Albert Einstein turned the world on its head. Everything is relative. Time is relative. Simultaneity is relative. There is no universal clock.
The same thing happened in the computer science world, not with a bang or the attention that was given relativistic physics, but with a small crowd paying attention. CAP Theorem has the same implications for computer scientists as relativity had for physicists, but no one is really paying attention.
Computer scientists may dream of a fully consistent database distributed across a network, capable of two-phase commit and typical consistency guarantees. Not many think about the implications of such a database.
Imagine you had your database split across two datacenters. Let's call them Foo and Bar. While at one point in time, Foo and Bar could talk, now they can't. Perhaps a clumsy janitor spilled the bucket for his mop, and it destroyed all of your routing hardware between your datacenters. Some customers are accessing Foo and some are accessing Bar. If they want to perform a read, they'll get data that's possibly stale. If they want to perform a write, what happens?
CAP Theorem is possibly the most important property of distributed systems that you can possibly understand. It's been motherfucking proven by computer scientists, bitches! So what does it state? The core idea is that consistency which is both instantaneous and global is impossible, except on a single node. As soon as you have more than node storing data for you, you either have to sacrifice availability or consistency. Much like how you can only infer the state of the universe from old light beams hitting your light cone in relativistic physics, distributed systems can only infer the state of other nodes via the protocols they communicate with. These protocols can fall into one of three different forms per the CAP theorem:
- CP: we have masters you have to listen to, bitch slaves. You can try to change the cluster state but all changes have to go through the masters, and if your master is split, you can't modify any state you bitch submissive node
- AP: sure, we'll accept reads and writes. We'll try to eventually resolve any conflicts in the event of a netsplit. If we're cool we'll let you specify your own conflict resolution mechanism in the event of conflicting writes, otherwise we'll just do last writer wins.
- CA: the network broke? Whoa. I was totally not expecting that. I was just happily doing bongrips until you barged in. I'm so high I don't even know what's going on.
If you're writing a datastore you consider to be "CA", you're doing it wrong. Fortunately, most datastores that claim to have "CA" properties are actually CP. That is to say, in the event of a network partition, they sacrifice availability. Reads to the cluster hopefully work, but writes shouldn't. Writes should error or time out for a consistent system. Any system that functions this way is in fact "CP". That is to say: it provides consistency, but in the event of a netsplit, you lose availability, at least for writes. Ideally these systems still provide stale reads.
Calling your system CA is an admission of the fact that if a partition separation occurs, and due to writes each partition has a separate view of the world, when the netsplit resolves your partitions have no mechanism for resolving conflicting states between partitions and thus your datastore is just fucked. Short of a system administrator picking which view of the world they consider to be the best and blowing away the state on the other nodes, the system state is going to be inconsistent and irresolvable.
So what are the alternatives which are partition tolerant and don't break in the event that the janitor spills mopwater on the router that routes packets between your datacenters?
Option #1: sacrifice consistency for availability
This option lets clients read and write from any partition. When connectivity between partitions is restored, there's a plan for resolving all of the writes into a consistent world state. The simplest plan is last writer wins, which is used by Cassandra. Whoever was the last one to alter the cluster state provided the definitive cluster state, simply by virtue of writing last.
In an eventual consistency model, clients may have inconsistent views of the world. This is unfortunately a fact of life. State may unexpectedly change by a particular client's view, because it's being modified on a different partition and those partitions managed to sync up. The road ahead of you may be a beach, but after syncing up with another part of the world, it may turn into a cactus. Such is life.
Option #2: sacrifice availability for consistency
Say you have a master/slave data store, and someone writes to a slave. Wat do? Slave can't talk to master. What's the most obvious option? Error.
This is a "CP" system. Slaves can serve stale state, but if you want to change the state, you have to talk to the master for that state. State can be partitioned/shareded/durr between multiple masters, but if the master for the particular state a slave wants to modify isn't available, sorry, that's a paddlin', or rather an error.
Option #3: highly available, and consistent at the partition level, netsplit fixed wat???
The only other option, the "CA" option, is that each partition pretends that writes to that partition are consistent, but when the netsplits are resolved, there's no consistent way to resolve that state. That is to say, if netsplits occur, each partition is highly available and allows writes, but as soon as the netsplit resolves, the cluster state is broke and irreconcilable.
In a quick survey of NoSQL datastores, Membase, MongoDB, and the hypothetical Redis Cluster all claim to be "CA". Are they really? Or do they just sacrifice availability in the event of a netsplit? If that's true, they're actually "CP", not "CA".
You really don't want to sacrifice partition tolerance. In fact, some might go as far as to say you CAN'T sacrifice partition tolerance. If you think your system is "CA" you probably understand the the CAP theorem and its implications.
CAP is the law, and AP or CP are the only reasonable options
If you're running an app on a distributed network, things are going to go wrong. You might idealistically claim that you can provide both consistency and availability, sacrificing partition tolerance. You can't.
Can you pick "CA"? Sure, if you're content with losing all your data if the network had any errors. That's probably not what you mean. If you're building software for a fault-tolerant world, it's probably either going to be:
- Consistent but unavailable during netsplits (CP)
- "Eventually" consistent but available during netsplits (AP)
The only other option is "broken after netsplits". The best option is probably AP: this is the approach was pioneered by the Amazon Dynamo system which powered Amazon's shopping cart, and by copycat open source systems like Riak and Cassandra, not that Riak and Cassandra aren't awesome too.
If you're not a fan AP of broken "CA", you're a fan of CP, which puts you in the same league as this guy: