This article explores strong consistency in distributed databases, defining what it means for systems to guarantee that all readers see the most recent write. It highlights the significant challenges and inherent costs of maintaining strong consistency across multiple machines, data centers, and continents, primarily due to the coordination required by physics. The piece sets the stage for understanding the mechanisms and trade-offs involved in building strongly consistent distributed systems.
Read original on ByteByteGoStrong consistency is a fundamental property in distributed systems, particularly databases, ensuring that once a write operation is complete, all subsequent read operations will return the updated value, regardless of which replica they query. This guarantee prevents scenarios where different users might observe conflicting states of the system, which is critical for applications like financial transactions where data integrity is paramount.
Achieving strong consistency across geographically distributed servers is inherently complex and expensive. The primary challenge stems from the need for coordinated agreement among all (or a quorum of) replicas before a write can be considered 'committed.' This coordination introduces latency and depends on the speed of light, making it a problem governed by physics. The CAP theorem further illustrates this trade-off, where a distributed system can only guarantee two out of three properties: Consistency, Availability, and Partition Tolerance. Strongly consistent systems prioritize Consistency and Partition Tolerance, often at the cost of Availability during network partitions.
Strong vs. Eventual Consistency
While strong consistency guarantees immediate visibility of writes, eventual consistency allows for temporary data discrepancies, where replicas will eventually converge to the same state without strict guarantees on the timing. The choice between these models depends heavily on the application's requirements for data integrity and tolerance for stale reads.
Implementing strong consistency typically involves distributed consensus algorithms like Paxos or Raft. These algorithms ensure that all participating nodes agree on a single outcome, even in the presence of failures. However, these mechanisms involve multiple rounds of communication between nodes, leading to increased latency, network overhead, and potential performance bottlenecks. The 'cost' of strong consistency therefore includes not only system complexity but also reduced write throughput and higher latency compared to eventually consistent models.