Data Consistency Models in Distributed Systems Questions and Answers for Viva

Answer-1: It defines the rules and guarantees about the visibility and ordering of updates in a distributed system.

Answer-2: To ensure that all nodes see the same data or understand the state of data coherently despite replication and distribution.

Answer-3: Strong consistency ensures that any read operation returns the most recent write value globally.

Answer-4: Eventual consistency guarantees that if no new updates are made, all replicas will eventually converge to the same value.

Answer-5: Causal consistency ensures that related operations are seen by all nodes in the same order, preserving cause-effect relationships.

Answer-6: Sequential consistency guarantees that operations from all nodes appear in some sequential order consistent with the order seen by each node.

Answer-7: Weak consistency offers minimal guarantees about the visibility of updates; reads may return stale or inconsistent data.

Answer-8: Linearizability is a stronger guarantee that requires real-time ordering of operations, whereas sequential consistency does not require real-time constraints.

Answer-9: It ensures that once a process reads a value, any subsequent reads will never return older values.

Answer-10: Monotonic write consistency guarantees writes from a single process are seen in the order they were issued.

Answer-11: It ensures a process always sees its own previous writes in subsequent reads.

Answer-12: Trade-offs include performance, availability, latency, and complexity of implementation.

Answer-13: Eventual consistency is widely used to maximize availability and partition tolerance.

Answer-14: CAP theorem states a distributed system can only provide two of three guarantees: consistency, availability, and partition tolerance.

Answer-15: Google Spanner provides strong consistency with global linearizability using synchronized clocks.

Answer-16: Quorum mechanisms require a majority of nodes to agree on reads/writes to enforce consistency.

Answer-17: Strict consistency is an ideal model that guarantees instantaneous propagation of writes, which is practically impossible; strong consistency relaxes some timing assumptions.

Answer-18: It allows updates to proceed independently and reconciles conflicts once partitions heal.

Answer-19: An unexpected or incorrect behavior caused by weak consistency, such as stale reads or lost updates.

Answer-20: Vector clocks track the causal relationship between events to enforce ordering.

Answer-21: Often weak or eventual consistency to optimize performance.

Answer-22: Higher consistency often reduces availability, especially during network partitions.

Answer-23: BASE stands for Basically Available, Soft state, Eventual consistency?a model used in NoSQL systems.

Answer-24: It guarantees that after a write completes, any subsequent read will return the updated value.

Answer-25: A read operation that returns outdated data that does not reflect the most recent write.

Answer-26: Consensus protocols (e.g., Paxos, Raft) help enforce strong consistency by ensuring agreement on data state.

Answer-27: Strong consistency to avoid anomalies in transactions.

Answer-28: By conflict detection and resolution strategies, often with eventual consistency.

Answer-29: Consistency refers to data correctness across nodes; durability ensures data persists despite failures.

Answer-30: Higher latency can delay updates propagation, making strong consistency more costly.

Answer-31: Read and write quorum sizes determine the overlap ensuring the latest write is always read.

Answer-32: The time interval during which replicas may be inconsistent before converging.

Answer-33: CRDTs enable eventual consistency by automatically resolving conflicts without coordination.

Answer-34: They provide configurable consistency levels including eventual and strong consistency.

Answer-35: Timestamps help order events and resolve conflicts.

Answer-36: A model where a client sees a consistent view of data during a session but not necessarily across all clients.

Answer-37: It ensures users see their latest changes immediately, preventing confusion.

Answer-38: Transactional consistency uses ACID properties; eventual consistency relaxes these for scalability.

Answer-39: In case of network partitions, systems may sacrifice consistency to maintain availability.

Answer-40: Based on application requirements for latency, availability, data correctness, and failure tolerance.

Answer-41: A read that returns the latest write result as if all operations occurred instantaneously.

Answer-42: MVCC allows multiple versions of data, enabling snapshot isolation and some relaxed consistency.

Answer-43: Network delays, clock synchronization, and performance impact.

Answer-44: An order that respects the cause-effect relationship of operations.

Answer-45: Through techniques like fault injection, consistency checkers, and monitoring anomalies.

Answer-46: Data may be temporarily inconsistent but converges eventually.

Answer-47: A consistency anomaly where concurrent transactions cause inconsistent data states.

Answer-48: By choosing appropriate consistency models and replication strategies.

Answer-49: It provides consistent reads within a transaction, preventing some concurrency anomalies.

Answer-50: Hybrid consistency models, stronger guarantees in geo-distributed systems, and automated conflict resolution.