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What does strong consistency theoretically ensure after a write operation?
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Strong consistency theoretically ensures that the data written is immediately reflected in all subsequent read operations.
Explain the concept of availability in the context of the CAP theorem.
Availability means the system is always available for reads and writes, even during node failures, although it may return inconsistent data.
What is the significance of partition tolerance in a distributed system?
Partition tolerance ensures the system continues to function even if there is a failure in communication between nodes.
Why is partition tolerance assumed to be necessary for reliable services?
Partition tolerance is necessary because network partitions are expected to happen, and the system must continue to function despite communication issues between nodes.
What is meant by 'eventual consistency'?
Eventual consistency means that all updates to the data will spread to all nodes eventually, allowing them to converge to the same state over time.
Why can't real-world distributed systems achieve perfect strong consistency?
Due to network delays, real-world distributed systems can't achieve perfect strong consistency.
What design decision is typically influenced by the CAP theorem?
The CAP theorem helps making design decisions regarding which two properties (Consistency, Availability, Partition Tolerance) to prioritize in a distributed system.
Give an example of a scenario where availability priority is preferred.
Availability priority is preferred when it's acceptable for data to be slightly out of date, such as accepting orders during network issues and issuing refunds later.
How does availability prioritize successful request completion?
Availability prioritizes successful request completion by ensuring the system can always process reads and writes, even if the data may be inconsistent.
What type of consistency do most distributed databases aim for?
Most distributed databases aim for eventual consistency with minimal lag.
What happens to data state and synchronization during read requests in the context of the CAP theorem?
Read requests do not affect the data state and synchronization directly; they only retrieve the current state of the data.
Give an example of a scenario where consistency priority is crucial.
Consistency priority is crucial for accurate product stock management in e-commerce to avoid selling the same product multiple times.
In the CAP theorem, what does consistency ensure?
Consistency ensures that all users see the same data simultaneously and all read requests return updated data after a write.
Describe the trade-off involved in the CAP theorem.
The CAP theorem states that in a distributed system, only two of the three properties (Consistency, Availability, Partition Tolerance) can be ensured simultaneously.
What does CAP stand for in the context of distributed systems?
Consistency, Availability, Partition Tolerance
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