CAP Theorem

CAP Theorem

Consistency

• 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

• 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

• 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

Trade-offs

• 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

Consistency

• 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

Partition Tolerance

• 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

Availability

• 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

CAP Theorem Trade-offs

• 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

CAP Theorem

• 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

• 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

• 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