In 1999, Fox and Brewer published a paper on the CAP principle, where they wrote:

Strong CAP Principle. Strong Consistency, High Availability, Partition-resilience: Pick at most 2.

At PODC 2000, Brewer gave an invited talk where he popularized the CAP theorem (an unproven conjecture at the time), which was later formalized into a lower bound by Gilbert and Lynch, 2002. In this post we will state and prove a slightly stronger result whose essence appeared as early as PODC 1984 by DLS84 Theorem 5:

Theorem: It is impossible to implement State Machine Replication with safety and liveness given $n$ replicas for at least two clients and an adversary that can cause crash failures to $f\geq n/2$ replicas (and any number of crash failures to clients) in the partial synchrony model.

The consensus version of this theorem appears in DLS88 Theorem 4.3, the shared memory version in Lynch96, Theorem 17.6, and the SMR version in Gilbert and Lynch, 2002.

In hindsight, here is my interpretation of this impossibility:

  • Any system, in the face of a perceived 50-50 split, can either maintain safety or liveness, but not both.

In a previous post, we showed a similar result: that $f \geq n/2$ omission failures is impossible to obtain both safety and liveness in the lock step (synchronous) model.

Before we show the (rather simple) proof, let’s connect this to the CAP theorem.

What does the CAP theorem say?

A system cannot have all three properties: Consistency, Availability, and Partition tolerance. We now formally explain these in the context of State Machine Replication.

Consistency: sometimes also called Safety - that clients see responses that come from a common prefix of a total ordering of commands.

Availability: sometimes also called Liveness - that a non-faulty client will eventually get a response.

Partition Tolerance: this typically refers to the property that even if the adversary creates a partition of the system, each part will continue to provide the desired safety and/or liveness (consistency and/or availability). We will show how using crash failures and asynchrony we can essentially create such partitions.

Note that partition tolerance is different than safety and liveness, it’s more of a failure model or adversary model. As Gilbert and Lynch, 2012 conclude:

The CAP Theorem, in this light, is simply one example of the fundamental fact that you cannot achieve both safety and liveness in an unreliable distributed system.

Another example of this tension between safety and liveness occurs in the blockchain unsized setting.

Fox and Brewer’s categorization

Due to this impossibility, Fox and Brewer categorized all solutions into three buckets:

  1. CA: Consistent and Available - but not partition tolerant. The trivial solution is a centralized system. As there is no resilience to partitions, it’s not clear this is an interesting choice.

  2. AP: Available even during Partitions - but not always consistent. This is the space of weak consistency and eventual consistency that is common for some applications (HTTP Web caching is a great example). This approach, in the Byzantine setting, has several important use cases and connections to Eclipse attacks that will be a topic of future posts. Bitcoin’s consensus protocol is an example of AP. Ethereum’s inactivity leak mechanism is another example.

  3. CP: Consistent even during Partitions - but not always available. The system is always consistent, but a partition may cause a loss of availability. The trivial solution is a centralized system. A more interesting solution is Two Phase Commit which allows replication and obtains consistency at the cost of losing availability for partitions. Many distributed systems and databases use this approach, but even one failed/partitioned server can block the system. Can we get slightly better availability?

Yes, its a trilemma. Yes, in hindsight, Brewer writes that “2 of 3” is misleading because there are more nuanced properties that CAP does not capture. We highlight one such nuance and its deep connections to the impossibility proof.


A powerful approach to address the CAP theorem is to obtain consistency even during partitions and in addition a majority partition availability (MAJ-A) property:

Majority partition availability (MAJ-A): A partition that has the majority of the replicas can continue to have availability (in partial synchrony).

This is exactly what Paxos obtains and what many modern state machine replication systems provide (Raft, etcd, etc). Recall that Paxos is:

  • Always safe.
  • Maintains availability, after GST, for the majority partition.

Requiring just the majority part (or super majority part, like $2/3$) to be available during a partition is the path that many BFT protocols use (for example, PBFT).

A lower bound for both CAP and CP-MAJ-A

In one lower bound we prove:

  • Impossible to get both safety and liveness (aka, consistency and availability) for a system with two servers that can be partitioned (via an adversary controlling one server failure and partial synchrony).

This impossibility has two implications:

  1. It proves the CAP theorem - by showing a specific problematic partition into two parts of equal size. This is the essence of Gilbert and Lynch 2002, Theorem 2.
  2. It proves that the fault tolerance of CP-MAJ-A protocols like Paxos is optimal. The best one can hope for is $f<n/2$ failures, because when $f \geq n/2$ it is possible to partition the network into two parts of equal size. This is the essence of DLS88, Theorem 4.3.

The proof

This proof is word for word very similar to the proof for lock step synchrony and omission failures. The minor difference is the use of partial synchrony instead of omissions to create the partition. We provide the proof for completeness.

Assume a protocol that is safe and live for two replicas and two clients in partial synchrony and reach a contradiction. The adversary can cause one replica crash failure and any number of client crash failures.

World A:

Client $1$ sends command $C1$, and the adversary crashes server $2$ and client $2$ (or causes a partition between $1$ and $2$). All messages arrive immediately. Since the protocol is safe and live, the system must notify client $1$ that command $C1$ is the only committed command.

World B:

Client $2$ sends command $C2$, and the adversary crashes server $1$ and client $1$ (or causes a partition between $1$ and $2$). All messages arrive immediately. Since the protocol is safe and live, the system must notify client $2$ that command $C2$ is the only committed command.

World C:

Client $1$ sends command $C1$, and client $2$ sends command $C2$. Using partial synchrony, the adversary delays all communication between:

  1. Client $1$ and server $2$;
  2. Client $2$ and server $1$;
  3. Server $1$ and server $2$.

All other messages arrive immediately.

Observe that the view of server 1 in world A and world C is indistinguishable. Since in worlds A and C, client $1$ only communicates with server $1$, it also has indistinguishable views.

Similarly, the view of server 2 in world B and world C is indistinguishable. Since in worlds B and C, client $2$ only communicates with server $2$, it also has indistinguishable views.

So in world C, the two clients will see conflicting states and this is a violation of safety.


The proof captures the essence of the inability to tell the difference between a crash and a delay in partial synchrony.

Perhaps the slight difference between this version and previous formulations is the explicit separation to servers and clients.

It’s a good exercise to extend this proof to any $f \geq n/2$.

Abadi extends the CAP theorem to PACELC to highlight the importance of latency (a quantitative measure) not just availability (a binary measure) and the importance of latency even when there are no partitions.


Many thanks to Kartik Nayak and Seth Gilbert for insightful comments and feedback.

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