Questions and Answers
What is the primary focus of data-centric consistency models?
Which of the following consistency types places the highest emphasis on global consistency?
Which type of consistency allows for faster response times but may introduce inconsistencies?
What is a key trade-off when using data-centric consistency models?
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Which of the following is NOT a type of data-centric consistency?
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Study Notes
Data-Centric Consistency Models
- Aim to maintain data integrity in distributed systems.
- Focus on how updates are synchronized across nodes for a consistent client view.
Types of Data-Centric Consistency
- Strict Consistency: Ensures all reads reflect the latest write; no delay in visibility.
- Sequential Consistency: Operations appear to occur in some sequential order, respecting real-time order of operations.
- Causal Consistency: Operations that are causally related are seen by clients in the same order, while independent operations may be seen in different orders.
- FIFO (First In, First Out) Consistency: Guarantees that operations from a single client are seen by all clients in the order they were issued.
Client-Centric Models
- Focus on the perspectives of individual clients, providing quicker response times.
- Allow for possible temporary inconsistencies between clients.
- Data-Centric models promote stronger global consistency, which may impact performance negatively.
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Description
Test your knowledge on data-centric consistency models in distributed systems. This quiz covers types of consistency like strict, sequential, causal, and FIFO, analyzing their guarantees and trade-offs. Ideal for anyone studying data integrity and synchronization methods.
