noc19-cs33 Lec 14 CAP Theorem

Summary

The video explains the CAP theorem, proposed by Eric Brewer and proved by Gilbert and Lynch, which states that in distributed systems, it is impossible to guarantee all three properties: Consistency, Availability, and Partition Tolerance simultaneously. It details each property, emphasizing the importance of Availability in online businesses, where latency can significantly impact revenue. The video further explores how systems like Cassandra prioritize Availability and Partition Tolerance, offering eventual consistency, while traditional RDBMS systems ensure strong consistency and availability. The discussion concludes with an overview of consistency levels in Cassandra, including quorum strategies for maintaining data integrity.

Takeaways

• 😀 Key insights about effective communication techniques.
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• 😀 The role of non-verbal cues in conveying messages.
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• 😀 Techniques for providing constructive feedback.
• 😀 The significance of empathy in building strong relationships.
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• 😀 The value of continuous improvement in communication skills.

Q & A

What is the CAP theorem?

The CAP theorem, proposed by Eric Brewer and later proved by Gilbert and Lynch, states that in a distributed system, you can guarantee at most two out of three guarantees: Consistency, Availability, and Partition Tolerance.

What do the three guarantees of the CAP theorem represent?

The three guarantees are: 1) Consistency - all nodes see the same data at any time; 2) Availability - the system is operational and returns responses quickly; 3) Partition Tolerance - the system continues to operate despite network partitions.

How does latency affect online businesses like Amazon?

In online businesses, even a 500-millisecond latency can lead to significant revenue loss, as it may cause customers to churn. Each additional millisecond can result in millions of dollars in yearly losses.

What is 'Eventual Consistency' as mentioned in the script?

Eventual Consistency is a weak form of consistency where if writes stop, all replicas will eventually converge to the same value over time. It allows for high availability but may return stale data until convergence occurs.

What is the importance of Availability in the CAP theorem?

Availability is critical as it ensures that read and write operations complete reliably and quickly. A system with high availability performs operations continuously and returns results promptly, which is vital for user satisfaction.

What are the implications of network partitions on system performance?

Network partitions can disrupt communication between data centers, affecting data consistency. The CAP theorem implies that during such partitions, systems must choose between maintaining consistency or availability.

What consistency levels does Cassandra support?

Cassandra supports several consistency levels, including: 1) Any - the fastest, allowing any replica to respond; 2) All - the slowest, requiring all replicas to update; 3) One - at least one replica must be updated; 4) Quorum - a majority of replicas must be updated.

How does RDBMS differ from NoSQL systems in terms of consistency?

RDBMS systems typically provide strong consistency through ACID properties, while NoSQL systems like Cassandra offer BASE properties, which focus on availability and eventual consistency instead.

What is the role of quorum in Cassandra's consistency model?

Quorum ensures strong consistency by requiring a majority of replicas to be updated before a read or write operation is considered successful, balancing speed and consistency.

How do different workloads influence the choice of W and R in quorum settings?

The values of W (write replicas) and R (read replicas) are adjusted based on the workload type: for read-heavy workloads, W can be set to the total number of replicas, and R to one, while for write-heavy workloads, W and R are typically set to greater than half the total replicas to ensure consistency.