the requestor, so it replies “No”. If it hasn’t voted for a leader in the term, then it votes for the requestor, and replies “Yes”.

If the requester receives “Yes” votes from more than 1/2 of the cluster (counting itself), then it becomes the leader, and starts both processing requests from clients, and sending heartbeats to the other servers in the cluster.

If it doesn’t receive enough votes, then it waits to see if anyone else becomes the leader and starts sending heartbeats. If it doesn’t get a heartbeat in time, then it starts over: it would increment its term again, and try to start a fresh election.

### Term Transitions

For a given server, term transitions happen in three ways:

1. Timeout: the leading server needs to periodically communicate with each of the followers. This process is called heartbeat: even if the leader has no updates for its followers, it sends RPC calls to the followers just to say “I’m still here”. If a client goes too long without receiving a heartbeat, it decides that the leader was lost, and it will increment the term number, and trigger a new election.
2. Leader resignation: the current leader can, at any time, decide to stop being the leader. (This is typically done by an implementation as part of a system that says that there’s a maximum period between leader elections. For example, in the Aurora scheduler, we had leader elections at least once per day. In a raft consensus, the leader would trigger this by deciding it was time for it to stop being a leader, and triggering an election by starting a new term.)
3. External term change: every RPC received by a server includes a term number. If any RPC to a server ever includes a term number greater than the current term for that server, the server will update its term to the new number. As a special case of this, when a leader server decides to resign, it does that by sending an RPC to the other servers with an incremented term number.

### Appending to the log

We just spent a fair bit of time talking about leaders and elections. That’s almost beside the point. What we really want to do is just maintain a consistent log across the cluster of servers. Everything except creating log entries is just the book-keeping that’s necessary to make the consistent log work. The log itself is maintained using the `AppendEntries` RPC call.

When a client request does something that alters the state of the cluster, the leader describes that change by adding an entry to the log. It builds a proposed log entry, and sends it to the other members of the cluster using an RPC. If it gets enough “Yes” votes from other cluster members, then the log entry becomes committed, and the leader updates its `commitIndex` to the index of the new log entry to reflect that.

The RPC request takes a bunch of parameters:

3. prevLogIndex: the index number of the last log entry in the consensus log preceeding this new entry.
4. prevLogTerm: the number of the term where the last log entry was committed.
5. entries: a set of new log entries to be appended to the log.
6. leaderCommit: the index of the commitlog on the leader after this set of entries has been committed.

When an `AppendEntries` call is received by a follower, what it does is:

• If the receiver’s term is greater than the request term, then the receiver rejects the request by replying “No”.
• If the the receivers commit index is larger than the commit index of the request, then it rejects the request by replying “No”.
• If the receiver’s log doesn’t contain an entry at `prevLogIndex`, or that entry’s term doesn’t match the request `term`, then it rejects the request by replying “No”.
• If there’s an entry in the log with the same index as the new log entries, and the term in the request matches the receiver’s term, then the receiver removes all entries after `prevLogIndex` from its log.
• The receiver then appends the new entries from the request to its log.
• If the `leaderCommit` is greater than the `commitIndex` on the receiver, then the receiver updates its `commitIndex`.
• Finally, the receiver replies “Yes”.

When a majority of the cluster members have accepted an `AppendEntries` call, then the log entry gets committed.

The one part of this that’s confusing is how the logs get managed. The leader creates a new log entry, and sends it to the other servers. The complexity comes from dealing with cases where something doesn’t reach consensus.

For example, the leader sends entries 5, 6, and 7 to server S. S adds the entries to its copy of log – it now contains [1, 2, 3, 4, 5, 6, 7]. Meanwhile, the leader also sends those entries to server T, but the RPC to T fails due to a network fault. Another client request happens, and now the leader sends [5, 6, 7, 8] to S. S sees that it’s got entry 5 already: so it discards everything after 5, and then re-appends.

So the trailing segment of the log can change! How do we handle consensus?

The next time that the leader sends an `AppendEntries` to a follower, it contains the leader’s `commitIndex`. The follower updates its commit index to that value. Once it’s done that, any request from a leader that tries to modify anything that comes before that commit index will be rejected.

The consensus commit thus doesn’t really occur until the next heartbeat call after a log update.

## Raft versus Paxos

That’s the basics of Raft.

In comparison to Paxos, there’s a couple of things to notice:

1. There’s a lot less confusion around roles. Paxos had a ton of different roles, and rules for interactions between the different roles. Raft doesn’t have any of that: it’s just servers, with one of the servers designated as the leader.
2. Raft explicitly manages a log, and it adds complexity around log management. In Paxos, you’re just managing a single consensus value; in Raft, you’ve got a sequence of log entries.
3. Paxos is defined in terms of messages; Raft is designed in terms of remote procedure calls.

So is Raft really simpler than Paxos? I think that’s up for discussion. Personally, I prefer Paxos. There’s a lot of complexity hidden under the covers of the RPC system. It looks simple on the surface, but all of the complexity of message passing, lost messages, message duplication – it’s still there. It’s just been swept under the carpet, as if that really makes it easier.

The way that the logs get maintained is confusing. That’s inevitable: getting distributed knowledge is never easy. Raft at least makes that part of things explicit, whereas it’s a common part of Paxos implementations, but it’s not really specified in the protocol.

## 3 thoughts on “Simpler Consensus with Raft”

1. Seth Verrinder

One thing I noticed in the Raft paper is that the RPCs are very overloaded, so even though there aren’t very many you really have to pick them apart to see what’s going on. Also, tying the algorithm to RPC makes it difficult separate the parts that are essential from the parts that are just a consequence of RPC. For example, there’s no safety or correctness reason why the request and reply for AppendEntries have to operate in lock-step except that’s how RPC works. You could implement Raft using message passing and make the reply asynchronous.

I don’t think that Raft sweeps much under the carpet with RPCs, though. At least not any more than a typical Paxos description. I think the only guarantee that Raft requires from the RPC implementation is that messages don’t get garbled, which would be the same for Paxos right?

1. markcc Post author

Not really, no.

The blockchain consensus is based on much looser cooperation. In the blockchain, consensus is, basically, “longest chain wins”. In the event of network partitions, you could wind up with fairly long periods of non-obvious non-consensus, but the way that it relies on the global internet means that that kind of partition should effectively never happen – or if it did, it would be obvious enough that no one would be concerned about the trouble with the blockchain; and it would resolve as soon as the network partition.