CAP Theorem and BASE Theory of Databases

CAP Theorem and BASE Theory of Databases

Description
CAP theorem and BASE theory are the fundamental theoretical foundations of distributed database systems. CAP theorem defines the three characteristics that distributed systems must trade off in design, while BASE theory provides practical ideas for achieving high availability in distributed environments. Understanding these two theories helps us make reasonable architectural choices when designing distributed databases.

I. Detailed Explanation of CAP Theorem

  1. Definition of CAP

    • Consistency: All nodes have exactly the same data at the same time (equivalent to atomicity).
    • Availability: Every request receives a non-error response (does not guarantee the latest data).
    • Partition Tolerance: The system continues to operate normally during network partitions (communication interruptions between nodes).

  2. Impossibility of Achieving All Three

    • Network partitions are an inevitable phenomenon in distributed systems (e.g., fiber optic cables being cut), so P must be guaranteed.
    • Given that P must hold, C and A cannot be satisfied simultaneously:
      • If C is guaranteed: The system must prohibit writes during partitions (violating A).
      • If A is guaranteed: Writes are allowed during partitions, but data may become inconsistent (violating C).

  3. Trade-offs in Practical Systems

    • CP Systems (e.g., ZooKeeper): Reject writes during partitions to ensure data consistency.
    • AP Systems (e.g., Cassandra): Allow writes during partitions but may return stale data.

II. BASE Theory: A Supplement to CAP

  1. Basically Available

    • The system provides core functionality even during failures (e.g., queries degraded to return cached data).

  2. Soft State

    • Intermediate states are allowed (e.g., master-slave synchronization delays), meaning data across nodes may temporarily be inconsistent.

  3. Eventual Consistency

    • After a period of synchronization, all nodes' data will eventually become consistent (e.g., DNS system).

III. Practical Integration of CAP and BASE

  1. Scenario Analysis: E-commerce Inventory System

    • During partitions, if strong consistency (CP) is enforced, users cannot place orders; if BASE theory is adopted, overselling can be allowed and asynchronously corrected later.

  2. Technical Implementations

    • Read-Write Separation: Write operations go to the primary database, read operations go to replicas (accepting temporary inconsistency).
    • Asynchronous Replication: Use log synchronization tools (e.g., Canal) to synchronize data with delays.
    • Conflict Resolution: Use version numbers (Vector Clock) or CRDTs (Conflict-Free Replicated Data Types).

IV. Important Notes

  1. The C in CAP refers to strong consistency, while eventual consistency in BASE is a form of weak consistency.
  2. After network partition recovery, AP systems need to synchronize data through "Read Repair" or "Hinted Handoff".

By understanding CAP and BASE, we can flexibly choose distributed database solutions: financial transaction systems require CP, while social network scenarios may opt for AP.