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Consensus algorithms at scale: Part 4 - Establishment and revocation

In part 4 of the consensus algorithms series we look at how algorithm leaders are established and revoked.

Consensus algorithms at scale: Part 4 - Establishment and revocation

If you’re still catching up, you can find links to each article in the series at the bottom of this article.

The Leader election process is the less frequently used part of the consensus process. However, it is the more complex one. We will therefore drill into this part first.

Recap of parts 1-3

  • Durability is the main reason we want to use a consensus system.
  • Since Durability is use-case dependent, we made it an abstract requirement that the consensus algorithms assume nothing about the durability requirements.
  • We started off with the original properties of a consensus system as defined by Paxos and modified it to make it usable in practical scenarios. Instead of converging on a value, we changed the system to accept a series of requests.
  • We narrowed our scope down to single leader systems.
  • We came up with a new set of rules that are agnostic of durability. The essential claim is that a system that follows these rules will be able to satisfy the requirements of a consensus system. Specifically, we excluded some requirements like majority quorum that have previously been used as core building blocks in consensus algorithms.
  • We looked at a number of practical scenarios where it is difficult to make a simplistic majority quorum approach work well. A flexible consensus system would accommodate those use cases more comfortably.

Consensus Algorithms at Scale - Part 1

Consensus Algorithms at Scale - Part 2

Consensus Algorithms at Scale - Part 3

Conflating too many concerns

Traditional algorithms like Paxos and Raft try to do too many things at once. The cleverness of those approaches is commendable. However, such implementations are too rigid, and you cannot make modifications to specific parts of the algorithm without breaking something else.

What we are going to do now is separate those concerns, and talk about how to address them individually. We can still choose to conflate them, but it should be a conscious decision.

An important revelation is that all leader-based consensus algorithms perform the following actions when electing a new leader:

  • Revoke a previously existing leadership
  • Establish a new leader

An additional constraint is that a revoke must precede the establishment step. Otherwise, we will end up with more than one leader.

Majority-based consensus algorithms satisfy this constraint atomically: When a leader successfully recruits all the necessary followers, it automatically achieves the goal of revoking the previous leadership.

Because the revoke was implicitly achieved, it was never called out as a separate concern. More importantly, it was never called out as a concern that could be separated.

In other words, it is not necessary to perform the two operations as part of the same action. This separation becomes more important for consensus systems that are not majority-based.

To limit complexity, this section will start by focusing on establishment and revocation of leadership. Once we have analyzed these two actions, we will layer in the rest of the concerns, which are forward progress, race handling, and propagation of requests.

Even though the two actions can be performed separately, there exists a strong relationship between them: Leadership is established when all the parameters are in place for a leader to successfully complete requests. Any change that invalidates this condition is a revocation.

Proposal Numbers

In traditional consensus algorithms, the establishment of leadership is achieved by requesting followers to accept a specific proposal number. If a candidate manages to perform this action on the majority of the nodes, then the leadership is deemed as established.

To revoke such a leadership, the new candidate pushes a different proposal number to those followers, which implicitly revokes the previous leader’s ability to propagate requests to those nodes. When the majority of the followers are reached, the revocation of the previous leadership and the establishment of the new one are simultaneously achieved.

Without Proposal Numbers

The usage of proposal numbers is only one of many methods of establishing and revoking leadership. For example, in MySQL, the replication mechanism could also be used to achieve the same objectives: Pointing a semi-sync replica at a primary is an act of leadership establishment. Requesting such a replica to stop replicating or to replicate from a different source would achieve the objective of revocation.

Knowing the current leader

Depending on how we handle races, the current leader may not be known. If so, the revocation must be performed against all potential leaders. In other words, the election process must reach enough nodes to be sure that no existing leader can complete their requests. This will become more clear in the next blog where we will cover race conditions

Direct Leader Demotion

Now that we have identified revocation as a possible separate action, we can look at more than one way to revoke an existing leadership.

If the current leader is known, requesting that leader to step down also results in a valid revocation. This method is generally more graceful because the leader has the opportunity to complete in-flight requests and also inform clients of an imminent change in leadership.

Demoting the existing leader is meaningful only for planned changes, like a software rollout. If a leader becomes unreachable due to a crash or a network partition, we have to fall back to requesting the followers to stop accepting requests from the current leader to achieve revocation.

In Vitess, we have two operations that can perform a leadership change: PlannedReparentShard (PRS) and EmergencyReparentShard (ERS). For software rollouts, we use PRS to demote the current primary to a replica before performing the update. But we use ERS if we detect that the primary database is down or not reachable.

If a PRS is issued, the low level vttablet component of vitess goes into a lameduck mode where it allows in-flight transactions to complete, but rejects any new ones. At the same time, the front-end proxies (vtgate) begin to buffer such new transactions. Once PRS completes, all buffered transactions are sent to the new primary, and the system resumes without serving any errors to the application.

Why use two approaches?

A typical cluster could be completing thousands of requests per second. In contrast, a software rollout is likely a daily event. In further contrast, a node failure may happen once a month or even less frequently.

It is important that we optimize for the common case. This means that we want leadership changes to be graceful during software rollout. Ideally, the application should see no errors during this time. The approach of demoting the current leader gives us this opportunity.


Can we assume that two different algorithms are interchangeable? The answer is yes. Let us assume that a leadership is established by satisfying conditions A and B. One algorithm achieves revocation by making condition A false, and the other by making condition B false. In both cases, it is a successful revocation.

Once revocation is complete, both algorithms have to make conditions A and B true for the new leader, which will allow for subsequent rounds to use any method of revocation.

Other approaches

We can think of innumerable other ways to establish and revoke leadership, and they would all be valid as long as the revocation and establishment conditions are accurately satisfied. As an extreme example, cutting the network cable that connects a leader to its followers is also a valid way to revoke an existing leadership.

I know of one incident at Google where we had to dispatch a human to physically shut down a machine where a leader had gone rogue.

In the next blog post, we will discuss possible options for handling races and ensuring forward progress. At that time, we will re-evaluate these approaches.

Read the full Consensus Algorithms series