Data Consistency Models in Sharded Systems Quiz Quiz

Explore key concepts of data consistency models within sharded systems, examining how data remains reliable, synchronized, and accessible across distributed databases. Enhance your understanding of strong, eventual, and other consistency strategies essential for scalable database architectures.

1. Definition of Eventual Consistency

   What is meant by eventual consistency in a sharded database system?

   1. Shards ignore updates from other nodes permanently
   2. All nodes will eventually reflect the latest updates, but not instantly
   3. Updates to all shards are guaranteed visible to all nodes immediately
   4. Data is never synchronized between shards

   Explanation: Eventual consistency means that, given enough time without new updates, all nodes in the system will converge to the same data value. It does not require immediate synchronization as in strong consistency, making it more scalable but with delayed update visibility. The first option describes strong consistency, not eventual consistency. The third and fourth options are incorrect, as data is indeed synchronized eventually and no updates are ignored permanently.

2. Strong Consistency Model

   In the context of sharded systems, which statement best defines strong consistency?

   1. Data is only sometimes replicated between shards
   2. All writes are visible to all clients at the same time
   3. Writes can be lost if a shard goes offline
   4. Clients may receive outdated data after a write

   Explanation: Strong consistency ensures that, once a write is acknowledged, it is immediately visible to all clients, providing predictable read results. The second option is incorrect as strong consistency demands regular and immediate replication. The third is incorrect since outdated (stale) data should not be read. The fourth option describes data loss, which is unrelated to consistency level.

3. Sharding and Data Partitioning

   Why is sharding used in distributed database systems with respect to data consistency?

   1. To split data across multiple nodes to improve scalability while managing consistency
   2. To reduce network security risks by hiding data
   3. To increase query latency for all operations
   4. To guarantee global transaction atomicity automatically

   Explanation: Sharding divides data among multiple nodes, allowing systems to scale efficiently and to apply consistency techniques as needed. The first option is about security, which is not the main goal of sharding. The second option incorrectly suggests sharding harms performance. The fourth option is incorrect, as sharding does not guarantee global atomicity without additional protocols.

4. Read-Your-Own-Writes Consistency

   If a user submits an update and then immediately reads the same data from a sharded system, which consistency model ensures the user sees their change?

   1. Lost update avoidance
   2. Write-skew tolerance
   3. Read-your-own-writes consistency
   4. Stale data permission

   Explanation: Read-your-own-writes consistency ensures that after making a change, the user will immediately see the update in any subsequent reads. The second and third options refer to different consistency anomalies. The fourth is incorrect, as it allows out-of-date information, not guaranteeing recent writes are visible to the writer.

5. Linearizability

   How does linearizability differ from eventual consistency in a sharded system?

   1. Eventual consistency is more secure than linearizability
   2. Both models guarantee no data loss after network partitions
   3. Linearizability ignores failed writes, but eventual consistency does not
   4. Linearizability guarantees immediate consistency after every operation, while eventual consistency may delay updates across shards

   Explanation: Linearizability ensures all operations appear to happen instantly and in order to all nodes, unlike eventual consistency, which allows some delay. The second option mischaracterizes linearizability. The third relates to security, which is not the focus of these models. The fourth is misleading, as neither alone solves data loss from network failures.

6. Session Consistency

   Which scenario best illustrates session consistency in a distributed sharded system?

   1. All users see the same updates at the exact same time
   2. The system forgets all changes after a user logs out
   3. Each user is randomly assigned inconsistent data for every read
   4. A user in a session always sees their own recent changes, even if other users do not

   Explanation: Session consistency ensures that within a single session, users see their writes and updates immediately, though other sessions may have delayed visibility. Option two describes strong consistency. The third option describes inconsistency, and the fourth talks about data loss rather than consistency.

7. Monotonic Read Consistency

   In a distributed sharded system, what does monotonic read consistency provide for users?

   1. Enables simultaneous writes from all users without replication
   2. Allows users to see results from any random shard at any time
   3. Ensures users never see an older version of data after viewing a newer version within their session
   4. Guarantees every read returns the latest value system-wide regardless of session

   Explanation: Monotonic read consistency ensures once a user has seen a particular version of data, they will not observe earlier versions in subsequent reads. Option one describes strong consistency at a global level, which monotonic reads do not guarantee. The third option is incorrect, as randomness defeats consistency. The fourth describes a scenario ignoring consistency concerns.

8. Quorum-Based Consistency

   What is a key feature of quorum-based consistency in sharded systems?

   1. A majority of shard replicas must be reached for read or write operations
   2. All data is written to every shard before being acknowledged
   3. Reads are always from a random single replica
   4. Clients receive updates only if every node is online

   Explanation: Quorum-based consistency requires that a majority (a quorum) of replicas acknowledge reads or writes before confirming an operation, improving fault tolerance and coordination. The first option is too strict and not scalable. The third option is incorrect as systems can make progress with some nodes offline. The fourth option would not ensure consistency.

9. Causal Consistency

   Which statement best describes causal consistency in a sharded database system?

   1. Operations that are causally related are seen by all nodes in the same order
   2. All nodes ignore the order of operations completely
   3. Reads return the most recent write made anywhere
   4. Writes are committed only if no network partition exists

   Explanation: Causal consistency means operations that have a cause-effect relationship are applied in the same order for all nodes to maintain logical correctness. The second option is inaccurate as order does matter. The third describes network availability, not consistency level. The fourth describes strong consistency, not causal.

10. Implication of Weak Consistency

    Which is a potential consequence of weak consistency models in sharded systems used for online shopping carts?

    1. A user's cart may briefly show incorrect or out-of-date items after an update
    2. Every cart update is available instantly to all users across shards
    3. The system will never lose any updates under any circumstances
    4. Weak consistency always guarantees transactions are isolated

    Explanation: Weak consistency can lead to temporary discrepancies, such as shopping carts showing outdated items due to replication delays. The second option is a property of strong consistency. The third is not true since weak consistency does not protect against all data loss. The fourth is incorrect because isolation is not a guaranteed property of weak consistency.