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What is CAP Theorem?

davejsaunders profile image Dave Saunders ・3 min read

CAP Theorem describes the decisions we have to make when building a distributed data store.

Let's take a look at CAP Theorem, in under 5 minutes...

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Why should I care?

Have you ever wanted to know:

  • How distributed databases handle network failures?
  • What tradeoffs we must make when designing distributed data stores?

In 5 minutes or less

Eric Brewer's CAP Theorem tells us that a distributed data store must choose no more than two of the following:

  • Consistency
  • Availability
  • Partition Tolerance.

What do those definitions mean, and why can't we have all three?

Let's imagine we're designing a distributed database.

For simplicity our database will have just two interconnected instances, or 'nodes':

Node returning no when disconnected


Partition Tolerance

Partition Tolerance means that one or more nodes in our distributed system can be split up and unable to communicate with each other (partitioned), and the system can still function.

Only a complete network failure is allowed to cause the system to respond incorrectly, anything else must be tolerated.

If we have a single node then there can be no partitions. But, in a distributed system, faults are inevitable given enough time. Therefore we cannot sacrifice Partition Tolerance.

With that in mind, we can actually re-state the problem like this:

In the event of a Partition, should we choose either Consistency or Availability?

Imagine there's a fault in our system, and the connection to one of the nodes is broken. Our distributed database is now partitioned.

Node returning no when disconnected

Somebody tries to update a record on one of the nodes.. What do we do now?


Choosing Consistency

'Consistency' in CAP Theorem means that I can update a record on one node, and somebody reading from another node will immediately see the
effect of my update.

(Note: this is specifically called 'linearizable consistency').

Obviously, instant communication isn't realistic in a distributed environment, so in practice, the goal is to reduce this to a level where we don't notice it.

This is a really useful property if you are a banking system. It would be a big problem if I could withdraw money from one ATM, then walk down the road to another ATM and withdraw the same amount again, because the database was not consistent across all nodes.

The nodes in our example database can no longer communicate, so we cannot reflect a change across all nodes 'instantly'.

So, if we want to make sure both nodes stay 'consistent', there are a few ways to handle this. We could shut down entirely, or refuse all updates and only allow reads, for example.

Node returning no when disconnected

Because we can't accept the update request though, we are sacrificing availability...


Choosing Availability

'Availability' means that if we make a request to any working node, we must get a non-error response, regardless of any partitions in the system.

The data is allowed to be out of date, or 'stale', but it must be available.

Twitter is a good example of a system where we might choose Availability. If I 'like' a tweet, but the other nodes don't reflect that 'like' immediately, it's really not the end of the world.

In this case, it's more important for the system to be 'available' and accept the update than to be 'consistent'.

In our example scenario, we could continue to allow clients to read and write to nodes on both sides of the partition:

Node returning no when disconnected

That would give us a system that is Available, but because there's no way for those nodes to keep their information in sync while partitioned, it's impossible for this to be (immediately) Consistent too.

This problem is the root of CAP Theorem. We cannot have both Consistency and Availability in the event of a partition (a loss of communication between nodes).. we must choose between the two.


Want to know more?

Check out these links:

Discussion (3)

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tburette profile image
Thomas Burette

Simple and well written. Good links as well.
Most CAP Theorem explanations online are...not great at all. Some weird triangle or Venn diagram implying SQL databases are CA and explaining P the same way as A...

A few remarks on the subject of CAP:

  • You can make different choices for different part of the system. For example the money part of a system could be CP and the counting of the page views could be AP.
  • if your database is not distributed the CAP theorem doesn't apply : partition are impossible.
  • It's possible to have both C and A when there are no partitions. Some nosql vendors tried to justify not having consistent database even in the absence of a partition by saying "CAP says it's impossible to have C and A". Not really
  • Choosing to abandon Consistency of Availability makes life difficult : you have to program a partition mode. When the partition ends you have to perform some partition recovery to merge the data and fix incoherences that arose due to the left hand not knowing what the right hand did. It can get hairy.
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cjcon90 profile image
Ciaran Concannon

Brilliant, I'm getting into system design for the first time and this is one of the clearest explanations I've seen!

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davejsaunders profile image
Dave Saunders Author

Thank you so much!