CAP Theorem

Study Notes

Consistency ensures that all nodes in a distributed system agree on the same data value
In a consistent system, all reads will see the most recent write or an error
Strong consistency: all nodes agree on the same data value and will not return stale data
Weak consistency: nodes may return stale data, but will eventually agree on the same data value

Partition tolerance ensures that a distributed system continues to function even when network partitions occur
A network partition occurs when nodes in the system cannot communicate with each other
Partition tolerance is necessary in distributed systems because network partitions are inevitable

Availability ensures that a distributed system is always accessible and responsive
A highly available system is one that is always accessible and responsive, even in the event of failures
Availability is often at odds with consistency, as high availability can compromise consistency

The CAP theorem states that it is impossible for a distributed system to simultaneously provide more than two out of the three guarantees: consistency, availability, and partition tolerance
Trade-offs between these guarantees are necessary:
- CA (Consistency and Availability): sacrifices partition tolerance
- CP (Consistency and Partition Tolerance): sacrifices availability
- AP (Availability and Partition Tolerance): sacrifices consistency
The choice of trade-off depends on the specific use case and requirements of the system

Ensures all nodes in a distributed system agree on the same data value
Guarantees that all reads will see the most recent write or an error
Strong consistency: all nodes agree on the same data value and will not return stale data
Weak consistency: nodes may return stale data, but will eventually agree on the same data value

Ensures a distributed system continues to function even when network partitions occur
A network partition occurs when nodes in the system cannot communicate with each other
Partition tolerance is necessary in distributed systems because network partitions are inevitable

Ensures a distributed system is always accessible and responsive
A highly available system is one that is always accessible and responsive, even in the event of failures
Availability is often at odds with consistency, as high availability can compromise consistency

It is impossible for a distributed system to simultaneously provide more than two out of the three guarantees: consistency, availability, and partition tolerance
Trade-offs between these guarantees are necessary:
CA (Consistency and Availability): sacrifices partition tolerance
CP (Consistency and Partition Tolerance): sacrifices availability
AP (Availability and Partition Tolerance): sacrifices consistency
The choice of trade-off depends on the specific use case and requirements of the system

Consistency in a distributed system ensures that all nodes agree on the same value for a data item, with two types:
- Strong consistency: all nodes have the same value at the same time
- Weak consistency: nodes may have different values, but eventually converge
Partition tolerance refers to a system's ability to function despite network partitions, which occur when nodes cannot communicate with each other
The CAP theorem states that a distributed system cannot simultaneously guarantee:
- Consistency
- Availability
- Partition Tolerance
Trade-offs must be made between these three properties, depending on system requirements, such as:
- CA systems (consistent and available) sacrificing partition tolerance
- AP systems (available and partition-tolerant) sacrificing consistency
- CP systems (consistent and partition-tolerant) sacrificing availability

Distributed systems consist of multiple nodes communicating to achieve a common goal
Characteristics of distributed systems include:
- Decentralization: nodes make independent decisions
- Distribution: nodes are physically separated
- Concurrency: nodes perform tasks concurrently
Examples of distributed systems include:
- Cloud computing systems
- Distributed databases
- Peer-to-peer networks

Availability refers to a system's ability to respond to requests and provide a response
High availability means the system is always available and responsive, even in the presence of failures
Availability metrics include:
- Uptime percentage
- Response time
- Throughput