In distributed systems, achieving strong consistency is a critical and challenging aspect that affects system performance and fault tolerance. The discussion of consistency levels is particularly relevant as applications and data grow to span across multiple geographical locations and numerous servers, which involves complex coordination.

What is Strong Consistency?

Strong consistency in a distributed system ensures that any read operation retrieves the most recent write for a specific data point, regardless of which node in the system is accessed. This level of consistency appears to the user as if only a single copy of the data exists and that every read reflects the latest written value, even in the presence of concurrent writes.

Challenges in Achieving Strong Consistency

Distributed systems inherently face the dilemma of balancing between consistency, availability, and partition tolerance, famously outlined by the CAP theorem. According to the theorem, a distributed system can only simultaneously provide two of the following three guarantees:

• Consistency: Every read receives the most recent write or an error.
• Availability: Every request receives a non-error response, without the guarantee that it contains the most recent write.
• Partition Tolerance: The system continues to operate despite an arbitrary number of messages being dropped or delayed by the network between nodes.

When a network partition occurs, fulfilling both availability and strong consistency is theoretically and practically impossible. If a system aims for strong consistency, it may need to sacrifice availability, meaning that some parts of the system might become temporarily unavailable.

Techniques to Achieve Strong Consistency

1. Two-Phase Commit Protocol (2PC): A coordinator node manages the transaction. All participants of the transaction prepare to commit and await confirmation from the coordinator. While 2PC ensures consistency, it does so at the expense of availability and can be a bottleneck.
2. Paxos: This is a family of protocols for solving consensus in a network of unreliable processors (as in real-world distributed systems). Consensus generally involves multiple servers agreeing on values, and once achieved, the learnt value is replicated safely across the cluster.
3. Raft: Similar to Paxos in achieving consensus, Raft is designed to be more understandable and was developed to provide a clear and better-understood way to achieve strong consistency. It divides time into terms, and a leader is elected for each term. The leader handles all client interactions and log replication.
4. Quorum-based Techniques: These involve ensuring that operations on data replicas require a majority of nodes (known as a quorum) to participate, thus ensuring consistency across the cluster.

Transaction in Distributed Databases for Consistency

Distributed databases often use transactions to manage access and modifications to data in a way that ensures strong consistency. A transaction in a distributed database will typically employ mechanisms such as locks and write-ahead logging to maintain atomicity and durability across nodes.

Use Cases and Examples of Strong Consistency

• Financial Services: Banks and other financial institutions require transactions to reflect an accurate and current state across all nodes, especially in systems managing real-time transactions or account balances.
• E-Commerce Systems: Inventory management in e-commerce platforms must guarantee that products being purchased are deducted from inventory in real time and that all viewing consumers see the current state.

Summary Table

Conclusion

While strong consistency ensures reliability and correctness in a distributed system, it must be carefully balanced with system availability and fault tolerance. Given the inherent challenges and the trade-offs dictated by the CAP theorem, designing systems that wisely negotiate these aspects is key to building robust distributed systems. Choices often depend on the specific requirements and characteristics of the application, including the criticality of data accuracy versus the need for high availability.