CAP Theorem
The CAP theorem, also known as Brewer’s theorem, states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees:
- Consistency: Every read receives the most recent write or an error. This means all nodes see the same data at the same time.
- Availability: Every request receives a (non-error) response, without the guarantee that it contains the most recent write. This means the system is always up and running, even if some nodes are down.
- Partition tolerance: The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. This means the system can handle network failures.
In simpler terms, if there’s a network partition, you have to choose between consistency and availability. You can’t have both.
Let’s illustrate with a simplified example of two database servers, Server A and Server B, replicating data.
Scenario 1: Network Partition and Prioritizing Consistency
Imagine a network issue separates Server A and Server B. A client writes new data to Server A. If we prioritize consistency, Server A won’t acknowledge the write until it can confirm with Server B (which it can’t reach). This leads to an error or timeout for the client, upholding consistency (no stale data read) but sacrificing availability (client request failed).
// Conceptual JavaScript example (not real implementation)
// Server A receives write request
function writeData(data) {
if (networkPartitioned) {
return "Error: Unable to confirm write due to network partition"; // Prioritizing consistency
} else {
// ... replicate to Server B ...
return "Write successful";
}
}
Scenario 2: Network Partition and Prioritizing Availability
In the same scenario, if we prioritize availability, Server A would acknowledge the write immediately, even though it can’t communicate with Server B. This preserves availability (client request succeeds) but sacrifices consistency (Server B has stale data).
// Conceptual JavaScript example (not real implementation)
// Server A receives write request
function writeData(data) {
if (networkPartitioned) {
return "Write successful"; // Prioritizing availability (potential inconsistency)
} else {
// ... replicate to Server B ...
return "Write successful";
}
}
Choosing the Right Balance:
The CAP theorem forces you to make a trade-off. The best choice depends on your specific application needs:
- CP (Consistency and Partition Tolerance): Suitable for systems where data consistency is paramount, even at the cost of temporary unavailability during network issues. Examples: financial transactions, distributed databases.
- AP (Availability and Partition Tolerance): Suitable for systems where availability is crucial, even if it means potentially serving slightly stale data. Examples: social media, e-commerce product catalogs.
Remember, you cannot choose CA (Consistency and Availability) in a distributed system subject to network partitions. The CAP theorem ensures that in the presence of a partition, you must choose either consistency or availability.