Explore the foundational concepts of consistency models in real-time applications, focusing on how to select the most suitable model for various scenarios. Gain insights into strong and eventual consistency, trade-offs, and practical considerations for real-time system design.
Which consistency model guarantees that after a write operation completes, all subsequent reads will return the updated value, such as in online banking transactions?
Explanation: Strong consistency ensures that all clients see the most recent write immediately after it’s completed, which is essential for applications like online banking. Eventual consistency only guarantees that updates will be visible eventually, possibly allowing stale reads. Causal consistency enforces order for related operations but does not guarantee immediate visibility. Session consistency provides guarantees within a user's session, but not across all clients, making it less suitable for critical updates.
For which application is eventual consistency often acceptable, such as updating the number of 'likes' on a social media post?
Explanation: Eventual consistency is typically suitable for distributed social media feeds where temporary inconsistencies are tolerable and user experience is not severely affected. Stock market executions and critical hospital records require much stricter consistency. Real-time multiplayer positions need immediate synchronization for fairness and playability, making eventual consistency unsuitable there.
What is a common trade-off when choosing eventual consistency over strong consistency in a distributed real-time system?
Explanation: Choosing eventual consistency generally improves availability because nodes can operate even when not immediately synchronized. Lower availability and increased latency are more common with stricter models like strong consistency, while higher costs are not directly tied to eventual versus strong consistency in this context.
In which type of consistency model will a client always see their own updates immediately in a session, as seen when a user edits their profile and refreshes the page?
Explanation: Read-your-writes consistency ensures that a user will always see their most recent updates, making it ideal for scenarios like profile editing. Monotonic read consistency prevents going backward in time but doesn't assure you see your writes instantly. Strong consistency applies system-wide, not just per-session. Write-follow-reads consistency is not a standard model and is included as a distractor.
Which consistency model ensures that causally related updates are seen in proper order, as in a comment appearing after a post is displayed?
Explanation: Causal consistency guarantees the order of related operations, such as a comment following a post, which is required for a logical view of actions. Eventual consistency does not ensure order for related events. Strong consistency is stricter, ensuring latest data always, but is more than what is needed. Potential consistency is not a standard term and serves as a distractor.
Which consistency model typically results in lower latency for global data propagation in real-time collaborative applications, like document editing?
Explanation: Eventual consistency allows updates to propagate without waiting for immediate global synchronization, significantly reducing latency. Strict consistency and linearizable (likely intended typo for 'linearizable') consistency require all nodes to agree before updates are visible, increasing latency. 'Powerful consistency' is not a recognized model.
What is a typical disadvantage of using strong consistency in a large-scale, real-time chat application?
Explanation: Strong consistency introduces higher latency because all nodes must agree on updates before making them visible, slowing down message delivery. Message loss is not a direct consequence of consistency choice. Weaker data integrity is incorrect as strong consistency provides high integrity. Security is unrelated to data consistency models.
Which consistency model should be prioritized for an online store’s shopping cart to ensure real-time item availability and prevent double booking?
Explanation: Strong consistency is crucial in preventing overselling by ensuring all clients see updates immediately. Eventual consistency could allow several users to add the same item before updates propagate. Causal and session consistencies do not offer the system-wide guarantees required to prevent double booking in high-demand situations.
Which consistency model might occasionally expose users to slightly outdated information, but is acceptable if the system is eventually correct, as in some friend suggestion algorithms?
Explanation: Eventual consistency permits users to see outdated data temporarily but assures all updates become visible in time, which is acceptable for low-stakes features like friend suggestions. Immediate and absolute consistency are either not standards or are distractors promising stricter guarantees. Protective consistency is not a recognized model.
When network partitions may occur, which consistency model allows continued operation at the cost of temporary inconsistencies, suitable for location tracking in mobile apps?
Explanation: Eventual consistency allows systems to remain available during partitions, syncing updates once connectivity is restored, which is ideal for mobile apps with intermittent networks. Exclusive, rigid, and centralized consistency are not common terms in standard consistency modeling and do not offer benefits for partition tolerance.