How to implement a distributed system that satisfies all properties of CAP? What properties are there?

The CAP theorem states that in a distributed system, it is impossible to guarantee all three properties simultaneously:

• Consistency: All nodes see the same data at the same time.
• Availability: Every request receives a response (success or failure) without guarantee of data freshness.
• Partition Tolerance: The system continues to operate despite network partitions (loss of communication between nodes).

Only two of these properties can be guaranteed at the same time; all three cannot be guaranteed simultaneously.

Implementing a system that satisfies all properties of CAP in the classical sense is impossible. However, it is possible to approach a balance using the following approaches:

• Eventual Consistency: The system provides availability and partition tolerance, with consistency achieved over time.
• Use of consensus protocols: such as Paxos or Raft to achieve consistency during partitions.
• Hybrid architectures: different parts of the system can guarantee different properties depending on requirements.

For example, a distributed database that, during network partitions, provides data with possible update delays (maintaining availability and partition tolerance), and after network recovery, synchronizes data for consistency.

Thus, when designing a distributed system, it is important to determine CAP priorities based on business requirements and to use architectural patterns that minimize compromises.