Data Consistency Models in Distributed Systems

Problem Description
In distributed systems, data consistency models are the core mechanisms for ensuring data synchronization across multiple nodes. Please explain the differences between strong consistency, weak consistency, and eventual consistency, and illustrate their respective applicable scenarios with real-world examples.

Solution Process
1. Core Objectives of Consistency Models
In distributed systems, data is typically stored in multiple replicas across different nodes. A consistency model defines how the system should guarantee the correctness of returned results when clients read data. The core trade-off lies between: the synchronization speed among data replicas and system availability/performance.

2. Strong Consistency

• Definition: After any read or write operation is completed, all subsequent reads (regardless of which replica is accessed) will immediately see the latest written data.
• Implementation Principle: Typically achieved through synchronous replication (e.g., Raft, Paxos protocols). Writes must wait for all replicas to synchronize successfully before returning a result, potentially blocking other operations during this period.
• Example:
  User A transfers 100 yuan to a bank account. The system must ensure that User B sees the updated balance immediately when querying. If replica synchronization is delayed, B might read stale data, leading to errors like duplicate transfers.
• Applicable Scenarios: Scenarios with extremely high requirements for data accuracy, such as financial transactions or inventory deduction.
• Drawbacks: High latency (synchronous waiting reduces performance), low availability (service may be refused during network partitions).

3. Weak Consistency

• Definition: After a write, subsequent reads are not guaranteed to immediately obtain the latest value; the system may return stale data.
• Implementation Principle: Employs asynchronous replication. Write operations return success immediately, and replicas synchronize data in the background.
• Example:
  Social media like functionality. After User A likes a post, even if some users temporarily cannot see the updated like count, the core functionality is not affected.
• Applicable Scenarios: Scenarios with low real-time requirements (e.g., webpage visit statistics, non-critical configuration updates).
• Drawbacks: Data may remain inconsistent for extended periods, requiring the business layer to tolerate dirty reads.

4. Eventual Consistency

• Definition: A special form of weak consistency. It guarantees that in the absence of new writes, all replicas will eventually (after a period of time) synchronize to the latest state.
• Implementation Principle: Achieved via asynchronous replication plus conflict resolution mechanisms (e.g., version vectors, CRDTs).
• Example:
  DNS (Domain Name System): After a domain mapping update, global DNS servers may take minutes to hours to synchronize, but eventually, all query results become consistent.
• Applicable Scenarios: Most internet applications (e.g., e-commerce product detail pages, comment systems), balancing performance with eventual correctness.
• Drawbacks: Existence of an "inconsistency window," requiring the design of conflict handling (e.g., shopping cart merging).

5. Comparison and Selection Recommendations

6. Combined Use in Practical Architectures
Modern systems often employ hybrid strategies:

• E-commerce scenario: User balance uses strong consistency (to prevent overselling), while product view counts use eventual consistency.
• Technology selection: Financial systems use ZooKeeper (strong consistency), cache systems use Redis Cluster (eventual consistency).

Summary
The choice of consistency model is essentially a balance between business requirements and technical costs. Strong consistency ensures data safety at the expense of performance, while weak and eventual consistency improve system scalability by tolerating temporary inconsistency. During design, it is necessary to clarify the business's tolerance for consistency levels and implement monitoring and compensation mechanisms (e.g., reconciliation, retries) to mitigate risks.