Explore the fundamentals of eventual consistency in distributed NoSQL databases, including key principles, advantages, trade-offs, and typical scenarios. This quiz is designed to boost your understanding of how distributed systems handle data replication, consistency, and availability across nodes using eventual consistency models.
What does 'eventual consistency' mean in the context of distributed NoSQL databases?
Explanation: Eventual consistency ensures that, over time and with no further changes, all database nodes reflect the same data. This is different from immediate consistency, where updates are synchronized instantly. Saying no data is ever replicated is incorrect, as replication is a core aspect. Data being consistent only during downtime does not describe any real consistency model, and immediate updates across all nodes is a feature of strong consistency, not eventual.
According to the CAP theorem, which two qualities are prioritized when using eventual consistency in distributed systems?
Explanation: Eventual consistency typically means that distributed systems prioritize availability (system responds to requests even if some nodes are down) and partition tolerance (system continues to operate during network splits). Consistency is temporarily sacrificed in this approach. Options mentioning performance, security, speed, or scalability misrepresent the core elements of the CAP theorem.
If a user updates their profile information but another user sees the old data for a few seconds, which consistency model is likely in use?
Explanation: In eventual consistency, updates may take some time to be visible on all nodes, leading to short-lived periods where users see outdated information. Immediate or strict consistency would not allow this perceptible delay. 'No consistency' isn't a valid model—there is always some consistency mechanism in distributed databases.
Which trade-off is most often associated with adopting eventual consistency in a distributed system?
Explanation: Temporary inconsistency is a fundamental trade-off in eventual consistency, as updates may not instantly propagate everywhere. Data loss is not a direct result of this model, and immediate synchronization is something eventual consistency intentionally avoids. Permanent unavailability would be a system failure, not a trade-off.
Which scenario is best suited for eventual consistency in a NoSQL database?
Explanation: Social media feeds can tolerate short inconsistencies, prioritizing speed of access over momentary precision, making them ideal for eventual consistency. Banking, ticketing, and authentication systems require accurate and immediate data, so a stricter consistency model is necessary for those cases.
In an eventually consistent system, when can you be certain that data written to one node is visible to all other nodes?
Explanation: Eventual consistency guarantees all nodes will eventually converge to the same value if no new writes occur, but does not specify a fixed time. Immediate visibility is guaranteed only by strong consistency. System administrator approval and maintenance windows are unrelated to automated consistency processes.
What is a common technique used to resolve conflicts when multiple nodes update the same data in an eventually consistent system?
Explanation: Timestamps are widely used to determine which update should be kept when conflicts arise, ensuring the most recent change is preserved. Deleting data or using backups disregards valuable information, and randomly picking values undermines data integrity.
How does eventual consistency affect write availability during network partitions in a distributed system?
Explanation: Eventual consistency allows systems to accept writes even when some nodes are unreachable, ensuring continued operation. Rejecting writes or only permitting them at scheduled times would reduce availability. Permanent disabling is not a feature of eventual consistency but rather a sign of system failure.
When reading data from an eventually consistent system, what is a possible result for a client right after a write?
Explanation: Due to replication delays, clients may observe outdated information in an eventually consistent system immediately after a write. There is no guarantee that reads always return the latest value. Read operations are certainly allowed, and data does eventually become consistent, not permanently stale.
Which property must a distributed system relax to achieve high availability with eventual consistency?
Explanation: To achieve high availability, distributed systems using eventual consistency relax the requirement for strong (immediate) consistency across nodes. Storage capacity, security, and interface design are unrelated to the consistency-availability balance in system architecture.